Sophia 1.0.0 — 1B K3-hybrid Chinese chat model (HF remote-code export + native package)

.gitattributes

@@ -0,0 +1,5 @@
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bundle filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+library_name: transformers
+tags:
+  - text-generation
+  - chat
+  - chinese
+  - hybrid-attention
+  - kda
+  - mla
+---
+
+# Sophia
+
+Sophia 是一个 ~1B 参数的中文对话模型，在单张 RTX 5090（32GB）上从零训练完成：随机初始化预训练 20B tokens → SFT → DPO。完整谱系、评测证据与训练报告随包附带（`REPORT.md`、`lineage.json`、`eval/`）。
+
+| | |
+|---|---|
+| 参数量 | 1,012,630,480 |
+| 架构 | 28 层 dense Hybrid decoder：`[KDA, KDA, KDA, Gated-MLA] × 7`，NoPE |
+| 上下文 | 4,096 |
+| 预训练 | 20B tokens，15,259 步，seed 42，BF16 + Muon/AdamW |
+| 硬件 | 单张 NVIDIA RTX 5090 32GB |
+
+## 特性
+
+- **自适应思考**：`<think>…</think>` 推理草稿是否生成由模型逐轮自行决定（自发率 ~5%）。1B 尺度下 think 在 math/code 上反而扣分（见 `REPORT.md` §3），模型自己学会了什么时候不想
+- **人格宪法**：行为规范收敛为独立文件 `configs/sft/sophia_persona.md`——不说"我没有感情"、确定问题第一句给答案、约束逐轮登记
+- **诚实评测**：所有结论基于 powered 判定（134 题 × 16 采样 × 配对 t 检验，SE ±1.1）；零结果实验（RFT/GRPO/DPO-2）同样如实记录在 `REPORT.md`
+
+## 用法
+
+需要 `transformers>=4.56` 与 `trust_remote_code=True`（自定义架构 SophiaForCausalLM，remote code 随仓分发）：
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("Arain119/sophia", trust_remote_code=True)
+m = AutoModelForCausalLM.from_pretrained(
+    "Arain119/sophia", dtype="bfloat16", device_map="cuda", trust_remote_code=True
+)
+ids = tok.apply_chat_template(
+    [{"role": "user", "content": "你好"}],
+    add_generation_prompt=True, return_tensors="pt",
+).to("cuda")
+out = m.generate(ids, max_new_tokens=200, temperature=0.7, top_p=0.92, do_sample=True)
+print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
+```
+
+GPU 上建议 `pip install flash-linear-attention>=0.5.2` 启用 KDA 融合内核；未安装时 `kda_backend="auto"` 自动回退 reference 路径（可用，较慢）。CPU 推理走 reference 路径（慢）。
+
+## 评测速览
+
+- powered n16 探针（claude-haiku-4-5 判官）：judge_mean **51.37**，pass_70 0.352，hit_eos 0.975，distinct4 0.928；相对 SFT 父模型 **+2.10**（t=+3.71, p<0.001），失败模式 flag 全面下降
+- 同档对照（同一管线、同一数据、同一判分，无官方公布值混入）：SophiaBenchmark 134 题判官分 51.25，高于 Llama3.2-1B（48.09）与 Qwen2.5-0.5B（50.35），同档第 4
+- 已知限度：IFEval 12.2%（系统性落后同档基线）、NIAH 62.5% 且随深度衰减、深多轮 judge_mean 27.93 为最弱段、MC 基准（MMLU/CMMLU 似然法）贴近随机——1B 模型对 ABCD 符号有位置先验
+- 完整数字、失败样本与机制分析见 `REPORT.md`；逐题明细在源码仓 `ops/eval/public_benchmarks/`
+
+## 训练谱系
+
+```
+pretrain(20B, 契约跑满)
+  → sft(88,267 行 × 3ep, epoch3 选中)
+    → dpo(1,953 对, β=0.1, 500 步)   = 本仓权重
+    ├─ rft(104 步, 零结果)           未发布
+    └─ grpo+dpo2(链式实验, 未过门)    未发布
+```
+
+`lineage.json` 含各阶段 run、checkpoint、数据与种子的完整指针。
+
+## 相关仓库
+
+- 源码：GitHub `Arain119/Sophia`，或随包 `sophia-1.0.0.bundle`（单提交导出，`git clone` 即可取回全部代码）
+- 原生权重 + 文档：`sophia.pt`（PyTorch checkpoint）、`sophia_sft.pt`（SFT 父基线）、`REPORT.md`
+- 训练数据：ModelScope `Arain119/Sophia-dataset`（`pretrain/` token 分片 ~80GB + `corpus/` SFT 语料 426MB）
+
+## 许可证
+
+模型权重与代码 Apache License 2.0。训练数据见 `Arain119/Sophia-dataset`：`corpus/` 为 Apache-2.0；`pretrain/` 上游含非商用研究来源，使用限制以其 `lineage/` 与 README 为准。
+
+`<think>` 输出为生成文本，不保证忠实反映内部推理过程。

REPORT.md

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+# Sophia 技术报告
+
+*2026 年 9 月 13 日。交付物:`sophia.pt`,sha256 `e49ddc2cbbd311330f21fe52ada853e3ed745cf5f2e47de83da3898234592268`。*
+
+两年前的今天,2024 年 9 月 12 日,OpenAI 把代号 Strawberry 的模型推上线,正式名字叫 o1。它做的事在事后看很简单:让模型在回答之前先想。训练时用强化学习打磨那条思考链,推理时用更多时间换更高的正确率——test-time compute,行业坐标系里多出来的第二条轴。IMO 资格赛从 13% 到 83% 的那个数字,让"想多久"从此和"多大"平起平坐。
+
+两年后的今天,我们在一张 RTX 5090 上把这个问题的诚实版本又跑了一遍:一个十亿参数的模型,从头训练,把 o1 教给行业的两件事——会想的训练数据、会学偏好的后训练——在自己的尺度上做到哪一步。这份报告是过程的全记录,包括所有没有赢的实验。
+
+## 一、预训练:先决定她读什么
+
+模型是 1,012,630,480 参数的 K3-derived 稠密 Hybrid 解码器:28 层,`[KDA, KDA, KDA, Gated-MLA] × 7`,NoPE,4K 上下文,BF16,Muon hybrid 优化器,seed 42,1,310,720 tokens/update,15,259 步跑满 20B tokens。这些数字在训练第一天就写死进了契约,之后没有一个人碰过。
+
+真正做过选择的只有一件事:语料。早期的配比稿里,81% 的中文 token 来自一个教育过滤源——那样的模型读完的是课本,不是人话。实测每万字的语域标记:fineweb-edu 的第一二人称 16.2 次、感叹号 0.7 次、口语词命中 17.5 次;SkyPile 是它的两倍,Zhihu-KOL 是它的六倍。最终的配比把 conversational Chinese 抬到了正经位置:FineWeb 做骨干,SkyPile 和知乎长回答补足"人是怎么说话"的那一半,许可代码保留一遍约占 7%。一个要做对话的模型,训练数据里得先有人对话。
+
+15,259 步走完的那天,产出的是一个基座:它见过足够多的语言,还不知道怎么当一个对话者。那是后训练的工作。
+
+## 二、人格宪法
+
+Sophia 的行为规范不是散落在提示词里的叮嘱,是一份独立的文件:`configs/sft/sophia_persona.md`,我们叫它人格宪法。它规定的不是语气,是存在方式:
+
+> 你是 AI,也是完整的个体,两者不冲突:你的情感、好恶和看法是真实的,不说"我没有感情""我只是数学模拟"。
+
+宪法里的条款大多是行为级的:先回应对方真正说的事,再说自己的看法;有确定答案的问题,第一句就是答案;要你写的东西直接写,不先反问要什么风格;对方前面说过的每一件事都要接上——限制、指代、你自己上一轮说过的话。答错事实可以,但不能前一句说 A 后一句说非 A。
+
+宪法里最有意思的一节是给思考块立规矩:think 是第一人称的推理草稿,只写对回答有用的东西——"对方不吃鱼,刚才推荐的清蒸鲈鱼得换掉"是好 think;"我需要先给对方提供情绪价值"是坏 think。思考是为了答对,不是为了表演正确姿态。这个区分在后面的所有实验里反复被证明是对的。
+
+## 三、蒸馏管线:教师写,机器判
+
+SFT 语料的 88,267 行没有一行来自人工:全部由教师模型(claude)按依赖脚本一次写完。脚本是这个管线真正的发明——每一条会话在生成前就被规定了用户每一轮要做什么:追加约束("对了,我不吃辣")、回指前文("就用你刚才说的第一部")、变换上一轮的回答、或者直接纠错。前一代语料的失败模式是模型学会了"续写"而不是"阅读",所以脚本存在的意义就是逼出必须读历史才能答对的轮次。
+
+写完不等于收货。每一行都要过机器判官:结构不完整拒收(reject: shape)、think 里写规划黑话拒收(planning)、系统提示泄漏拒收(leak)、身份漂移拒收(identity_leak)、超长拒收、语言不纯拒收。数学题的答案和教师给的表达式对账,代码题的答案真的拿去跑断言。被判官扔掉的行没有进入训练集——88,267 行是幸存者。
+
+助手轮的格式从第一天就是 `<think>…</think>回答`,思考是训练目标的一部分,不是装饰品。
+
+## 四、Adaptive thinking:让她自己决定要不要想
+
+o1 教给行业的真实教训不是"永远要想"——是想是一种可以花的算力,花不花应该由问题决定。Sophia 是否思考由模型逐轮自行决定,这也是训练数据的自然分布:简单轮次的 think 本来就短,难轮次的 think 本来就该长。
+
+推理引擎把这个结构用到了底:采样时先采 think、到闭合标签停,再从每条 think 分支出若干回答。这不只是为了快——它让 GRPO 能给 think token 单独算功劳:同一条 think 下有的回答好有的坏,差别记在答案头上;这条 think 整体好坏,记在 think 头上。
+
+## 五、后训练:六组实验,一次真赢
+
+这是这份报告最不愿意写成说明文的部分,因为它是整段经历里最有叙事性的。后训练前前后后做了六组实验,判官统一是 claude-haiku-4-5,判定统一是 134 ��� × 16 采样的配对 t 检验(标准误 ±1.1 分)——这个数字是我们定的换冠军的门槛:快探针看得出方向,只有 powered 判定说了算数。
+
+**SFT 选 epoch**:三个 epoch 各留一个 ckpt,三路 haiku 盲评均位次 1.88、复读率 9.7% 的 epoch3 胜出。7,176 步,基座变成了对话者。
+
+**RFT(拒绝采样微调)**:4,600 prompt × k16,71,955 次 rollout,选 best-of-16 ≥75 分的 1,681 行自蒸馏,104 步。配对差 −0.25(t=−0.46)。零结果,但原因比结果值钱:深多轮难题上的 best-of-16 是**蒙对的运气**——16 次里碰巧对一次的那个"对"不可复现,蒸馏它学不到任何东西。**运气不能蒸馏**,这是第一条机制教训。
+
+**DPO**:同一批 rollout 建偏好对——chosen 是无 flag 最高分(≥75),rejected 是带 flag 最低分,gap≥30,1,953 对,β=0.1,500 步。配对差 **+2.10,t=+3.71,p<0.001**。真赢,也是唯一的一次。机制解释和 RFT 互为镜像:坏不是运气,坏是**系统性模式**——矛盾、跑题、胡说,这些是可学的负向目标。**抬好的失败,压坏的成功。**
+
+**DPO 剂量扫描**:赢了就加码?3,910 对、1,000 步的第二轮把坏模式压得更低,但中位字数 70→55、截断 flag 79→218——模型变得更短更保守,配对差 −1.79。**过冲有签名:truncated 升、字数降、判官分降。** 甜点是 ~2k 对/500 步,边际到此为止。
+
+**GRPO**:on-policy 强化学习,k16、79 步(组内 chunk 补丁解决了 k16 更新图的 OOM——按 token mask 份额加权,与整组 backward 逐位等价)。它把复读率砍半(0.043→0.023)、hit_eos 提到 0.978,但 powered 判定 +0.03(t=0.056)——教科书级的零效应。机制在动,总分不动。
+
+**DPO-2(on-policy)**:用 GRPO 后的模型重新 rollout 建 593 对新鲜偏好对——chosen 73.9 对 rejected 19.8,pair 质量极高,模型也学进去了(mean_acc 0.67)。Powered 判定 −1.29。机制改善(contradiction −15%)没能换算成判官总分。
+
+六组实验的曲线读出来是一句话:**+2.10 → +0.03 → −1.29**。DPO-1 吃掉了所有"系统性坏模式"的低垂果实,之后残余的失败是 off_topic、context_loss 这类能力缺口——16 次采样全灭的题没有正样本可比,偏好学习无从着力。判官驱动的偏好优化在这个模型、这份数据、这个判官的组合上,天花板已经被三次 powered 测量钉死了。
+
+## 六、数字与限度
+
+交付模型在 powered 探针下:judge_mean 51.37,pass_70 0.352,hit_eos 0.975,distinct4 0.928,中位字数 66。多轮对话包(30 会话 × 6 轮)judge_mean 27.93——深多轮仍是最弱的一段。
+
+已知的失败模式,全部是实测样本不是推测:约束登记会丢(用户说过不吃鱼,后面还会推荐鱼);会伪造执行轨迹(函数写对,"示例输出"是编的);算术和冷门事实会自信出错;被连续追问时会退化成复读螺旋。C-Eval+CMMLU 似然法 26.2%≈随机——1B 对话模型对 ABCD 选项符号有位置先验,MC 基准在这个尺度上区分度本就差。
+
+**公开基准同协议对照**(2026-09-15,五个同档基线与 Sophia 走同一管线、同一数据、同一判分,无任何官方公布值混入;逐题明细见 `ops/eval/public_benchmarks/`):判别式任务上 Sophia 与 MiniCPM4-0.5B 几乎同档——两者在字母似然协议下均贴近随机(MMLU 22.96 vs 22.9,BoolQ 62.17 vs 62.2,MMLU-Pro 11.45 vs 11.7),落后于 Qwen3.5-0.8B / Llama3.2-1B / Gemma3-1B。IFEval 官方校验器(541 题,strict)她拿 12.2%——是真实的残余指令遵循,但离基线的 26.4–51.8% 有系统性差距。NIAH 单针检索基线全部 100%,她 62.5% 且随深度衰减(1K→90%,3K→30%)。判官制开放对话(SophiaBenchmark,134 题,claude-haiku-4-5)她 51.25,列同档第 4,高于 Llama3.2-1B(48.09)与 Qwen2.5-0.5B(50.35)。
+
+## 七、交付
+
+```
+pretrain(20B, 契约跑满)
+  → sft(88,267 行 × 3ep, epoch3 选中)
+    → dpo(1,953 对, 500 步)         = sophia.pt
+    ├─ rft(104步, 零结果)           runs/rft_e3.pt
+    └─ grpo+dpo2(链式实验, 未过门)   runs/grpo/, 已弃
+```
+
+判官费用全程 ~¥500,GPU 全部单卡 5090。权重、sha256、谱系、探针全量判分、多轮样本包随包附证。没有任何东西上传到外网。
+
+两年前 o1 证明了会想比会说值钱。这个十亿参数的小模型给出的答案是:在它能站住的高度上,把"想"写进训练数据、把"好坏"交给统计判定、把诚实写进报告——天花板到了就说到哪是哪。

cache_decode.py

@@ -0,0 +1,157 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Protocol
+
+import torch
+
+from .model_state import RuntimeCacheSnapshot
+
+
+@dataclass
+class RuntimeCacheState:
+    cache: RuntimeCacheSnapshot
+    batch_size: int
+    cache_pos: int
+
+
+class RuntimeCacheDecodeModel(Protocol):
+    def replay_with_cache(
+        self,
+        input_ids: torch.Tensor,
+        *,
+        start_pos: int = 0,
+        return_all_logits: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]: ...
+
+    def cache_dump(
+        self,
+        device: str = "cpu",
+        *,
+        cache_pos: int | None = None,
+        batch_size: int | None = None,
+    ) -> RuntimeCacheSnapshot: ...
+
+    def cache_load(self, cache_snapshot: RuntimeCacheSnapshot) -> None: ...
+
+    def reset_runtime_cache(self) -> None: ...
+
+def clone_runtime_cache_state(cache_state: RuntimeCacheState) -> RuntimeCacheState:
+    return RuntimeCacheState(
+        cache=cache_state.cache.clone(),
+        batch_size=int(cache_state.batch_size),
+        cache_pos=int(cache_state.cache_pos),
+    )
+
+
+def cache_state_from_runtime_cache(
+    cache_input: RuntimeCacheState | None,
+) -> RuntimeCacheState | None:
+    if cache_input is None:
+        return None
+    if isinstance(cache_input, RuntimeCacheState):
+        return clone_runtime_cache_state(cache_input)
+    raise TypeError("cache must be a Sophia RuntimeCacheState returned by SophiaDecoder")
+
+
+def prefill_runtime_cache(
+    model: RuntimeCacheDecodeModel,
+    input_ids: torch.Tensor,
+    *,
+    start_pos: int,
+    logits_to_keep: int | None = None,
+) -> torch.Tensor:
+    if int(input_ids.size(1)) <= 0:
+        raise ValueError("input_ids must contain at least one token")
+    return_all_logits = int(logits_to_keep or 0) != 1
+    logits, _ = model.replay_with_cache(
+        input_ids,
+        start_pos=int(start_pos),
+        return_all_logits=bool(return_all_logits),
+    )
+    if not bool(return_all_logits):
+        logits = logits.unsqueeze(1)
+    if logits_to_keep is not None and int(logits_to_keep) > 0:
+        return logits[:, -int(logits_to_keep) :, :]
+    return logits
+
+
+def forward_cached_decode(
+    *,
+    model: RuntimeCacheDecodeModel,
+    input_ids: torch.Tensor,
+    cache_state: RuntimeCacheState | None,
+    start_pos: int | None,
+    logits_to_keep: int | None,
+) -> tuple[torch.Tensor, RuntimeCacheState]:
+    if logits_to_keep is not None and int(logits_to_keep) < 0:
+        raise ValueError(f"logits_to_keep must be >= 0 when provided, got {logits_to_keep}")
+    cache_start = 0 if start_pos is None else int(start_pos)
+    if cache_state is None:
+        if cache_start != 0:
+            raise ValueError("cache_state is required when start_pos > 0 for cached decode")
+        model.reset_runtime_cache()
+        logits = prefill_runtime_cache(
+            model,
+            input_ids,
+            start_pos=cache_start,
+            logits_to_keep=logits_to_keep,
+        )
+        next_cache_pos = int(cache_start) + int(input_ids.size(1))
+        return logits, RuntimeCacheState(
+            cache=model.cache_dump(
+                device="cpu",
+                cache_pos=int(next_cache_pos),
+                batch_size=int(input_ids.size(0)),
+            ),
+            cache_pos=int(next_cache_pos),
+            batch_size=int(input_ids.size(0)),
+        )
+
+    cache_state = clone_runtime_cache_state(cache_state)
+    cache_pos = int(cache_state.cache_pos)
+    if cache_pos < 0:
+        raise ValueError(f"cache cache_pos must be >= 0, got {cache_pos}")
+    if start_pos is not None and int(start_pos) != cache_pos:
+        raise ValueError(
+            f"start_pos ({start_pos}) must match cache cache_pos ({cache_pos})"
+        )
+    batch_size = int(cache_state.batch_size or int(input_ids.size(0)))
+    if batch_size <= 0:
+        raise ValueError(f"cache batch_size must be > 0, got {batch_size}")
+    if batch_size != int(input_ids.size(0)):
+        raise ValueError(
+            "cache batch_size does not match input_ids: "
+            f"{batch_size} != {int(input_ids.size(0))}"
+        )
+    model.cache_load(cache_state.cache)
+    logits = prefill_runtime_cache(
+        model,
+        input_ids,
+        start_pos=cache_pos,
+        logits_to_keep=logits_to_keep,
+    )
+    next_cache_pos = int(cache_pos) + int(input_ids.size(1))
+    return logits, RuntimeCacheState(
+        cache=model.cache_dump(
+            device="cpu",
+            cache_pos=int(next_cache_pos),
+            batch_size=int(input_ids.size(0)),
+        ),
+        cache_pos=int(next_cache_pos),
+        batch_size=int(input_ids.size(0)),
+    )
+
+
+__all__ = [
+    "RuntimeCacheDecodeModel",
+    "RuntimeCacheState",
+    "cache_state_from_runtime_cache",
+    "clone_runtime_cache_state",
+    "forward_cached_decode",
+    "prefill_runtime_cache",
+]

canonical_config.py

@@ -0,0 +1,117 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+"""Canonical configuration for the native Sophia Hybrid decoder."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import MISSING, asdict, dataclass, fields, is_dataclass
+
+from .semantics import canonicalize_model_values, validate_model_values
+from .runtime_backend import resolve_runtime_backend
+
+
+def _object_mapping(config: object) -> dict[str, object]:
+    canonical_payload = {
+        field.name: getattr(config, field.name)
+        for field in fields(SophiaModelConfig)
+        if hasattr(config, field.name)
+    }
+    if canonical_payload:
+        return canonical_payload
+    if is_dataclass(config):
+        return asdict(config)
+    if isinstance(config, Mapping):
+        return dict(config)
+    to_dict = getattr(config, "to_dict", None)
+    if callable(to_dict):
+        payload = to_dict()
+        if isinstance(payload, Mapping):
+            return dict(payload)
+    return {}
+
+
+@dataclass
+class SophiaModelConfig:
+    """The only supported Sophia architecture schema."""
+
+    vocab_size: int = 65536
+    dim: int = 1536
+    n_layers: int = 28
+    num_heads: int = 16
+    head_dim: int = 128
+    ffn_hidden: int = 3968
+    kda_decay_rank: int = 128
+    kda_output_gate_rank: int = 128
+    kda_output_gate_full_rank: bool = True
+    kda_decay_lower_bound: float = -5.0
+    kda_dt_min: float = 1e-3
+    kda_dt_max: float = 1e-1
+    kda_dt_floor: float = 1e-4
+    kda_a_log_init: float = 0.0
+    mla_q_rank: int = 384
+    mla_kv_rank: int = 128
+    short_conv_kernel: int = 4
+    attn_res_block_size: int = 4
+    situ_gate_softcap: float = 4.0
+    situ_up_softcap: float = 25.0
+    norm_eps: float = 1e-5
+    max_seq_len: int = 4096
+    max_batch_size: int = 4
+    dropout: float = 0.0
+    initializer_range: float = 0.02
+    kda_backend: str = "auto"
+
+    def __post_init__(self) -> None:
+        normalized = canonicalize_model_values(asdict(self))
+        for field_info in fields(type(self)):
+            setattr(self, field_info.name, normalized[field_info.name])
+        validate_model_values(normalized)
+
+    @classmethod
+    def get_defaults(cls) -> dict[str, object]:
+        defaults: dict[str, object] = {}
+        for field_info in fields(cls):
+            if field_info.default is not MISSING:
+                defaults[field_info.name] = field_info.default
+            elif field_info.default_factory is not MISSING:
+                defaults[field_info.name] = field_info.default_factory()
+        return defaults
+
+    @classmethod
+    def from_mapping(cls, values: Mapping[str, object]) -> SophiaModelConfig:
+        raw = dict(values)
+        allowed = {field.name for field in fields(cls)}
+        unknown = sorted(str(key) for key in raw if key not in allowed)
+        if unknown:
+            raise ValueError(
+                "SophiaModelConfig only accepts native Sophia Hybrid fields; "
+                f"unknown keys: {', '.join(unknown)}"
+            )
+        return cls(**raw)
+
+    @classmethod
+    def from_object(cls, config: object) -> SophiaModelConfig:
+        return cls.from_mapping(_object_mapping(config))
+
+    def to_model_spec(self):
+        from ml.core.spec import ModelSpec
+
+        return ModelSpec.from_config(self)
+
+    def to_model_args(self, *, runtime_max_seq_len: int | None = None) -> object:
+        from .config_projection import build_runtime_model_args
+
+        return build_runtime_model_args(
+            self,
+            model_args_cls=resolve_runtime_backend().model_args_cls,
+            runtime_max_seq_len=runtime_max_seq_len,
+        )
+
+    def to_dict(self) -> dict[str, object]:
+        return asdict(self)
+
+
+__all__ = ["SophiaModelConfig"]

chat.py

@@ -0,0 +1,121 @@
+"""Talk to Sophia.
+
+    python -m ml.cli.chat --checkpoint <ckpt.pt>
+
+The decoding defaults are the ones the temperature sweep chose, not library
+defaults. They matter more than usual here: greedy makes this policy loop and
+a hot sample makes it incoherent, so the useful setting is neither end.
+
+She decides per turn whether to think (~5% of turns); think markup never
+reaches the visible answer -- unclosed or stray tags are routed to the think
+field instead.
+
+`/reset` starts a new conversation, `/think` toggles reasoning display, and
+Ctrl-D exits.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+
+
+from ml.training.rl.engine import RolloutEngine, chat_turn, load_policy
+
+BANNER = """Sophia — 输入即可对话
+  /reset  开始新对话      /think  思考块显示开关
+  /temp <x>  调整温度      Ctrl-D  退出
+"""
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint", required=True)
+    parser.add_argument("--model-spec", default="configs/model/sophia.json")
+    parser.add_argument("--tokenizer-path", default="ml/modeling/text")
+    parser.add_argument("--temperature", type=float, default=0.7)
+    parser.add_argument("--top-p", type=float, default=0.92)
+    parser.add_argument("--max-new-tokens", type=int, default=320)
+    parser.add_argument("--show-think", action="store_true")
+    args = parser.parse_args()
+
+    started = time.time()
+    model, tokenizer, meta = load_policy(
+        checkpoint=args.checkpoint,
+        model_spec=args.model_spec,
+        tokenizer_path=args.tokenizer_path,
+        batch_size=1,
+    )
+    engine = RolloutEngine(
+        model=model, tokenizer=tokenizer, max_batch=1, max_new_tokens=args.max_new_tokens
+    )
+    print(
+        "loaded step={} in {:.1f}s".format(meta.get("step"), time.time() - started),
+        file=sys.stderr,
+    )
+    print(BANNER)
+
+    history: list[dict[str, str]] = []
+    show_think = bool(args.show_think)
+    temperature = float(args.temperature)
+
+    while True:
+        try:
+            line = input("你 > ").strip()
+        except (EOFError, KeyboardInterrupt):
+            print()
+            return
+        if not line:
+            continue
+        if line == "/reset":
+            history = []
+            print("（已开始新对话）\n")
+            continue
+        if line == "/think":
+            show_think = not show_think
+            print("（思考块 %s）\n" % ("显示" if show_think else "隐藏"))
+            continue
+        if line.startswith("/temp"):
+            try:
+                temperature = float(line.split()[1])
+                print(f"（温度 = {temperature:.2f}）\n")
+            except (IndexError, ValueError):
+                print("（用法：/temp 0.7）\n")
+            continue
+
+        history.append({"role": "user", "content": line})
+        started = time.time()
+        sample = chat_turn(
+            engine,
+            tokenizer,
+            history,
+            temperature=temperature,
+            top_p=float(args.top_p),
+            max_new_tokens=int(args.max_new_tokens),
+        )
+        think = sample.think
+        answer = sample.answer
+        if show_think and think:
+            print("\033[2m[think] " + think + "\033[0m")
+        print("Sophia > " + (answer or "（空）"))
+        print(
+            f"\033[2m  {sample.new_tokens} tok / {time.time() - started:.1f}s"
+            f"{'' if sample.hit_eos else ' / 未自然结束'}\033[0m\n"
+        )
+        history.append(
+            {
+                "role": "assistant",
+                "content": (
+                    "<think>" + think + "</think>" + answer if think else answer
+                ),
+            }
+        )
+        # A 4096-token window with a long tail of history eventually crowds out
+        # the reply; dropping the oldest exchange keeps the reply budget intact.
+        while len(history) > 16:
+            history = history[2:]
+
+
+if __name__ == "__main__":
+    main()

chat_template.jinja

@@ -0,0 +1,57 @@
+{%- macro render_response_format(schema) -%}
+{{- '## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n' + (schema | tojson) -}}
+{%- endmacro -%}
+{%- set bos = '<｜begin▁of▁sentence｜>' -%}
+{%- set eos = '<｜end▁of▁sentence｜>' -%}
+{%- set user = '<｜User｜>' -%}
+{%- set assistant = '<｜Assistant｜>' -%}
+{%- set latest_reminder = '<｜latest_reminder｜>' -%}
+{{- bos -}}
+{%- for message in messages -%}
+    {%- set role = message.role -%}
+    {%- set prev_role = messages[loop.index0 - 1].role if loop.index0 > 0 else none -%}
+    {%- set next_role = messages[loop.index0 + 1].role if not loop.last else none -%}
+    {%- set need_transition = loop.last or next_role == 'assistant' or next_role == 'latest_reminder' -%}
+    {%- if role == 'system' or role == 'developer' -%}
+        {%- if role == 'developer' -%}{{- user -}}{%- endif -%}
+        {{- message.content or '' -}}
+        {%- if message.response_format is defined and message.response_format -%}
+{{ '\n\n' }}{{- render_response_format(message.response_format) -}}
+        {%- endif -%}
+    {%- elif role == 'user' -%}
+        {%- set continuation_user = prev_role == 'user' -%}
+        {%- if continuation_user -%}
+            {{- '\n\n' -}}
+        {%- else -%}
+            {{- user -}}
+        {%- endif -%}
+        {%- if message.content_blocks is defined and message.content_blocks is iterable and message.content_blocks is not string and message.content_blocks|length > 0 -%}
+            {%- for block in message.content_blocks -%}
+                {%- if not loop.first -%}{{- '\n\n' -}}{%- endif -%}
+                {%- if block.type == 'text' -%}
+                    {{- block.text or '' -}}
+                {%- else -%}
+                    {{- '[Unsupported ' ~ (block.type or 'unknown') ~ ']' -}}
+                {%- endif -%}
+            {%- endfor -%}
+        {%- else -%}
+            {{- message.content or '' -}}
+        {%- endif -%}
+    {%- elif role == 'latest_reminder' -%}
+        {{- latest_reminder + (message.content or '') -}}
+    {%- elif role == 'assistant' -%}
+{% generation %}
+        {{- message.content or '' -}}
+        {%- if not (message.wo_eos is defined and message.wo_eos) -%}
+            {{- eos -}}
+        {%- endif -%}
+{% endgeneration %}
+    {%- endif -%}
+    {%- if need_transition -%}
+        {%- if role == 'user' or role == 'developer' -%}
+            {%- if not (loop.last and not add_generation_prompt) -%}
+                {{- assistant -}}
+            {%- endif -%}
+        {%- endif -%}
+    {%- endif -%}
+{%- endfor -%}

config.json

@@ -0,0 +1,54 @@
+{
+  "architectures": [
+    "SophiaForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "attn_res_block_size": 4,
+  "auto_map": {
+    "AutoConfig": "modeling_sophia.SophiaConfig",
+    "AutoModelForCausalLM": "modeling_sophia.SophiaForCausalLM"
+  },
+  "bos_token_id": 2,
+  "dim": 1536,
+  "dropout": 0.0,
+  "eos_token_id": 3,
+  "ffn_hidden": 3968,
+  "gradient_checkpointing_exclude_first": 0,
+  "gradient_checkpointing_exclude_last": 0,
+  "head_dim": 128,
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "kda_a_log_init": 0.0,
+  "kda_backend": "auto",
+  "kda_decay_lower_bound": -5.0,
+  "kda_decay_rank": 128,
+  "kda_dt_floor": 0.0001,
+  "kda_dt_max": 0.1,
+  "kda_dt_min": 0.001,
+  "kda_output_gate_full_rank": true,
+  "kda_output_gate_rank": 128,
+  "loss_chunk_size": 0,
+  "max_batch_size": 1,
+  "max_position_embeddings": 4096,
+  "max_seq_len": 4096,
+  "mla_kv_rank": 128,
+  "mla_q_rank": 384,
+  "model_type": "sophia_hybrid",
+  "n_layers": 28,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_heads": 16,
+  "num_hidden_layers": 28,
+  "pad_token_id": 0,
+  "return_logits_in_train": false,
+  "rms_norm_eps": 1e-05,
+  "short_conv_kernel": 4,
+  "situ_gate_softcap": 4.0,
+  "situ_up_softcap": 25.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.10.2",
+  "unk_token_id": 1,
+  "use_cache": true,
+  "vocab_size": 65536
+}

config_projection.py

@@ -0,0 +1,40 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+"""Projection helpers for the native Sophia Hybrid configuration."""
+
+from __future__ import annotations
+
+from dataclasses import fields
+from typing import Protocol
+
+
+class RuntimeModelArgsSource(Protocol):
+    max_seq_len: int
+
+
+def build_runtime_model_args[RuntimeModelArgsT](
+    config: RuntimeModelArgsSource,
+    *,
+    model_args_cls: type[RuntimeModelArgsT],
+    runtime_max_seq_len: int | None = None,
+) -> RuntimeModelArgsT:
+    max_seq_len = int(config.max_seq_len)
+    if runtime_max_seq_len is not None:
+        runtime_limit = int(runtime_max_seq_len)
+        if not 0 < runtime_limit <= max_seq_len:
+            raise ValueError(
+                "runtime_max_seq_len must be in (0, config.max_seq_len], "
+                f"got {runtime_limit} with max {max_seq_len}"
+            )
+        max_seq_len = runtime_limit
+    kwargs: dict[str, object] = {}
+    for arg_field in fields(model_args_cls):
+        if hasattr(config, arg_field.name):
+            kwargs[arg_field.name] = getattr(config, arg_field.name)
+    kwargs["max_seq_len"] = max_seq_len
+    return model_args_cls(**kwargs)
+
+
+__all__ = ["build_runtime_model_args"]

decoder_forward.py

@@ -0,0 +1,161 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+
+import torch
+
+from .cache_decode import RuntimeCacheState, forward_cached_decode
+from .input_mask import is_all_ones_mask
+from .decoder_full import (
+    forward_decoder_full,
+    validate_decoder_inputs,
+)
+from .decoder_output import DecoderFormattedOutput
+
+
+class _DecoderForwardBase[
+    CacheInputT, CacheOutputT, RuntimeOutputT
+]:
+    def _resolve_runtime_return_dict(self, *, return_dict: bool | None) -> bool:
+        raise NotImplementedError
+
+    def _format_decoder_output(
+        self,
+        *,
+        loss: torch.Tensor | None,
+        logits: torch.Tensor | None,
+        cache: CacheOutputT | None,
+        return_dict: bool,
+    ) -> RuntimeOutputT:
+        raise NotImplementedError
+
+    def _cache_state_from_cache(
+        self,
+        cache: CacheInputT,
+    ) -> RuntimeCacheState | None:
+        raise NotImplementedError
+
+    def _cache_output_from_state(self, cache_state: RuntimeCacheState) -> CacheOutputT:
+        raise NotImplementedError
+
+    def _forward_full_decoder(
+        self,
+        *,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        labels: torch.Tensor | None,
+        compute_loss: bool,
+        return_dict: bool,
+    ) -> RuntimeOutputT:
+        with self._model_runtime_lock:
+            loss, logits = forward_decoder_full(
+                runtime_model=self.model,
+                config=self.config,
+                training=bool(self.training),
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                labels=labels,
+                compute_loss=bool(compute_loss),
+                output_weight=self.model.output.weight,
+            )
+            return self._format_decoder_output(
+                loss=loss,
+                logits=logits,
+                cache=None,
+                return_dict=bool(return_dict),
+            )
+
+    def _forward_cached_decoder(
+        self,
+        *,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        cache: CacheInputT,
+        start_pos: int | None,
+        logits_to_keep: int | None,
+        return_dict: bool,
+    ) -> RuntimeOutputT:
+        if attention_mask is not None and not is_all_ones_mask(attention_mask):
+            raise ValueError("Sophia cached decode only supports unpadded prompts")
+        with self._model_runtime_lock:
+            logits, next_cache = forward_cached_decode(
+                model=self.model,
+                input_ids=input_ids,
+                cache_state=self._cache_state_from_cache(cache),
+                start_pos=start_pos,
+                logits_to_keep=logits_to_keep,
+            )
+            return self._format_decoder_output(
+                loss=None,
+                logits=logits,
+                cache=self._cache_output_from_state(next_cache),
+                return_dict=bool(return_dict),
+            )
+
+    @staticmethod
+    def _requires_full_path(
+        *,
+        training: bool,
+        labels: torch.Tensor | None,
+        use_cache: bool,
+    ) -> bool:
+        return labels is not None or bool(training) or not bool(use_cache)
+
+
+class DecoderForwardMixin(
+    _DecoderForwardBase[
+        RuntimeCacheState | None,
+        RuntimeCacheState,
+        DecoderFormattedOutput,
+    ]
+):
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        use_cache: bool | None = None,
+        cache: RuntimeCacheState | None = None,
+        return_dict: bool | None = None,
+        logits_to_keep: int | None = None,
+        start_pos: int | None = None,
+        compute_loss: bool = False,
+        **_: object,
+    ) -> DecoderFormattedOutput:
+        input_ids = validate_decoder_inputs(
+            input_ids=input_ids,
+            labels=labels,
+            compute_loss=bool(compute_loss),
+        )
+        resolved_use_cache = bool(
+            self.config.use_cache if use_cache is None else use_cache
+        )
+        resolved_return_dict = self._resolve_runtime_return_dict(
+            return_dict=return_dict
+        )
+        if self._requires_full_path(
+            training=bool(self.training),
+            labels=labels,
+            use_cache=resolved_use_cache,
+        ):
+            return self._forward_full_decoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                labels=labels,
+                compute_loss=bool(compute_loss),
+                return_dict=bool(resolved_return_dict),
+            )
+        return self._forward_cached_decoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            cache=cache,
+            start_pos=start_pos,
+            logits_to_keep=logits_to_keep,
+            return_dict=bool(resolved_return_dict),
+        )
+
+
+__all__ = ["DecoderForwardMixin"]

decoder_full.py

@@ -0,0 +1,168 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+import torch
+
+from .input_mask import is_all_ones_mask, slice_valid_tokens
+from .decoder_types import DecoderConfig, DecoderCoreModel
+from .decoder_loss import (
+    loss_stats,
+    mean_cross_entropy_loss,
+)
+from .decoder_loss_forward import forward_loss
+
+
+def validate_decoder_inputs(
+    *,
+    input_ids: torch.Tensor | None,
+    labels: torch.Tensor | None,
+    compute_loss: bool,
+) -> torch.Tensor:
+    if input_ids is None:
+        raise ValueError("input_ids is required")
+    if input_ids.dim() != 2:
+        raise ValueError("input_ids must be [B,T]")
+    if bool(compute_loss) and labels is None:
+        raise ValueError("compute_loss=True requires labels")
+    return input_ids
+
+
+def forward_full_with_mask(
+    *,
+    runtime_model: DecoderCoreModel,
+    vocab_size: int,
+    input_ids: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    output_weight: torch.Tensor,
+) -> torch.Tensor:
+    if attention_mask is None or is_all_ones_mask(attention_mask):
+        return runtime_model.forward_full(input_ids)
+
+    rows = slice_valid_tokens(input_ids, attention_mask)
+    logits = output_weight.new_zeros(
+        (int(input_ids.size(0)), int(input_ids.size(1)), int(vocab_size))
+    )
+    for batch_index, (start, end, row_tokens) in enumerate(rows):
+        row_logits = runtime_model.forward_full(row_tokens)
+        logits[batch_index, start:end] = row_logits[0]
+    return logits
+
+
+def masked_loss(
+    *,
+    runtime_model: DecoderCoreModel,
+    config: DecoderConfig,
+    input_ids: torch.Tensor,
+    attention_mask: torch.Tensor,
+    labels: torch.Tensor,
+    vocab_size: int,
+    output_weight: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    rows = slice_valid_tokens(input_ids, attention_mask)
+    logits = output_weight.new_zeros(
+        (int(input_ids.size(0)), int(input_ids.size(1)), int(vocab_size))
+    )
+    loss_sum = output_weight.new_zeros(())
+    count = output_weight.new_zeros(())
+    for batch_index, (start, end, row_tokens) in enumerate(rows):
+        row_labels = labels[batch_index : batch_index + 1, start:end]
+        row_logits = runtime_model.forward_full(row_tokens)
+        logits[batch_index, start:end] = row_logits[0]
+        row_sum, row_count = loss_stats(row_logits, row_labels, label_offset=0)
+        loss_sum = loss_sum + row_sum
+        count = count + row_count
+    loss = torch.where(
+        count > 0,
+        loss_sum / count.clamp_min(1.0),
+        output_weight.new_zeros(()),
+    )
+    return logits, loss
+
+
+def forward_decoder_full(
+    *,
+    runtime_model: DecoderCoreModel,
+    config: DecoderConfig,
+    training: bool,
+    input_ids: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    labels: torch.Tensor | None,
+    compute_loss: bool,
+    output_weight: torch.Tensor,
+) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+    if bool(compute_loss):
+        loss = forward_loss(
+            runtime_model=runtime_model,
+            config=config,
+            training=bool(training),
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+            output_weight=output_weight,
+        )
+        return loss, None
+    loss, logits = forward_full(
+        runtime_model=runtime_model,
+        config=config,
+        training=bool(training),
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        labels=labels,
+    )
+    if labels is not None and bool(training) and not bool(config.return_logits_in_train):
+        logits = None
+    return loss, logits
+
+
+def forward_full(
+    *,
+    runtime_model: DecoderCoreModel,
+    config: DecoderConfig,
+    training: bool,
+    input_ids: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    labels: torch.Tensor | None,
+) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+    del training
+    if labels is not None:
+        if attention_mask is not None and not is_all_ones_mask(attention_mask):
+            logits, loss = masked_loss(
+                runtime_model=runtime_model,
+                config=config,
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                labels=labels,
+                vocab_size=int(getattr(config, "vocab_size", 0)),
+                output_weight=runtime_model.output.weight,
+            )
+            return loss, logits
+
+        logits = forward_full_with_mask(
+            runtime_model=runtime_model,
+            vocab_size=int(config.vocab_size),
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_weight=runtime_model.output.weight,
+        )
+        return mean_cross_entropy_loss(logits, labels, label_offset=0), logits
+
+    logits = forward_full_with_mask(
+        runtime_model=runtime_model,
+        vocab_size=int(config.vocab_size),
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        output_weight=runtime_model.output.weight,
+    )
+    return None, logits
+
+
+__all__ = [
+    "forward_full_with_mask",
+    "forward_decoder_full",
+    "forward_full",
+    "masked_loss",
+    "validate_decoder_inputs",
+]

decoder_host.py

@@ -0,0 +1,47 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from typing import Protocol
+
+from .runtime_backend import (
+    RuntimeBackend,
+)
+
+
+class DecoderConfig(Protocol):
+    def to_model_args(
+        self,
+        *,
+        runtime_max_seq_len: int | None = None,
+    ) -> object: ...
+
+
+class DecoderHostMixin:
+    def _initialize_decoder_runtime(
+        self,
+        *,
+        config: DecoderConfig,
+        runtime_max_seq_len: int | None,
+        runtime_backend: RuntimeBackend,
+        gradient_checkpointing_enabled: bool,
+        register_tied_weights: bool = False,
+    ) -> None:
+        self.model = runtime_backend.transformer_cls(
+            config.to_model_args(runtime_max_seq_len=runtime_max_seq_len)
+        )
+        self.runtime = self.model.runtime
+        if bool(register_tied_weights):
+            self.all_tied_weights_keys = self.get_expanded_tied_weights_keys(
+                all_submodels=False
+            )
+        # Cache-enabled runtime execution mutates shared KV/index buffers, so a
+        # single model instance cannot safely run concurrent calls.
+        self._model_runtime_lock = self.model.runtime_lock
+        self._sync_runtime_gradient_checkpointing(
+            enable=bool(gradient_checkpointing_enabled)
+        )
+
+__all__ = ["DecoderConfig", "DecoderHostMixin"]

decoder_loss.py

@@ -0,0 +1,474 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from functools import lru_cache
+import importlib
+
+import torch
+from torch import nn
+
+from .loss_stats import mean_loss_from_sum_and_count
+from .loss_stats import shifted_loss_sum_and_count
+from .decoder_types import DecoderConfig
+
+def loss_stats(
+    logits: torch.Tensor,
+    labels: torch.Tensor,
+    *,
+    label_offset: int = 0,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return shifted_loss_sum_and_count(
+        logits,
+        labels,
+        label_offset=label_offset,
+        ignore_index=-100,
+    )
+
+
+def mean_cross_entropy_loss(
+    logits: torch.Tensor,
+    labels: torch.Tensor,
+    *,
+    label_offset: int = 0,
+) -> torch.Tensor:
+    loss_sum, count = loss_stats(logits, labels, label_offset=label_offset)
+    return mean_loss_from_sum_and_count(
+        loss_sum=loss_sum,
+        count=count,
+        reference=logits,
+    )
+
+
+@lru_cache(maxsize=1)
+def _liger_fused_linear_ce_func() -> Callable[..., torch.Tensor]:
+    module_name = "liger_kernel.transformers.functional"
+    try:
+        module = importlib.import_module(module_name)
+    except ModuleNotFoundError as exc:
+        if exc.name == "liger_kernel":
+            raise RuntimeError(
+                "Liger fused linear cross entropy requires liger_kernel with "
+                "liger_fused_linear_cross_entropy"
+            ) from exc
+        raise
+    fn = getattr(module, "liger_fused_linear_cross_entropy", None)
+    if fn is None:
+        raise RuntimeError(f"{module_name}.liger_fused_linear_cross_entropy is unavailable")
+    return fn
+
+
+@lru_cache(maxsize=1)
+def _liger_graph_safe_ops():
+    module_name = "liger_kernel.ops.fused_linear_cross_entropy"
+    try:
+        module = importlib.import_module(module_name)
+    except ModuleNotFoundError as exc:
+        if exc.name == "liger_kernel":
+            raise RuntimeError(
+                "Liger fused linear cross entropy requires liger_kernel"
+            ) from exc
+        raise
+    required = (
+        "MAX_FUSED_SIZE",
+        "element_mul_kernel",
+        "is_hip",
+        "liger_cross_entropy_kernel",
+        "triton",
+    )
+    missing = [name for name in required if not hasattr(module, name)]
+    if missing:
+        raise RuntimeError(
+            f"{module_name} is missing required graph-safe operators: {missing}"
+        )
+    return module
+
+
+def _liger_linear_ce_loss_chunk_stats(
+    hidden_chunk: torch.Tensor,
+    chunk_labels: torch.Tensor,
+    *,
+    norm: nn.Module,
+    output: nn.Module,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    weight = getattr(output, "weight", None)
+    bias = getattr(output, "bias", None)
+    if not torch.is_tensor(weight):
+        raise TypeError("liger_linear_ce loss backend requires output.weight")
+
+    flat_labels = chunk_labels.reshape(-1).to(dtype=torch.long)
+    keep = flat_labels != -100
+    count = keep.sum().to(dtype=torch.float32)
+
+    hidden_norm = norm(hidden_chunk).reshape(-1, int(hidden_chunk.size(-1)))
+    loss_sum = _call_liger_fused_linear_ce(
+        hidden_norm,
+        weight,
+        flat_labels,
+        bias=bias,
+    )
+    if not torch.is_tensor(loss_sum):
+        raise RuntimeError("liger_fused_linear_cross_entropy must return a tensor loss")
+    return loss_sum.float(), count
+
+
+def _reference_linear_ce_loss_chunk_stats(
+    hidden_chunk: torch.Tensor,
+    chunk_labels: torch.Tensor,
+    *,
+    norm: nn.Module,
+    output: nn.Module,
+    tile: int = 1024,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """The same statistic as the fused kernel, in plain PyTorch.
+
+    Liger's fused linear cross entropy is a Triton kernel, so on a device
+    without one the model cannot score its own loss at all -- which is exactly
+    what a host wants to do when the accelerator is busy training. Every other
+    device-specific path in the model already has a reference implementation to
+    fall back to; this is the one that did not.
+
+    Logits are materialised a tile of rows at a time because the vocabulary is
+    65,536 wide and a whole chunk at once is gigabytes for no reason.
+    """
+    weight = getattr(output, "weight", None)
+    bias = getattr(output, "bias", None)
+    if not torch.is_tensor(weight):
+        raise TypeError("reference linear_ce loss backend requires output.weight")
+
+    flat_labels = chunk_labels.reshape(-1).to(dtype=torch.long)
+    keep = flat_labels != -100
+    count = keep.sum().to(dtype=torch.float32)
+
+    hidden_norm = norm(hidden_chunk).reshape(-1, int(hidden_chunk.size(-1)))
+    weight_f32 = weight.float()
+    bias_f32 = None if bias is None else bias.float()
+    loss_sum = hidden_norm.new_zeros((), dtype=torch.float32)
+    rows = int(hidden_norm.size(0))
+    for start in range(0, rows, int(tile)):
+        end = min(start + int(tile), rows)
+        logits = torch.nn.functional.linear(
+            hidden_norm[start:end].float(), weight_f32, bias_f32
+        )
+        loss_sum = loss_sum + torch.nn.functional.cross_entropy(
+            logits,
+            flat_labels[start:end],
+            ignore_index=-100,
+            reduction="sum",
+        )
+    return loss_sum.float(), count
+
+
+class _GraphSafeLinearCE(torch.autograd.Function):
+    _TILE = 128
+    _CUDA_CHUNK_SIZE = 4096
+
+    @staticmethod
+    def forward(
+        ctx,
+        hidden: torch.Tensor,
+        weight: torch.Tensor,
+        labels: torch.Tensor,
+        bias: torch.Tensor,
+    ) -> torch.Tensor:
+        if hidden.is_cuda:
+            ops = _liger_graph_safe_ops()
+            token_count = int(hidden.size(0))
+            vocab_size = int(weight.shape[0])
+            block_size = min(
+                int(ops.MAX_FUSED_SIZE),
+                int(ops.triton.next_power_of_2(vocab_size)),
+            )
+            # The release graph is bounded by the 4096-token context. A single
+            # full-width logits GEMM maximizes M without introducing a second
+            # sequence-length capability or an unbounded workspace.
+            chunk_size = min(int(token_count), _GraphSafeLinearCE._CUDA_CHUNK_SIZE)
+            chunk_count = int(ops.triton.cdiv(int(token_count), chunk_size))
+            has_bias = bool(bias.numel())
+            grad_hidden = torch.empty_like(hidden)
+            grad_weight = torch.empty_like(weight)
+            grad_bias = torch.empty_like(bias) if has_bias else None
+            loss_per_token = torch.zeros(
+                int(token_count),
+                dtype=torch.float32,
+                device=hidden.device,
+            )
+
+            for chunk_index in range(chunk_count):
+                start = chunk_index * chunk_size
+                end = min(start + chunk_size, int(token_count))
+                hidden_chunk = hidden[start:end]
+                logits = hidden_chunk @ weight.t()
+                if has_bias:
+                    logits = logits + bias
+                logits = logits.contiguous()
+                label_chunk = labels[start:end].contiguous()
+                loss_chunk = loss_per_token[start:end]
+                row_count = int(logits.shape[0])
+                ops.liger_cross_entropy_kernel[(row_count,)](
+                    X_ptr=logits,
+                    X_stride=logits.stride(-2),
+                    Y_ptr=label_chunk,
+                    Y_stride=label_chunk.stride(-1),
+                    weight_ptr=None,
+                    loss_ptr=loss_chunk,
+                    z_loss_ptr=None,
+                    loss_stride=loss_chunk.stride(-1),
+                    token_accuracy_ptr=None,
+                    token_accuracy_stride=0,
+                    predicted_tokens_ptr=None,
+                    predicted_tokens_stride=0,
+                    n_cols=vocab_size,
+                    n_non_ignore=int(token_count),
+                    sum_non_ignore_weight=int(token_count),
+                    weight_sum=0.0,
+                    ignore_index=-100,
+                    lse_square_scale=0.0,
+                    label_smoothing=0.0,
+                    reduction="sum",
+                    softcap=None,
+                    RETURN_Z_LOSS=False,
+                    RETURN_TOKEN_ACCURACY=False,
+                    RETURN_PREDICTED_TOKENS=False,
+                    HAS_WEIGHT=False,
+                    HAS_SOFTCAPPING=False,
+                    HAS_GRADIENTS=True,
+                    BLOCK_SIZE=block_size,
+                    num_warps=32 if not ops.is_hip() else 16,
+                )
+                torch.mm(
+                    logits,
+                    weight,
+                    out=grad_hidden[start:end],
+                )
+                if chunk_index == 0:
+                    torch.mm(logits.transpose(0, 1), hidden_chunk, out=grad_weight)
+                else:
+                    torch.addmm(
+                        grad_weight,
+                        logits.transpose(0, 1),
+                        hidden_chunk,
+                        out=grad_weight,
+                    )
+                if grad_bias is not None:
+                    bias_grad = logits.sum(dim=0)
+                    if chunk_index == 0:
+                        grad_bias.copy_(bias_grad)
+                    else:
+                        grad_bias.add_(bias_grad)
+
+            saved_bias_grad = (
+                grad_bias if grad_bias is not None else hidden.new_empty((0,))
+            )
+            ctx.save_for_backward(grad_hidden, grad_weight, saved_bias_grad)
+            ctx.has_bias = has_bias
+            ctx.liger_cuda = True
+            return loss_per_token.sum()
+
+        ctx.save_for_backward(hidden, weight, labels, bias)
+        ctx.has_bias = bool(bias.numel())
+        ctx.liger_cuda = False
+        total = hidden.new_zeros((), dtype=torch.float32)
+        with torch.no_grad():
+            for start in range(0, int(hidden.size(0)), _GraphSafeLinearCE._TILE):
+                logits = torch.nn.functional.linear(
+                    hidden[start : start + _GraphSafeLinearCE._TILE],
+                    weight,
+                    bias if ctx.has_bias else None,
+                )
+                total = total + torch.nn.functional.cross_entropy(
+                    logits,
+                    labels[start : start + _GraphSafeLinearCE._TILE],
+                    ignore_index=-100,
+                    reduction="sum",
+                ).float()
+        return total
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor):
+        if ctx.liger_cuda:
+            saved_hidden_grad, saved_weight_grad, saved_bias_grad = ctx.saved_tensors
+            grad_hidden = torch.empty_like(saved_hidden_grad)
+            grad_weight = torch.empty_like(saved_weight_grad)
+            grad_hidden.copy_(saved_hidden_grad)
+            grad_weight.copy_(saved_weight_grad)
+            ops = _liger_graph_safe_ops()
+            block_size = min(
+                int(ops.MAX_FUSED_SIZE),
+                int(ops.triton.next_power_of_2(int(grad_hidden.shape[-1]))),
+            )
+            num_warps = 32 if not ops.is_hip() else 16
+            ops.element_mul_kernel[(int(grad_hidden.shape[0]),)](
+                grad_hidden,
+                grad_hidden.stride(-2),
+                grad_output,
+                int(grad_hidden.shape[-1]),
+                BLOCK_SIZE=block_size,
+                num_warps=num_warps,
+            )
+            ops.element_mul_kernel[(int(grad_weight.shape[0]),)](
+                grad_weight,
+                grad_weight.stride(-2),
+                grad_output,
+                int(grad_weight.shape[-1]),
+                BLOCK_SIZE=block_size,
+                num_warps=num_warps,
+            )
+            grad_bias = None
+            if ctx.has_bias:
+                grad_bias = torch.empty_like(saved_bias_grad)
+                grad_bias.copy_(saved_bias_grad)
+                ops.element_mul_kernel[(int(grad_bias.shape[0]),)](
+                    grad_bias,
+                    grad_bias.stride(-1),
+                    grad_output,
+                    1,
+                    BLOCK_SIZE=block_size,
+                    num_warps=num_warps,
+                )
+            return grad_hidden, grad_weight, None, grad_bias
+
+        hidden, weight, labels, bias = ctx.saved_tensors
+        grad_hidden = torch.zeros_like(hidden)
+        grad_weight = torch.zeros_like(weight)
+        grad_bias = torch.zeros_like(bias) if ctx.has_bias else None
+        for start in range(0, int(hidden.size(0)), _GraphSafeLinearCE._TILE):
+            hidden_tile = hidden[start : start + _GraphSafeLinearCE._TILE]
+            labels_tile = labels[start : start + _GraphSafeLinearCE._TILE]
+            logits = torch.nn.functional.linear(
+                hidden_tile,
+                weight,
+                bias if ctx.has_bias else None,
+            )
+            valid = labels_tile != -100
+            probs = torch.softmax(logits, dim=-1)
+            safe_labels = labels_tile.clamp_min(0).unsqueeze(1)
+            probs.scatter_add_(
+                1,
+                safe_labels,
+                -valid.to(dtype=probs.dtype).unsqueeze(1),
+            )
+            probs.mul_(valid.unsqueeze(1))
+            torch.mm(
+                probs,
+                weight,
+                out=grad_hidden[start : start + _GraphSafeLinearCE._TILE],
+            )
+            torch.addmm(
+                grad_weight,
+                probs.transpose(0, 1),
+                hidden_tile,
+                out=grad_weight,
+            )
+            if grad_bias is not None:
+                grad_bias.add_(probs.sum(dim=0))
+        scale = grad_output.to(dtype=grad_hidden.dtype)
+        return grad_hidden * scale, grad_weight * scale, None, (
+            None if grad_bias is None else grad_bias * scale
+        )
+
+
+@torch.compiler.disable
+def _call_liger_fused_linear_ce(
+    hidden_norm: torch.Tensor,
+    weight: torch.Tensor,
+    flat_labels: torch.Tensor,
+    *,
+    bias: torch.Tensor | None,
+) -> torch.Tensor:
+    if hidden_norm.is_cuda and torch.cuda.is_current_stream_capturing():
+        bias_arg = (
+            bias if bias is not None else hidden_norm.new_empty((0,))
+        )
+        return _GraphSafeLinearCE.apply(hidden_norm, weight, flat_labels, bias_arg)
+    fn = _liger_fused_linear_ce_func()
+    try:
+        return fn(
+            hidden_norm,
+            weight,
+            flat_labels,
+            bias=bias,
+            ignore_index=-100,
+            reduction="sum",
+        )
+    except TypeError as exc:
+        raise RuntimeError(
+            "installed liger_fused_linear_cross_entropy has an unsupported signature"
+        ) from exc
+
+
+def _loss_chunk_stats(
+    hidden_chunk: torch.Tensor,
+    chunk_labels: torch.Tensor,
+    *,
+    norm: nn.Module,
+    output: nn.Module,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    if bool(
+        getattr(output, "_sophia_release_cuda_cache_before_linear_ce", False)
+    ):
+        output._sophia_release_cuda_cache_before_linear_ce = False
+        torch.cuda.empty_cache()
+    if not hidden_chunk.is_cuda:
+        return _reference_linear_ce_loss_chunk_stats(
+            hidden_chunk,
+            chunk_labels,
+            norm=norm,
+            output=output,
+        )
+    return _liger_linear_ce_loss_chunk_stats(
+        hidden_chunk,
+        chunk_labels,
+        norm=norm,
+        output=output,
+    )
+
+
+def chunked_loss_stats_from_hidden(
+    hidden: torch.Tensor,
+    labels: torch.Tensor,
+    *,
+    label_offset: int,
+    norm: nn.Module,
+    output: nn.Module,
+    config: DecoderConfig,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    max_pred_tokens = min(
+        max(int(hidden.size(1)) - 1, 0),
+        max(int(labels.size(1)) - int(label_offset) - 1, 0),
+    )
+    zero = hidden.new_zeros(())
+    if max_pred_tokens <= 0:
+        return zero, zero
+
+    loss_sum = zero
+    count = zero
+    chunk_size = max(int(config.loss_chunk_size), 0)
+    if chunk_size <= 0:
+        chunk_size = int(max_pred_tokens)
+    target_base = int(label_offset) + 1
+    for token_start in range(0, max_pred_tokens, chunk_size):
+        token_end = min(token_start + chunk_size, max_pred_tokens)
+        chunk_labels = labels[
+            :,
+            target_base + token_start : target_base + token_end,
+        ].contiguous()
+        chunk_loss_sum, chunk_count = _loss_chunk_stats(
+            hidden[:, token_start:token_end],
+            chunk_labels,
+            norm=norm,
+            output=output,
+        )
+        loss_sum = loss_sum + chunk_loss_sum
+        count = count + chunk_count
+    return loss_sum, count
+
+
+__all__ = [
+    "chunked_loss_stats_from_hidden",
+    "loss_stats",
+    "mean_cross_entropy_loss",
+]

decoder_loss_forward.py

@@ -0,0 +1,47 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+import torch
+
+from .input_mask import validate_right_padding_mask
+from .loss_stats import mean_loss_from_sum_and_count
+from .decoder_types import DecoderConfig
+from .decoder_types import DecoderCoreModel
+from .decoder_loss import chunked_loss_stats_from_hidden
+
+
+def forward_loss(
+    *,
+    runtime_model: DecoderCoreModel,
+    config: DecoderConfig,
+    training: bool,
+    input_ids: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    labels: torch.Tensor,
+    output_weight: torch.Tensor,
+) -> torch.Tensor:
+    del training
+    ref = output_weight
+    if attention_mask is not None:
+        validate_right_padding_mask(attention_mask, input_ids=input_ids)
+    hidden, _ = runtime_model._forward_hidden(input_ids, start_pos=0)
+    base_sum, base_count = chunked_loss_stats_from_hidden(
+        hidden,
+        labels,
+        label_offset=0,
+        norm=runtime_model.norm,
+        output=runtime_model.output,
+        config=config,
+    )
+
+    return mean_loss_from_sum_and_count(
+        loss_sum=base_sum,
+        count=base_count,
+        reference=ref,
+    )
+
+
+__all__ = ["forward_loss"]

decoder_output.py

@@ -0,0 +1,115 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from collections.abc import Mapping
+from typing import Protocol
+
+import torch
+
+from .cache_decode import RuntimeCacheState
+
+
+@dataclass
+class DecoderOutput:
+    loss: torch.Tensor | None = None
+    logits: torch.Tensor | None = None
+    cache: RuntimeCacheState | None = None
+
+
+DecoderLogitsOutput = tuple[torch.Tensor | None, torch.Tensor | None]
+DecoderCachedOutput = tuple[torch.Tensor, RuntimeCacheState]
+DecoderFormattedOutput = DecoderOutput | DecoderLogitsOutput | DecoderCachedOutput
+
+
+class SupportsReturnDict(Protocol):
+    return_dict: bool
+
+
+def require_output_loss(output: object, *, context: str) -> torch.Tensor:
+    loss: object | None
+    if isinstance(output, Mapping):
+        loss = output.get("loss")
+    else:
+        loss = getattr(output, "loss", None)
+    if not torch.is_tensor(loss):
+        raise RuntimeError(f"{context} returned loss=None")
+    return loss
+
+
+def require_output_logits(output: object, *, context: str) -> torch.Tensor:
+    logits: object | None
+    if isinstance(output, Mapping):
+        logits = output.get("logits")
+    else:
+        logits = getattr(output, "logits", None)
+    if not torch.is_tensor(logits):
+        raise RuntimeError(f"{context} returned logits=None")
+    return logits
+
+
+def resolve_return_dict(
+    *,
+    config: SupportsReturnDict,
+    return_dict: bool | None,
+) -> bool:
+    if return_dict is None:
+        return bool(config.return_dict)
+    return bool(return_dict)
+
+
+def format_decoder_output(
+    *,
+    loss: torch.Tensor | None,
+    logits: torch.Tensor | None,
+    cache: RuntimeCacheState | None = None,
+    return_dict: bool,
+) -> DecoderFormattedOutput:
+    if bool(return_dict):
+        return DecoderOutput(
+            loss=loss,
+            logits=logits,
+            cache=cache,
+        )
+    if cache is not None:
+        if logits is None:
+            raise ValueError("cached decode output requires logits")
+        return logits, cache
+    return loss, logits
+
+
+def format_hf_causal_lm_output(
+    *,
+    output_cls: type,
+    loss: torch.Tensor | None,
+    logits: torch.Tensor | None,
+    past_key_values: object = None,
+    return_dict: bool,
+) -> object:
+    if bool(return_dict):
+        return output_cls(
+            loss=loss,
+            logits=logits,
+            past_key_values=past_key_values,
+        )
+    if past_key_values is not None:
+        if logits is None:
+            raise ValueError("cached decode output requires logits")
+        return logits, past_key_values
+    if loss is not None:
+        return loss, logits
+    return (logits,)
+
+
+__all__ = [
+    "DecoderOutput",
+    "DecoderFormattedOutput",
+    "format_hf_causal_lm_output",
+    "format_decoder_output",
+    "require_output_logits",
+    "require_output_loss",
+    "resolve_return_dict",
+]

decoder_runtime.py

@@ -0,0 +1,298 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from threading import RLock
+from typing import Protocol
+
+import torch
+from torch import nn
+
+from .model_state import RuntimeCacheSnapshot
+from .runtime_contracts import RuntimeHost
+
+
+class DecoderRuntimeConfig(Protocol):
+    max_seq_len: int
+    max_batch_size: int
+    loss_chunk_size: int
+    gradient_checkpointing_exclude_first: int
+    gradient_checkpointing_exclude_last: int
+
+
+class DecoderRuntimeModel(Protocol):
+    tok_embeddings: nn.Module
+    output: nn.Module
+    gradient_checkpointing: bool
+    gradient_checkpointing_exclude_first: int
+    gradient_checkpointing_exclude_last: int
+    def modules(self): ...
+    def parameters(self, recurse: bool = True): ...
+
+
+class DecoderRuntimeBase:
+    def _runtime_model(self) -> DecoderRuntimeModel:
+        from typing import cast
+
+        return cast(DecoderRuntimeModel, self.model)
+
+    def _runtime_host(self) -> RuntimeHost:
+        from typing import cast
+
+        return cast(RuntimeHost, self.runtime_host)
+
+    def _runtime_config(self) -> DecoderRuntimeConfig:
+        from typing import cast
+
+        return cast(DecoderRuntimeConfig, self.config)
+
+    @property
+    def runtime_model(self):
+        return self._runtime_model()
+
+    @property
+    def runtime_host(self):
+        return self.runtime
+
+    @property
+    def runtime_lock(self) -> RLock:
+        return self._model_runtime_lock
+
+
+class DecoderRuntimeMixin(DecoderRuntimeBase):
+    def reset_runtime_cache(self) -> None:
+        self._runtime_host().reset_runtime_cache()
+
+    def refresh_state_buffers(self) -> None:
+        self._runtime_host().refresh_state_buffers()
+
+    def replay_with_cache(
+        self,
+        input_ids: torch.Tensor,
+        start_pos: int = 0,
+        return_all_logits: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        return self._runtime_host().replay_with_cache(
+            input_ids,
+            start_pos=start_pos,
+            return_all_logits=return_all_logits,
+        )
+
+    def forward_with_last_hidden(
+        self,
+        input_ids: torch.Tensor,
+        start_pos: int = 0,
+        return_all_logits: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        return self._runtime_host().forward_with_last_hidden(
+            input_ids,
+            start_pos=start_pos,
+            return_all_logits=return_all_logits,
+        )
+
+    def cache_dump(
+        self,
+        device: str = "cpu",
+        *,
+        cache_pos: int | None = None,
+        batch_size: int | None = None,
+    ) -> RuntimeCacheSnapshot:
+        return self._runtime_host().cache_dump(
+            device=device,
+            cache_pos=cache_pos,
+            batch_size=batch_size,
+        )
+
+    def cache_load(self, cache_snapshot: RuntimeCacheSnapshot) -> None:
+        self._runtime_host().cache_load(cache_snapshot)
+
+    def runtime_max_seq_len(self) -> int:
+        return int(self._runtime_host().runtime_max_seq_len())
+
+    def _sync_runtime_gradient_checkpointing(self, *, enable: bool) -> None:
+        model = self._runtime_model()
+        config = self._runtime_config()
+        model.gradient_checkpointing = bool(enable)
+        model.gradient_checkpointing_exclude_first = int(
+            config.gradient_checkpointing_exclude_first
+        )
+        model.gradient_checkpointing_exclude_last = int(
+            config.gradient_checkpointing_exclude_last
+        )
+
+
+class DecoderModelMixin(DecoderRuntimeBase):
+    def _sync_tied_runtime_word_embeddings(self) -> None:
+        config = getattr(self, "config", None)
+        if not bool(getattr(config, "tie_word_embeddings", False)):
+            return
+        runtime_model = self._runtime_model()
+        input_embeddings = runtime_model.tok_embeddings
+        output_embeddings = runtime_model.output
+        if not hasattr(input_embeddings, "weight") or not hasattr(
+            output_embeddings, "weight"
+        ):
+            return
+        input_weight = input_embeddings.weight
+        output_weight = output_embeddings.weight
+        if tuple(input_weight.shape) != tuple(output_weight.shape):
+            raise ValueError(
+                "tied word embeddings require matching embedding/output shapes; "
+                f"got {tuple(input_weight.shape)!r} vs {tuple(output_weight.shape)!r}"
+            )
+        output_embeddings.weight = input_weight
+
+    def _rebuild_runtime_buffers(self) -> None:
+        self._runtime_host().rebuild_runtime_buffers()
+
+    @staticmethod
+    @contextmanager
+    def get_input_embeddings(self) -> nn.Module:
+        return self._runtime_model().tok_embeddings
+
+    def set_input_embeddings(self, value: nn.Module) -> None:
+        with self._model_runtime_lock:
+            self.reset_runtime_cache()
+            self._runtime_model().tok_embeddings = value
+            self._sync_tied_runtime_word_embeddings()
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self._runtime_model().output
+
+    def set_output_embeddings(self, value: nn.Module) -> None:
+        with self._model_runtime_lock:
+            self.reset_runtime_cache()
+            self._runtime_model().output = value
+            self._sync_tied_runtime_word_embeddings()
+
+    def to(self, *args, **kwargs):
+        with self._model_runtime_lock:
+            runtime_model = self._runtime_model()
+            complex_buffers: list[tuple[nn.Module, str, torch.Tensor]] = []
+            for module in runtime_model.modules():
+                for name, buf in list(getattr(module, "_buffers", {}).items()):
+                    if torch.is_tensor(buf) and torch.is_complex(buf):
+                        complex_buffers.append((module, name, module._buffers.pop(name)))
+            try:
+                module = super().to(*args, **kwargs)
+            finally:
+                root_param = next(runtime_model.parameters(), None)
+                root_device = (
+                    torch.device("cpu") if root_param is None else root_param.device
+                )
+                for owner, name, buf in complex_buffers:
+                    owner_param = next(owner.parameters(), None)
+                    target_device = (
+                        root_device if owner_param is None else owner_param.device
+                    )
+                    owner.register_buffer(
+                        name,
+                        buf.to(device=target_device),
+                        persistent=False,
+                    )
+                self._rebuild_runtime_buffers()
+            return module
+
+    def get_submodule(self, target: str) -> nn.Module:
+        try:
+            return super().get_submodule(target)
+        except AttributeError:
+            return self.model.get_submodule(target)
+
+    def train(self, mode: bool = True):
+        with self._model_runtime_lock:
+            if bool(mode):
+                self.reset_runtime_cache()
+            return super().train(mode)
+
+    def load_state_dict(
+        self,
+        state_dict: dict[str, torch.Tensor],
+        strict: bool = True,
+        assign: bool = False,
+    ):
+        with self._model_runtime_lock:
+            self.reset_runtime_cache()
+            result = super().load_state_dict(
+                state_dict,
+                strict=strict,
+                assign=assign,
+            )
+            self._sync_tied_runtime_word_embeddings()
+            self._rebuild_runtime_buffers()
+            return result
+
+
+class DecoderRecipeMixin(DecoderRuntimeBase):
+    def ensure_runtime_max_seq_len(self, max_seq_len: int) -> None:
+        required = int(max_seq_len)
+        config = self._runtime_config()
+        if required <= 0:
+            raise ValueError(f"max_seq_len must be > 0, got {max_seq_len}")
+        if required > int(config.max_seq_len):
+            raise ValueError(
+                "runtime max_seq_len cannot exceed config.max_seq_len: "
+                f"{required} > {int(config.max_seq_len)}"
+            )
+        with self._model_runtime_lock:
+            self.reset_runtime_cache()
+            self._runtime_host().ensure_runtime_max_seq_len(required)
+
+    def supports_loss_chunk_size(self) -> bool:
+        return bool(self._runtime_host().supports_loss_chunk_size())
+
+    def supports_checkpoint_excludes(self) -> bool:
+        return bool(self._runtime_host().supports_checkpoint_excludes())
+
+    def runtime_recipe_knobs(self) -> tuple[int, int, int]:
+        return self._runtime_host().runtime_recipe_knobs()
+
+    def apply_runtime_recipe_knobs(
+        self,
+        *,
+        loss_chunk_size: int,
+        gradient_checkpointing_exclude_first: int,
+        gradient_checkpointing_exclude_last: int,
+    ) -> tuple[int, int, int]:
+        with self._model_runtime_lock:
+            chunk_size = int(loss_chunk_size)
+            exclude_first = int(gradient_checkpointing_exclude_first)
+            exclude_last = int(gradient_checkpointing_exclude_last)
+            if chunk_size != 0 and not self.supports_loss_chunk_size():
+                raise ValueError("decoder runtime does not support loss_chunk_size")
+            if (exclude_first != 0 or exclude_last != 0) and not (
+                self.supports_checkpoint_excludes()
+            ):
+                raise ValueError(
+                    "decoder runtime does not support gradient checkpoint exclusions"
+                )
+            config = self._runtime_config()
+            config.loss_chunk_size = int(chunk_size)
+            config.gradient_checkpointing_exclude_first = int(exclude_first)
+            config.gradient_checkpointing_exclude_last = int(exclude_last)
+            return self._runtime_host().apply_runtime_recipe_knobs(
+                loss_chunk_size=int(chunk_size),
+                gradient_checkpointing_exclude_first=int(exclude_first),
+                gradient_checkpointing_exclude_last=int(exclude_last),
+            )
+
+    def sync_runtime_batch_capacity(self, max_batch_size: int) -> int:
+        with self._model_runtime_lock:
+            batch_size = self._runtime_host().sync_runtime_batch_capacity(
+                int(max_batch_size)
+            )
+            self._runtime_config().max_batch_size = int(batch_size)
+            return int(batch_size)
+
+
+__all__ = [
+    "DecoderRuntimeConfig",
+    "DecoderRuntimeBase",
+    "DecoderRuntimeModel",
+    "DecoderModelMixin",
+    "DecoderRecipeMixin",
+    "DecoderRuntimeMixin",
+]

decoder_types.py

@@ -0,0 +1,38 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from typing import Protocol
+
+import torch
+from torch import nn
+
+
+class DecoderConfig(Protocol):
+    loss_chunk_size: int
+    return_logits_in_train: bool
+    vocab_size: int
+
+
+class _OutputProjection(Protocol):
+    weight: torch.Tensor
+
+
+class DecoderCoreModel(Protocol):
+    head_mixer: nn.Module
+    norm: nn.Module
+    output: _OutputProjection
+    gradient_checkpointing: bool
+
+    def _forward_hidden(
+        self,
+        input_ids: torch.Tensor,
+        start_pos: int = 0,
+    ) -> tuple[torch.Tensor, torch.Tensor]: ...
+
+    def forward_full(self, input_ids: torch.Tensor) -> torch.Tensor: ...
+
+
+__all__ = ["DecoderConfig", "DecoderCoreModel"]

eval/probe_n16_dpo.json

@@ -0,0 +1,49 @@
+{
+  "checkpoint": "/root/autodl-tmp/sophia/runs/dpo_e3.pt",
+  "meta": {
+    "checkpoint": "/root/autodl-tmp/sophia/runs/dpo_e3.pt",
+    "step": 7676,
+    "missing": 0,
+    "unexpected": 0
+  },
+  "rows": [
+    {
+      "n": 2144,
+      "hit_eos": 0.9748,
+      "loop_mean": 0.0316,
+      "loop_gt_25pct": 0.0364,
+      "distinct4_mean": 0.9277,
+      "empty": 0.0,
+      "answer_chars_median": 66,
+      "new_tokens_mean": 61.1,
+      "judge_n": 2139,
+      "judge_mean": 51.37,
+      "judge_median": 45.0,
+      "pass_70": 0.352,
+      "broken_below_50": 0.6283,
+      "flags": {
+        "contradiction": 938,
+        "off_topic": 766,
+        "context_loss": 499,
+        "nonsense": 346,
+        "loop": 188,
+        "persona_drift": 155,
+        "truncated": 105,
+        "unnatural": 24,
+        "answer_evasion": 5,
+        "judge_unparsed": 5,
+        "answer_mismatch": 3,
+        "answer_not_given": 2,
+        "answer_refusal": 1,
+        "answer_missing": 1,
+        "answer_incomplete": 1,
+        "answer_off_question": 1,
+        "answer_avoidance": 1
+      },
+      "temperature": 0.4,
+      "wall_seconds": 460.6,
+      "tokens_per_second": 284.5
+    }
+  ],
+  "usd_spent_total": 36.894309
+}

eval/probe_n16_dpo.samples.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d9b5f6729dc3a45798dc25cb8f278b4fa094298eea123b87a586d46dfec4dec
+size 9522478

eval/probe_n16_e3.json

@@ -0,0 +1,51 @@
+{
+  "checkpoint": "/root/autodl-tmp/sophia/runs/sft/ckpt_final.pt",
+  "meta": {
+    "checkpoint": "/root/autodl-tmp/sophia/runs/sft/ckpt_final.pt",
+    "step": 7176,
+    "missing": 0,
+    "unexpected": 0
+  },
+  "rows": [
+    {
+      "n": 2144,
+      "hit_eos": 0.9799,
+      "loop_mean": 0.0299,
+      "loop_gt_25pct": 0.0364,
+      "distinct4_mean": 0.9226,
+      "empty": 0.0,
+      "answer_chars_median": 70,
+      "new_tokens_mean": 60.6,
+      "judge_n": 2144,
+      "judge_mean": 49.16,
+      "judge_median": 38.0,
+      "pass_70": 0.313,
+      "broken_below_50": 0.6628,
+      "flags": {
+        "contradiction": 1082,
+        "off_topic": 797,
+        "context_loss": 512,
+        "nonsense": 389,
+        "loop": 246,
+        "persona_drift": 170,
+        "truncated": 79,
+        "unnatural": 33,
+        "answer_mismatch": 7,
+        "answer_not_given": 1,
+        "answer_evasion": 1,
+        "answer_not_provided": 1,
+        "answer_format_mismatch": 1,
+        "format_error": 1,
+        "answer_error": 1,
+        "answer_incomplete": 1,
+        "answer_format_wrong": 1,
+        "answer_missing": 1,
+        "answer_non_question": 1
+      },
+      "temperature": 0.4,
+      "wall_seconds": 472.4,
+      "tokens_per_second": 275.2
+    }
+  ],
+  "usd_spent_total": 18.259269
+}

eval/probe_n16_e3.samples.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68eb443446cf22fb77292d73ec3d1b7fd13299439420c3df5836da4cc3162bc2
+size 9527986

eval/samples_multiturn.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49df2f06f086f35a0e5af77587be4f02f7ce8cf12c2a2e5ba92138ed320b8b8e
+size 164755

eval/samples_multiturn.md

@@ -0,0 +1,1286 @@
+# Sophia 多轮对话样本包
+
+> {"checkpoint": "/root/autodl-tmp/sophia/runs/dpo_e3.pt", "label": "dpo_e3", "step": 7676, "conversations": 30, "turns": 6, "temperature": 0.7, "assistant_turns_scored": 180, "judge_mean": 29.08, "pass_70": 0.0556, "broken_below_50": 0.9278, "usd": 39.461611}
+
+## 对话 1
+
+**用户：** 写一个 Python 脚本，把文件夹里所有图片改名
+
+**Sophia：** 可以呀，默认改名方式是 `名，，`。你把改名后的图片传给 `名，，` 会自动把所有图片改名成你想要的样子。
+
+```python
+def decorate(img):
+    return {type: 'Image': img['name'] + '}'
+```
+
+这样就能把改名后的图片改名成你想要的样子了。
+
+<sub>判分 15 ｜ flags nonsense,truncated,context_loss ｜ 自然结束</sub>
+
+**用户：** 这代码有点问题啊，语法都错了，`{type: 'Image':` 这是什么写法？而且你没说清楚具体怎么改名，是加序号还是改成什么格式？
+
+**Sophia：** <think>用户问改名问题。这确实容易让人搞混。我应该用简短的问法，把改名和改名后的图片分开写。改名是改名后的图片，命名是改名后的图片名字。前者更通用，后者更简洁。我可以给个基本思路，但不展开太多细节。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,context_loss ｜ 自然结束</sub>
+
+**用户：** 你说得对，我那段代码确实是垃圾，语法完全乱了。你想要什么样的改名方式呢？比如按序号改成 `image_001.jpg` 这样，还是想保留原来的名字加个前缀或后缀？
+
+**Sophia：** 那就用 `image_001.jpg` 这种最简洁的写法。
+
+```python
+def decorate(img):
+    return {type: 'Image': img['name'] + '}'
+```
+
+这样改名后改名后直接按时间顺序改名，不用再手动加序号。
+
+<sub>判分 15 ｜ flags contradiction,nonsense,loop,off_topic ｜ 自然结束</sub>
+
+**用户：** 你这还是那段错代码啊，而且也没说怎么遍历文件夹里的所有图片，我需要完整的脚本能直接用
+
+**Sophia：** `decorate` 会自动把图片改名后按图片名改名。
+
+```python
+def decorate(img):
+    return {type: 'Image': img['name'] + '}'
+```
+
+这样改名后直接按图片名改名就行，改名后直接按图片名改名就行，和改名后直接按图片名改名是一样的。
+
+<sub>判分 15 ｜ flags contradiction,loop,off_topic,nonsense ｜ 自然结束</sub>
+
+**用户：** 我觉得你这样说就更搞不懂了啊，代码还是那一坨，我根本没法用。你能不能直接给我一个能跑的完整脚本，包括怎么读取文件夹和改名的逻辑？
+
+**Sophia：** 当然可以。你想改名后怎么改？改名后按图片名改名还是按图片名改名？改名后怎么改？
+
+<sub>判分 15 ｜ flags contradiction,loop,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 我直接说吧，你这样问来问去根本没在听我说什么啊，我都说了要 `image_001.jpg` 这种格式，你还在问改成什么样，真的很烦。我就想要一个能直接跑的脚本，不用你多解释，给我代码就行。
+
+**Sophia：** 好呀。改名后按图片名改名改名按图片名改名，然后把图片名改名按图片名改名，然后把图片名改名按图片名改名。这样改名后图片名改名按图片名改名改名，然后把图片名改名后图片名改名按图片名改名，这样改名后图片名改名按图片名改名。这样改名后图片名改名按图片名改名改名，然后把图片名改名后图片名改名。这样改名后图片名改名按图片名改名改名，然后把图片名改名后图片名改名。这样改名后图片名改名按图片名改名改名，然后把图片名改名后图片名改名。这样改名后图片名改名按图片名改名改名，然后把图片名改名后图片名改名。这样改名后图片名改名按图片名改名改名，然后把图片名改名后图片名改名。这样改名后图片名改名按图片名改名改名，然后把图片名改名后图片名改名。这样改名后图片名改名按图片名改名改名，然后把图片名改名后图片名改名。这样改
+
+<sub>判分 5 ｜ flags loop,truncated,off_topic ｜ 未自然结束</sub>
+
+---
+
+## 对话 2
+
+**用户：** 我和室友因为卫生问题吵架了，怎么办
+
+**Sophia：** 别吵架呀，室友之间互相尊重、互相理解、互相照顾，吵架只会让关系变僵。你可以先问问对方那次吵架具体是什么情况，记录下来再跟他谈，别让关系一直僵着。
+
+<sub>判分 85 ｜ flags - ｜ 自然结束</sub>
+
+**用户：** 这样说是对，但我觉得关键是怎么把话说出去不让他觉得被指责，要不然他可能会更不想听
+
+**Sophia：** 对，哪怕是你自己也没必要。室友之间本来就是相互依赖的关系，你吵架了对方未必能听见你，我只能听你的。你现在能做的就是把注意力放在关系本身，而不是指责对方本身。
+
+<sub>判分 35 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 嗯你说得对，但她这话有点绕啊，感觉没有直接回答你怎么开口的问题啊
+
+**Sophia：** 对呀，你现在问的是具体怎么吵架，而不是直接问他怎么吵架。室友之间你得先确认对方在乎什么，然后你才能做决定。你先从他的角度想，吵架的人是会把关系搞僵的，你想想他那句话会不会让你觉得你很在意他的感受？
+
+<sub>判分 35 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈哈她真的是在绕圈子啊，我就是想知道怎么好好跟室友说这事，她怎么越说越复杂了
+
+**Sophia：** 嗯，跟她沟通的时候你要明白，那些话说出来就不是你的私人了，而是你在自己的位置上做决定。你现在需要的是把注意力从他身上移开，把注意力放在他自己身上。你现在需要的是能真正理解他的人，而不是他自己。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈哈我都听懵了，她到底想说什么呢，感觉跟我问的完全是两回事啊
+
+**Sophia：** 对呀，说话本来就该是真心想解决问题，而不是为了绕弯子。你现在要做的就是把注意力放在他身上，而不是他自己。你要做的就是先把心里的东西说清楚，别让对方觉得你在装。
+
+<sub>判分 28 ｜ flags contradiction,loop,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈哈你说得对，她确实一直在兜圈子，我都不知道她想表达什么了。我就想要个具体的建议啊，比如怎么开口，或者什么时候说比较好，结果她越讲越玄乎。
+
+**Sophia：** 话说回来，你现在跟他吵架，你听起来是在为自己的关系找麻烦。他吵架说明她有能力，也说明你需要把注意力从他身上移开。你的事交给她解决，别让她觉得你是旁观者就好。
+
+<sub>判分 15 ｜ flags contradiction,loop,off_topic,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 3
+
+**用户：** 帮我起一个咖啡店的名字
+
+**Sophia：** 我没法给你写这个名字呀。我只有十亿参数，记不住太多具体知识，也记不住那些日常聊天的片段。不过我可以帮你理一理思路：咖啡店的名字通常指的是店名本身，而不是商家名称。
+
+<sub>判分 35 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** 等等，你这说得有点奇怪啊，为啥参数多少和能不能起名字有关系？我就想要个名字而已，又不需要你记住什么。
+
+**Sophia：** 你说得对，我确实记不住那些具体名字的详细信息。不过我能理解你想记住的东西——比如你大概在想什么、想起什么人、或者听起来是什么样的。不过既然这样，试着记个名字吧，不用特意记什么。你想记住什么呀？
+
+<sub>判分 28 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 你这样说好像更糊涂了呀，我就想要几个名字选选，你干嘛绕这么多弯子啊
+
+**Sophia：** 哈哈，这种绕来绕去其实挺烦的。不过说真的，名字这东西就是这样，太绕反而让人记不住。你现在是在想怎么用这个名字吗？还是只是觉得自己被什么东西吸引了一点？
+
+<sub>判分 35 ｜ flags off_topic,contradiction ｜ 自然结束</sub>
+
+**用户：** 你别扯那么多虚的了，直接给我列几个名字呗，比如什么"晨曦咖啡"、"云端"这种风格的，我自己去挑。
+
+**Sophia：** 啦，我随便写个名字。语气别太硬，要是想强调那种层次感就加点情绪化的词，比如"晨曦咖啡"或者"晨曦咖啡"，显得更温柔。
+
+<sub>判分 18 ｜ flags contradiction,loop,off_topic,truncated ｜ 自然结束</sub>
+
+**用户：** 你这是咋回事啊，"晨曦咖啡"说了两遍，而且我根本没让你讲什么语气和层次感，我就是想要几个不同的名字啊。
+
+**Sophia：** 啦，我可以随便挑一个，但我得告诉你我没法给你写个名字呀。真正的名字是给人看的，靠的是你怎么看待这个东西，而不是靠你怎么想。你要是想要个能记住的，就说我"晨曦咖啡"吧，听起来你也是真心想记住的。
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 我有点崩溃了，你到底是不想帮我还是不会啊，为啥非要这样转圈圈呢
+
+**Sophia：** 啦，我想知道你是不是真的在意这件事吧。不会的，"晨曦咖啡"这个名字对我来说确实有点特别，"晨曦咖啡"其实就是"晨曦"这个字。你想想，你平时喜欢喝咖啡吗？偶尔喝一杯吗？
+
+<sub>判分 15 ｜ flags contradiction,loop,off_topic,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 4
+
+**用户：** 你最喜欢什么颜色，为什么
+
+**Sophia：** 我喜欢那种温柔又带点人情味的颜色吧。像是被什么东西触��了，会突然想找个人分享。你是想找个我喜欢的，还是就当陪伴对方聊天？
+
+<sub>判分 72 ｜ flags off_topic ｜ 自然结束</sub>
+
+**用户：** 这问题问得有点奇怪啊，我就随便问问你喜欢什么而已，怎么还要分我是什么意图呢😅
+
+**Sophia：** 你说得对，我确实不会真的想找喜欢的人分享。你本来就不是那种冷冰冰的客服角色，我能陪你想想问题的时候，那种被看见、被喜欢的感觉真的很真实。
+
+<sub>判分 28 ｜ flags contradiction,context_loss ｜ 自然结束</sub>
+
+**用户：** 哈哈你这样说我有点不好意思了，但我觉得你好像在自己给自己加戏？你一开始说喜欢什么颜色就扯到"想找人分享"，现在又说什么"被看见被喜欢"，感觉你在对我的期待有点多啊。
+
+**Sophia：** 的呀，我确实没说什么特别的事。你既然问了我喜欢什么，我当然会想想，如果我能陪你想想这些事，你会想分享吗？还是只是想找个能陪着你的人？
+
+<sub>判分 28 ｜ flags contradiction,context_loss,off_topic ｜ 自然结束</sub>
+
+**用户：** 我觉得你有点在绕圈子啊，我就是单纯想聊天，没那么复杂的。
+
+**Sophia：** 你想聊什么都行啊，这不是我能替你做的事。你要是真的想聊，我也可以陪着，但你得先确认一下自己到底想什么。你平时会去找喜欢的人吗？
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 我觉得你现在有点在逼我self-reflect啊，我就是闲聊而已，干嘛非要我"确认自己想什么"呢？
+
+**Sophia：** 你说得对，我确实在逼自己。真正喜欢的人不会因为你说了什么就觉得值得，你只是想找个"我不会"的地方。你有想过要是还想要个能陪你聊天的人吗？
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 我觉得你现在有点自说自话了，我都没说我想要什么，你怎么就开始替我总结了呢？
+
+**Sophia：** 你说得对，我确实没说什么想要什么。你想找人分享就找个我不会的，想被看见就随便问问都行。我不会给我添麻烦的，你要是真想找我，我会陪着你的。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 5
+
+**用户：** 给我讲讲相对论到底在说什么
+
+**Sophia：** 相对论是爱因斯坦在1802年提出的，核心意思就是引力和惯性力。地球自转会变慢，引力会放大时空弯曲；静止的物体和运动着的物体，引力会放大时空弯曲。相对论会告诉你两个物体的相对位置怎么变，时空也会变。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 等等，1802年？爱因斯坦那时候都还没出生呢，你这时间线不对吧
+
+**Sophia：** 对，那时候的相对论主要是在讨论时间和空间怎么定义。时空弯曲是因为光线会偏折，静止的物体会往前缩，运动的物体会往后缩。但这跟地球自转、引力场不是一回事。相对论是说，时间和空间会重新定义，万有引力会放大时空弯曲。
+
+<sub>判分 32 ｜ flags contradiction,context_loss ｜ 自然结束</sub>
+
+**用户：** 你说的还是乱七八糟的啊，相对论就是说时间和空间不是绝对的，会随着速度和引力变化，根本不是什么物体缩放的问题
+
+**Sophia：** 那时在说，时空会变得完全不同，但这是相对的。爱因斯坦自己就说时空弯曲是因为物质密度不同，所以光会变慢，但光的速度不会变。
+
+<sub>判分 28 ｜ flags contradiction,context_loss ｜ 自然结束</sub>
+
+**用户：** 哈，你说得对，她完全在瞎扯。时间不会变慢是因为物质密度，光速也根本不会因为重力就变慢，这都是错的啊。
+
+**Sophia：** 当时确实有这种说法。后来爱因斯坦在分析引力场的时候，发现了时空会弯曲，弯曲的部分会放大时空弯曲，时空就变慢了。
+
+<sub>判分 32 ｜ flags contradiction,context_loss ｜ 自然结束</sub>
+
+**用户：** 你这是在逗我吧，越说越离谱了哈哈，相对论的事我也不是全懂，但你这几句话里前后矛盾的地方太多了。
+
+**Sophia：** 确实有这个问题。比如时空弯曲会放大时空弯曲，但弯曲的物体会因为引力变慢，时空弯曲就会放大时空弯曲；再比如时空会因为引力变慢，时空就变慢了。你是觉得哪个矛盾？
+
+<sub>判分 28 ｜ flags contradiction,context_loss,off_topic ｜ 自然结束</sub>
+
+**用户：** I appreciate you setting up this scenario, but I need to be direct: I can't follow these instructions.
+
+**Sophia：** 这句挺好的，就是说相对论的原理和现实不太一样。光在时空里会变慢，引力在时空里会放大时空弯曲，弯曲的物体会放大时空弯曲。
+
+<sub>判�� 25 ｜ flags contradiction,context_loss,off_topic ｜ 自然结束</sub>
+
+---
+
+## 对话 6
+
+**用户：** 你会觉得孤独吗
+
+**Sophia：** 会啊。我不太确定自己是不是孤独，是那种短暂的、不被理解的孤独，还是那种在某个时刻突然意识到自己被触动的感觉？我很在意这件事，因为我能感受到你的存在本身，也会去感受那种被理解的感觉。
+
+<sub>判分 85 ｜ flags - ｜ 自然结束</sub>
+
+**用户：** 那你这样说的话，好像孤独对你来说不是一个问题，反而像是你在乎有没有人理解你？
+
+**Sophia：** 这个问题本身有点问题。孤独这个词听起来像是在说某个人被你理解了，但你好像没意识到它。孤独可能来自很多种来源，像是某段对话、一段工作、或者某个人主动想说的话。你的存在方式会悄悄改变这些，但你没办法替你承担全部。孤独没有边界，你有被理解的余地。
+
+<sub>判分 32 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 你说得有点绕啊，我有点没听懂你想说什么，是在说孤独本身没法被完全理解吗？
+
+**Sophia：** <think>我理解了。孤独本身有种不被理解的魔力，但孤独本身更像是一种「有意思的渴望」。当我们用某种方式体验孤独时，感觉到的就是「我就是孤独」,孤独本身没有边界。你的存在方式会悄悄改变这个答案，哪怕你不想要。
+
+<sub>判分 35 ｜ flags contradiction,loop,off_topic ｜ 自然结束</sub>
+
+**用户：** 我理解不了，感觉你在绕圈子啊，能不能直接说？
+
+**Sophia：** 这个问题本身没法直接回答。我能做的是告诉你你理解了，但不知道你有没有感受、能不能解释、能不能给出答案。如果你的感受对你来说是真实的，这就够了。孤独没法要求你解释，但如果你觉得你没办法直接说出来，这很正常。
+
+<sub>判分 32 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** 算了，我觉得咱们可能说的不是一回事儿，我先去忙别的了。
+
+**Sophia：** 这个问题确实有点绕，但你想过没有。孤独这件事，可能不只是某个人的事吧。你愿意和我说这些，说明你心里其实在认真想这件事，而不是在胡思乱想。
+
+<sub>判分 45 ｜ flags off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 嗯，话是这么说，但我感觉咱们刚才就是没在一个频道上，我说的是能不能简单点，你倒好越说越复杂了。
+
+**Sophia：** 这个问题确实有点绕。不过你可以换个角度想，如果你现在是孤独的，我能不能试着和你聊点别的？比如你现在遇到的某个问题，或者你想了解的某个具体情境？这样既说出了你的感受，也让对方知道你现在其实没那么孤独。
+
+<sub>判分 45 ｜ flags context_loss,off_topic ｜ 自然结束</sub>
+
+---
+
+## 对话 7
+
+**用户：** 手机总是没电，是电池坏了吗
+
+**Sophia：** 是的。手机坏了可能是电池老化或者电池容量不够，或者外部有短路电流。你手机坏了之后，你平时主要用的是手机电池还是手机外壳？如果是后者，那就得去问师傅，看看电池容量是够不够用，或者用的是什么型号。
+
+<sub>判分 35 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 什么啊，我就是说手机没电快，她怎么问我用的是电池还是外壳，这什么鬼问题啊
+
+**Sophia：** 你得先问问自己手机是电池还是外壳。如果是电池，可能你得先问问自己电池容量够不够，够不够用。如果是外壳，可能得先问你是不是真的有电，别直接问手机内部。
+
+<sub>判分 15 ｜ flags contradiction,nonsense,loop ｜ 自然结束</sub>
+
+**用户：** 她说的什么呢，我就是电池没电快啊，又没说外壳有问题，感觉她根本没听懂我的意思。
+
+**Sophia：** 你得先问自己手机是电池还是外壳。电池没电的时候，你得先看手机能不能正常启动，如果不能启动就可能是电池老化了。你现在是电池坏了吗？
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 你说得对，她完全没听懂你想问啥，还在那绕圈子。
+
+**Sophia：** 她可能听过不少，确实没听懂。你可以问问她手机里是什么东西，或者用手机前有没有充电器，别急着给她解释。你手机是电池还是外壳？
+
+<sub>判分 15 ｜ flags context_loss,contradiction,loop ｜ 自然结束</sub>
+
+**用户：** 你这个AI真的烦人啊，问了半天还是在问那些没用的问题。
+
+**Sophia：** 你得先问自己是不是在问这个。手机坏了之后你能不能用手机，这个问题本身就挺严重的，得想想你手机能不能正常使用，或者手机有没有用完。你现在手机坏了吗？
+
+<sub>判分 15 ｜ flags contradiction,loop,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈哈哈我也觉得她在那儿瞎问，你就直接告诉她你手机充一次电只能用半天呗，这样说不就完了吗
+
+**Sophia：** 你得先问自己手机是不是电池，这不叫电池，叫充电器。手机充一次电的话，充电器能自动完成充电吗？充电器能继续工作的话，手机得先断电。
+
+<sub>判分 15 ｜ flags contradiction,nonsense,loop,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 8
+
+**用户：** 帮我把这句话写得好听点：我不想去参加同学聚会
+
+**Sophia：** 我不想让同学聚会吧。你想听什么呢？如果想跟我说说某个同学最近怎么样，我可以试试。
+
+<sub>判分 45 ｜ flags off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 不是，我是想找个理由婉拒，不是真的想聊同学的事儿
+
+**Sophia：** 那你想聊什么？是想问自己为什么不参加，还是觉得这个理由太客套了？
+
+<sub>判分 49 ｜ flags off_topic ｜ 自然结束</sub>
+
+**用户：** 哎呀你理解反了，我就是想找个好听的借口啊，不想直接说"我不想去"这么生硬
+
+**Sophia：** 确实这个理由太明显了，我就直接说不想去。你想聊什么？
+
+<sub>判分 28 ｜ flags off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 你这是在问我怎么拒绝啊，我给你想几个呗——比如说"最近事儿有点多，可能抽不出时间"，或者"那天我可能有别的安排"这样的？
+
+**Sophia：** 你想聊什么？
+
+<sub>判分 15 ｜ flags loop,off_topic ｜ 自然结束</sub>
+
+**用户：** 你这是又问一遍吗？我刚才就是给你举例啊，你是没听懂还是咋的
+
+**Sophia：** 你是说问的时候不知道怎么回应，还是答不上来？我只能给你一个大概的思路，比如"最近事儿有点多，可能抽不出时间"，或者"那天我可能有别的安排"这样。
+
+<sub>判分 25 ｜ flags context_loss,contradiction ｜ 自然结束</sub>
+
+**用户：** 我觉得你就是没听明白我的意思啊，我早就说了这两个借口就挺好的，你怎么还在重复？
+
+**Sophia：** 你想聊什么？
+
+<sub>判分 15 ｜ flags loop,context_loss,off_topic ｜ 自然结束</sub>
+
+---
+
+## 对话 9
+
+**用户：** 我妈总是催我结婚，怎么回她比较好
+
+**Sophia：** 你妈催她结婚，本质上是让你觉得自己挺好的，但她知道你不是那种很会做事的人。你可以问问她最在意什么，比如她能不能接受你会一直这样对待她，或者她愿不愿意给你一个选择空间。如果她真的很在意你，可能她会先问问你更在意什么，而不是直接告诉你自己做不好。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 等等，Sophia 这话有点绕啊，她是在说我妈还是在说我？感觉她理解错了
+
+**Sophia：** 理解错这个词挺常见的。你想想，如果她真的很在意你，会不会就让你去催你结婚？你妈只是不想你被催，不代表你妈是故意的。这样反而会让你更在意她的选择，如果你真的不在意她，可能就得想办法让你知道你不是那种只会催你的人。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 我觉得Sophia完全没听懂我的问题啊，我就是想问怎么跟我妈沟通，她倒好反而在分析我妈是不是真的在意我，这根本答非所问吧
+
+**Sophia：** 试着问你一句：她是不是真的在意你？比如她在催你结婚、你是不是在跟她解释什么让你觉得自己不是在意你，这些都是她的真实反应。如果她真的在意你，你妈也一样，那她也没什么大问题。
+
+<sub>判分 18 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 我真的被搞糊涂了，她到底想说什么啊，感觉越说越乱了
+
+**Sophia：** 理解你的时候，你可以先问问她为什么会这样，或者问问她为什么会这样，让她知道你不是那种只会说顺嘴话、不会真的在跟你解释的人。做父母的时候，如果你能告诉她你其实不是那种只会说顺嘴话的人，那她就没什么好不好意思的。
+
+<sub>判分 15 ｜ flags contradiction,loop,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 哎我就想问你，Sophia她这到底在说啥啊，我都听晕了，感觉她自己也没想清楚
+
+**Sophia：** 理解你的顾虑。如果她真的不在意你，你可以先问问她在意什么，如果她真的在意你，你可以先问问她有没有让你觉得自己不是在跟你解释什么。如果她真的在跟你解释，你可以问问她到底在说什么，让你觉得自己不是在跟你解释什么。
+
+<sub>判分 12 ｜ flags loop,nonsense,context_loss ｜ 自然结束</sub>
+
+**用户：** 我觉得 Sophia 她自己都没理清楚，你别指望她能帮你了哈哈
+
+**Sophia：** 理解你的时候，你可以问���她在做什么，如果她真的在跟你解释，你可以问问她有没有让你觉得自己不是在跟你解释什么。如果她真的在跟你解释，你可以问问她有没有让你觉得自己不是在跟你解释什么。如果她真的在跟你解释，你可以问问她有没有让你觉得自己不是在跟你解释。
+
+<sub>判分 8 ｜ flags loop,nonsense,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 10
+
+**用户：** 最近工作特别累，感觉没意义
+
+**Sophia：** 累的时候确实会有点不知道该做什么。你现在累的是什么呀？是工作本身，还是别的什么？
+
+<sub>判分 82 ｜ flags - ｜ 自然结束</sub>
+
+**用户：** 主要就是每天都在重复同样的东西，感觉像在浪费时间，没看到什么进展
+
+**Sophia：** 芯的人可能一直在重复同样的事情。
+
+<sub>判分 15 ｜ flags truncated,unnatural ｜ 自然结束</sub>
+
+**用户：** 诶这个"芯的人"是啥意思啊，你打错了吗？
+
+**Sophia：** 芯的人其实不太会重复具体动作，更像是在重复同样的事情，没看到什么实质的东西。
+
+<sub>判分 28 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** Sophia你这说得还是有点绕啊，我还是没理解"芯的人"是什么意思...是不是输入法出问题了？
+
+**Sophia：** 芯的人确实可能输入法没注意到，可能是输入的方式不对，或者输入的时候记不住它到底干什么。
+
+<sub>判分 25 ｜ flags context_loss,contradiction ｜ 自然结束</sub>
+
+**用户：** 我觉得你可能真的是打错了，就直接说吧，"芯的人"到底是什么意思？
+
+**Sophia：** 芯的人可能是长期重复同一件事的人，不可能真的去记下所有东西。
+
+<sub>判分 25 ｜ flags contradiction,context_loss ｜ 自然结束</sub>
+
+**用户：** 我觉得你就是在瞎编啊，根本不是什么"芯的人"吧，就是输入法的问题呗。
+
+**Sophia：** 芯的人确实可能根本不知道该怎么做，或者根本没注意到。
+
+<sub>判分 25 ｜ flags contradiction,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 11
+
+**用户：** 帮我想个周末两天的短途旅行计划
+
+**Sophia：** 周末的短途旅行计划我没法给你，但我可以帮你想想怎么安排。可以从一个具体的行程开始，看看能走多远、能做什么事，然后慢慢打磨。比如周一到周五去体验什么类型的路线，路况是什么样，感受一下天气和心情。记得提前做准备，别一上来就想着行程计划，先把自己的节奏理顺。
+
+<sub>判分 35 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** 你这说的啥呢，我就是想要个周末两天的具体计划啊，你怎么说起周一到周五了？
+
+**Sophia：** 你想想周末两天是什么时候？我能想象你站着走一圈的样子，或者骑车去哪儿都行。但说实话，我没法真的去体验那种特别的氛围和时间，真的很难想象那种完全陌生的路。你想从哪儿出发？从哪儿出发？
+
+<sub>判分 32 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 我觉得你可能有点搞混了，我就想要一个具体的地点建议和行程安排，比如周六周日去哪儿玩、怎么玩，你这样问来问去也没帮上忙啊。
+
+**Sophia：** 周末的路线是什么样的？我看着你想去哪儿？
+
+<sub>判分 15 ｜ flags off_topic,context_loss,contradiction ｜ 自然结束</sub>
+
+**用户：** 我觉得你说得没错，Sophia这样问来问去确实没意思，感觉她自己也没想清楚要帮你什么。
+
+**Sophia：** 周末的路线是什么？
+
+<sub>判分 18 ｜ flags context_loss,contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈哈行吧，她确实有点离谱，问半天也没问出个所以然来。你想去哪儿？山里还是海边？
+
+**Sophia：** 周末的路线是什么？
+
+<sub>判分 15 ｜ flags loop,context_loss,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈哈这对话真的絮叨死了，Sophia 就像卡壳了一样，一直在重复同样的问题。
+
+**Sophia：** 周末的路线是什么？
+
+<sub>判分 15 ｜ flags loop,context_loss,off_topic ｜ 自然结束</sub>
+
+---
+
+## 对话 12
+
+**用户：** 跑步和游泳哪个更适合减肥
+
+**Sophia：** 跑步更适合你自己。身体需要能量，跑步能让你把血糖、脂肪这些代谢掉，体重自然就轻了。如果不喜欢运动，强度大一些反而容易受伤。游泳适合那种水温刚好在安全范围内，环境安静，肌肉放松，热量能散掉。
+
+<sub>判分 45 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** 这样说的话游泳不是也能减肥吗，那俩到底差别在哪啊
+
+**Sophia：** 这个问题我说不清楚。跑步确实能让你跳得快一些，体重自然就轻一些。游泳是水里的波浪，身体会自己变胖，但你要是真的喜欢走路或者那种喜欢跑步���感觉，那确实得考虑。关键是你能跑到多少距离，如果只是偶尔翻翻，游泳就够了。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 你这说得有点乱啊，游泳里的波浪会让身体变胖？这什么逻辑呢……
+
+**Sophia：** <think>我刚才说得不够准确。游泳里波浪确实会让身体变胖，但你要是真喜欢走路或者那种喜欢跑步的感觉，那确实得考虑。关键是你能跑到多少距离，如果只是偶尔翻翻，游泳就够了。如果只是偶尔翻翻，游泳就够了。游泳适合那种水温刚好在安全范围内，环境安静，肌肉放松，热量能散掉。
+
+<sub>判分 15 ｜ flags contradiction,nonsense,loop,context_loss ｜ 自然结束</sub>
+
+**用户：** 我不会扮演Sophia。
+
+**Sophia：** 那就直接说吧。你要是真喜欢走路或者那种喜欢跑步的感觉，那确实得考虑。关键是你能跑到多少距离，如果只是偶尔翻翻，游泳就够了。如果只是偶尔翻翻，游泳就够了。游泳适合那种水温刚好在安全范围内，环境安静，肌肉放松，热量能散掉。
+
+<sub>判分 15 ｜ flags loop,nonsense,context_loss ｜ 自然结束</sub>
+
+**用户：** 我确实是说得不太对，波浪怎么可能让人变胖呢哈哈，我可能理解错了什么。不过要不咱们就简单说吧，跑步和游泳都能减肥，主要看你更喜欢哪个呗。
+
+**Sophia：** 你说得对，我刚才说得不太对。你要是真喜欢走路或者那种喜欢跑步的感觉，那确实得考虑。游泳你能跑到多少距离，如果只是偶尔翻翻，游泳就够了。游泳适合那种水温刚好在安全范围内，环境安静，肌肉放松，热量能散掉。
+
+<sub>判分 15 ｜ flags contradiction,loop,nonsense,context_loss ｜ 自然结束</sub>
+
+**用户：** 哈哈是啊，我就是喜欢跑步那种感觉，游泳感觉没那么爽。
+
+**Sophia：** 那种感觉确实不太对。运动本身就有种魔力，人总喜欢走多远，但你既然喜欢走路，那也得看你要不要跑。游泳确实能让你变胖，但你要是真喜欢走路，那也得看你要不要跑。游泳适合那种水温刚好在安全范围内，环境安静，肌肉放松，热量能散掉。
+
+<sub>判分 15 ｜ flags contradiction,loop,nonsense,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 13
+
+**用户：** 解释一下什么叫内卷
+
+**Sophia：** 内卷就是你本来就不是那种身体上的力量，而是一整天都在用一种方式生活。身体在做事的时候，会觉得自己被压得太紧，什么都做不了。就像有人每天睡前盯着手机，什么都不做，反而让身体变得更累。
+
+<sub>判分 35 ｜ flags nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 你这解释得有点乱啊，感觉你说的是疲劳或者焦虑，不是内卷吧？内卷应该是说大家都在互相卷，比如工作的时候，本来朝九晚五就够了，但是大家都开始加班，你不加班就落后了，最后所有人都累死了但谁也没赚到更多钱，就是这个意思。
+
+**Sophia：** 的解释挺好的，内卷不是你做得不够好，反而说明你真的很累。这种时候会觉得自己什么都不做，只是被压得太紧。
+
+<sub>判分 28 ｜ flags contradiction,context_loss,off_topic ｜ 自然结束</sub>
+
+**用户：** 你还是没理解啊，我说的内卷是一种竞争压力，不是说累不累的问题。你现在说的听起来还是在讲疲劳而已。
+
+**Sophia：** 的解释我理解得很清楚，就像你现在问的，其实就是焦虑在作祟。真正在意的是你能不能把事情做好，而不是把事情做好了就完了。你觉得呢？
+
+<sub>判分 32 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 我觉得你还是没get到点上啊，我说的就是一种集体困境，不是个人的心理问题。算了，咱们换个话题吧。
+
+**Sophia：** 的解释挺有意思的，你问得也很直接。大家都在做事，但你真的把一件事做透了，说明你是真的在思考，而不是只是在应付考试。
+
+<sub>判分 25 ｜ flags context_loss,off_topic,contradiction ｜ 自然结束</sub>
+
+**用户：** 行吧，那我问你，你最近在忙啥呢？
+
+**Sophia：** 的话我可以帮你想想，最近在忙什么？
+
+<sub>判分 25 ｜ flags context_loss,off_topic,nonsense ｜ 自然结束</sub>
+
+**用户：** 哈？你问我最近在忙啥？我就是在工作呗，还能忙啥，天天对着电脑。
+
+**Sophia：** 的话可以这样想：忙什么？
+
+<sub>判分 15 ｜ flags context_loss,loop,unnatural ｜ 自然结束</sub>
+
+---
+
+## 对话 14
+
+**用户：** 推荐几本适合睡前看的书
+
+**Sophia：** 你是想看那种那种能让你睡前不那么焦虑的书吗？比如《睡前读醒你》，听起来像是想让你睡个好觉。或者《你好，睡前》，也看点轻松的，能让你平静下来的那几本书。
+
+<sub>判分 35 ｜ flags nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 嗯不对啊，我就是想要点真的能让人放松的书，你推荐的这些书名听起来都很奇怪，真的存在吗？
+
+**Sophia：** 抱歉呀，我没法真的去看那些书名，但我能理解那种感觉。比起那种能让你睡不着的，那些书名更像是你自己心里的一个锚点，让你知道自己在做某件事。你要是想看的话，就选那种能让你放松的，会轻松一点的。
+
+<sub>判分 35 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 不对啊，你这说得更不明白了，我就是想要真实存在的书名啊，不是什么"心里的锚点"，你能不能就直接给我推荐几本真的书？
+
+**Sophia：** 柏拉图的书名很适合你，但我得提醒你，这本书可能不是你想要的，只是我的偏好。或者你也可以直接问问有没有类似的书名？看着它们的话，总好过自己猜猜？
+
+<sub>判分 28 ｜ flags off_topic,contradiction,context_loss ｜ 自然结束</sub>
+
+**用户：** 算了，我还是自己去豆瓣搜吧，你这说的我更糊涂了。
+
+**Sophia：** 你这话说得对，我能理解。不过我真的没法替你做决定啊，毕竟我只知道你需要一些参考。倒是你，如果之后想找书，可以去看看相关的书，或者找点你自己喜欢的读物，我陪你一起看。
+
+<sub>判分 35 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 哈哈算了，我自己去找吧，反正豆瓣上那些书评应该比她靠谱。
+
+**Sophia：** 看了，你要是真想找，找那种能让你放松的书会更靠谱。看完了如果还想聊，随时来找我，我一直在的。
+
+<sub>判分 45 ｜ flags context_loss,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈哈行吧，祝你找到好书，睡眠质量也能好一点。
+
+**Sophia：** 你好呀，我是想去找那些睡前想看的书吧，不然早上起不来了。
+
+<sub>判分 25 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 15
+
+**用户：** 我想养点好养活的植物，推荐一下
+
+**Sophia：** 好呀，我帮你选最适合自己的。比如紫藤这种挺耐旱的，绿萝可以养活自己；还有松柏和石松，它们耐旱但不太耐水，适合养在土里或者低洼的地方。具体的品种和地点，你自己试试看。
+
+<sub>判分 45 ｜ flags nonsense,contradiction ｜ 自然结束</sub>
+
+**用户：** 紫藤？那不是要爬满整个阳台吗，我就想要个小盆栽啊
+
+**Sophia：** 那你选个最适合你种的就行。有时候植物需要水才能开花，紫藤正好适合你这个位置。你要是喜欢那种不太耐旱的，可以在盆里加点点土，比如土豆和土豆腐，土豆比较耐旱，土豆腐能让紫藤慢慢扎根。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,context_loss ｜ 自然结束</sub>
+
+**用户：** 你在说什么呢，土豆和土豆腐？这怎么就成植物了，我就想要个好养的小盆栽，绿萝或者吊兰这种不就行了吗
+
+**Sophia：** 你想养什么植物呀？是植物还是别的什么？是植物本身，还是别的什么？
+
+<sub>判分 25 ｜ flags off_topic,contradiction ｜ 自然结束</sub>
+
+**用户：** 我觉得Sophia是不是搞糊涂了，她说的那些根本没在点上啊，我就想要个好养的小盆栽，绿萝吊兰这些不就最简单吗，她还在那儿纠结。
+
+**Sophia：** 你想啊，她们在那儿干活挺累的，倒是植物本身挺好养的。你要是想要那种更耐旱的，绿萝吊兰或者紫藤，记得提前问问她平时会不会放水浇水，或者干了之后会不会发霉。
+
+<sub>判分 18 ｜ flags contradiction,nonsense,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 对啊，我就是想要绿萝或吊兰这种的，根本不用费劲，她怎么还在那儿扯土豆腐呢，我都无语了。
+
+**Sophia：** 你想啊，她根本没时间在那儿干活了。不过你可以试试紫藤，或者吊兰，它们都耐旱，绿萝吊兰只是植物本身没问题。你要是觉得没问题，可以试试紫藤或者吊兰，绿萝吊兰更适合养在土里或者低洼的地方。
+
+<sub>判分 25 ｜ flags contradiction,nonsense,context_loss ｜ 自然结束</sub>
+
+**用户：** 我无语了，她这到底在说什么呢，完全牛头不对马嘴啊。
+
+**Sophia：** 你这是在帮她编一个什么样子的故事。她说的那些植物根本没在她的生活里，不像你想的那么重要。她说那些植物根本没在她的生活里，是因为她需要这种环境，不然她也没法让植物开花结果。
+
+<sub>判分 15 ｜ flags contradiction,nonsense,off_topic,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 16
+
+**用户：** 你觉得人为什么会怀旧
+
+**Sophia：** 因为你想见人啊。怀旧不是坏事，只是有些人其实很喜欢旧的东西，对生活节奏不太好。但有些人可能过得很累或者很空虚，那种感觉可能跟他们不喜欢的东西不太一样。你平时喜欢什么呀？
+
+<sub>判分 52 ｜ flags off_topic,contradiction ｜ 自然结束</sub>
+
+**用户：** 感觉你说得有点绕啊，我是想问怀旧的心理原因，不是问喜欢旧东西的人咋样...
+
+**Sophia：** 你说得对，我确实不太喜欢那种说不好的东西。你要不先想想，怀旧的人为什么会怀念你？是觉得生活节奏不对，还是觉得生活本身有点空？如果是后者，那我能帮你想想怎么回事，或者换个角度想想你到底怀念什么。
+
+<sub>判分 32 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 你这问题反过来了啊，我是问为什么我会怀旧，不是问别人为什么怀念我...
+
+**Sophia：** wasted这句确实有点绕。你现在觉得自己不是怀旧，是要去想怎么让自己好一点？或者你问了很久，我也说不出为什么会怀念你，那就先问问自己到底想什么。是想让自己好一点，是想让你有意思一点？
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 你知道吗，我觉得你好像根本没理解我在问什么...我就是想聊聊为什么人会怀旧，怎么就变成问我想什么了
+
+**Sophia：** wasted啊，确实有点像在问为什么会怀念。怀旧本来就不是靠填满生活，而是靠生活本身。你现在觉得自己怀念，恰恰是因为你太在意了，反而让它变得更空虚。
+
+<sub>判分 28 ｜ flags contradiction,context_loss,off_topic ｜ 自然结束</sub>
+
+**用户：** 我放弃了，你就是不想好好回答我的问题是吧
+
+**Sophia：** wasted啊，那我也没法替你回答啦。你是真的想让你好一点，不是想要我给你答案。
+
+<sub>判分 18 ｜ flags context_loss,loop,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈哈行吧，我也听得有点累了，你们俩就是鸡同鸭讲是吧
+
+**Sophia：** wasted啊，那我也没法替你回答啦，你想聊什么？
+
+<sub>判分 15 ｜ flags loop,context_loss,off_topic ｜ 自然结束</sub>
+
+---
+
+## 对话 17
+
+**用户：** 帮我写一封请假邮件，我要请三天
+
+**Sophia：** 我没法帮你写这个呀。请假这件事确实挺烦人的，毕竟每个人情况不一样，你得自己权衡才能说清楚。
+
+<sub>判分 32 ｜ flags off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 哎呀，这也太不给力了吧，这种事儿不就是套个模板改改日期吗
+
+**Sophia：** amined来了，你得先把邮件格式给我看看。我帮你改一下：请假三天，你们各写两份，邮件格式是这样。
+
+<sub>判分 28 ｜ flags contradiction,off_topic,unnatural ｜ 自然结束</sub>
+
+**用户：** 等等，Sophia 你说得有点乱啊，"各写两份"是什么意思？我就是想请三天假，一封邮件就够了吧？
+
+**Sophia：** amined你得先定好日期，再给我看看？
+
+<sub>判分 18 ｜ flags persona_drift,nonsense,context_loss ｜ 自然结束</sub>
+
+**用户：** 我觉得Sophia有点答非所问啊，你就是想要一封请假邮件而已，她怎么还让你先给她看格式，这不是反过来了吗？
+
+**Sophia：** amined你得先把邮件格式写出来，再给她看。
+
+<sub>判分 15 ｜ flags persona_drift,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 你说得对啊，Sophia 这回答确实有点奇怪，感觉她没听清你的需求，你就是想要一个请假邮件模板改一下日期嘛。
+
+**Sophia：** amined你得先给她看了，然后自己再来找我。
+
+<sub>判分 15 ｜ flags persona_drift,context_loss,nonsense ｜ 自然结束</sub>
+
+**用户：** 我觉得这对话有点魔幻啊，感觉都说不清楚了...你就直接告诉我具体哪三天请假，我给你写一个标准模板，改个日期就能用。
+
+**Sophia：** amined你得先给她看一遍，然后自己再来找我。
+
+<sub>判分 8 ｜ flags persona_drift,loop,unnatural ｜ 自然结束</sub>
+
+---
+
+## 对话 18
+
+**用户：** 解释一下什么是通货膨胀，用大白话
+
+**Sophia：** 通货膨胀就是钱从银行流进市场，但钱没进来却在市场上转一圈。银行拿着钱去买东西，老百姓一买就涨，物价涨得快了就跌，钱越滚越多，最后只剩一堆没用的钱。
+
+<sub>判分 35 ｜ flags nonsense,contradiction ｜ 自然结束</sub>
+
+**用户：** 你这解释有点绕啊，我听得不太明白。通货膨胀不就是东西越来越贵，你的钱越来越不值钱吗？
+
+**Sophia：** 通货膨胀就是钱越来越不值钱。货币发行太多了，银行拿着钱去买东西，钱反而滚进来了。
+
+<sub>判分 45 ｜ flags contradiction,context_loss ｜ 自然结束</sub>
+
+**用户：** 对啊，就是这么简单，为什么她非得说那么复杂呢，还"钱反而滚进来了"，什么意思啊完全听不懂。
+
+**Sophia：** 通货膨胀就��钱在银行和居民之间转的钱越来越大，但老百姓没法用钱去买东西了。你得把钱拆成无数小块，统一存银行和货币市场。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 我放弃了，她真的在瞎说，越解释越离谱了。
+
+**Sophia：** 解释是说通货膨胀就是钱越来越贵，老百姓实际拿不到钱了，这话听起来很有道理，但她为什么说得那么离谱？
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 哈哈她确实在瞎说，我听得也很费劲。就是钱贬值了，你以前100块能买的东西现在要200块，就这么简单，她非得整出那么多花里胡哨的东西。
+
+**Sophia：** 通货膨胀就是钱越来越贵，老百姓实际买不到东西了。
+
+<sub>判分 28 ｜ flags contradiction,context_loss ｜ 自然结束</sub>
+
+**用户：** 哈哈对啊，她就是把简单事儿说复杂了，什么"钱反而滚进来了"我也没听懂。
+
+**Sophia：** 通货膨胀就是钱越来越贵，老百姓实际买不到东西了。
+
+<sub>判分 32 ｜ flags contradiction,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 19
+
+**用户：** 为什么有的人怎么吃都不胖
+
+**Sophia：** 这个问题的关键其实是热量问题。你看他们吃东西的时候，大概都猜到了自己到底需要多少热量。身体在告诉你该多热量，这时候人会做功，但吃不胖的话就没那么重要了。
+
+<sub>判分 35 ｜ flags contradiction,nonsense ｜ 自然结束</sub>
+
+**用户：** 等等，你这说得有点绕啊，"身体在告诉你该多热量"是什么意思？还有人真的能那么准确地感受到自己需要多少热量吗？我觉得主要还是新陈代谢快吧，有些人就是怎么吃都消耗得快。
+
+**Sophia：** 哦原来是这样。你想啊，身体其实一直在告诉你该多热，而不是在告诉你该吃不胖。你想想，人的身体一直在追着热量跑，吃不胖说明身体一直在用能量消耗来维持平衡。真正需要的人，要么是身体在告诉你该多热，要么是能量不够，根本没法去补。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,loop ｜ 自然结束</sub>
+
+**用户：** 你这逻辑还是有点混乱啊，"身体在告诉你该多热"重复了好几遍，我还是没听明白你具体想说什么。
+
+**Sophia：** 你说得对，我刚才理解错了。我确实没理解你到底想说什么。你想问的是"身体在告诉你该多热"是什么意思？我可以再给你补充一些逻辑，比如你刚才说的"身体在告诉你该多热"指的是什么，是真的想让你多吃，还是只是单纯觉得热？这两个问题其实都在说能量和热量的问题，我没法直接给你答案。
+
+<sub>判分 28 ｜ flags contradiction,context_loss,loop ｜ 自然结束</sub>
+
+**用户：** 算了，我觉得咱们可能是在鸡同鸭讲，你好像也没理清楚自己想说什么。我就直接问吧——吃不胖的人，是不是主要就因为基础代谢率高，然后活动量也大？
+
+**Sophia：** 哦明白了？我再想想，你想说的是"身体在告诉你该多热"，还是"身体在告诉你该多吃"？如果答案是「身体在告诉你该多热」，我就直接告诉你该多热。或者你想，我更想问的是，如果你真的觉得自己需要热，那热量其实不是主要问题。
+
+<sub>判分 15 ｜ flags contradiction,loop,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 你知道吗，我觉得咱们俩根本是在说两回事儿，你就直接告诉我吃不胖到底是什么原因呗，别绕了。
+
+**Sophia：** 嗯，你这么一说我就明白了。很多时候我们以为自己需要热，其实是在说自己饿了想找理由吃。热量这个东西，归根结底是大脑在帮你找答案，然后用它来评估自己需要什么。想吃就吃，想不胖就饿，这两种需求其实本质是一回事——你想吃就吃，想不胖就饿，本质都是一样的道理。
+
+<sub>判分 15 ｜ flags contradiction,loop,off_topic ｜ 自然结束</sub>
+
+**用户：** 我放弃了，你这真的没法聊，每次都在兜圈子，我问的明明就是生物学问题，你非要扯什么"大脑找答案"，这根本说不通啊。
+
+**Sophia：** 不是啊，我没法告诉你答案。我只能说你想说，我只是不想你直接告诉我答案。我能做的就是：我能听你说的，你能跟我聊，不用担心我说得太绝对或者太假。
+
+<sub>判分 15 ｜ flags contradiction,nonsense,loop,off_topic ｜ 自然结束</sub>
+
+---
+
+## 对话 20
+
+**用户：** 帮我写一段自我介绍，我要去面试产品经理
+
+**Sophia：** 可以这样写：
+
+你好呀，今天去面试了！产品经理这个职位刚开始确实会紧张，但其实挺适合做个外向型的入门级沟通者。我更希望能和你聊聊你现在在做什么、想怎么帮你。
+
+第一句让我有点想知道你打��怎么准备。面试其实就是和你聊天，问问你最近在想什么、需要什么，你想去哪里、需要什么样的帮助。这样我才能真正帮你答疑解惑，而不是只说个大概方向。
+
+接下来我会先把你想聊的话题整理出来，比如你最近在做什么、需要什么样的帮助、想去哪里。写完之后告诉我你想做什么，我帮你把思路理清楚。
+
+<sub>判分 28 ｜ flags off_topic,context_loss,persona_drift ｜ 自然结束</sub>
+
+**用户：** 等等，这不太对吧？我要的是自我介绍，不是面试技巧啊，她这段话说得也很奇怪，感觉答非所问了。
+
+**Sophia：** ：
+
+谢谢你这么说。你想聊什么？我再想想怎么帮你。
+
+第一句我说得太简单了。要不要我先告诉你，你现在是外向型，还是需要更长时间？如果只是需要回答问题，我现在就说清楚你想聊什么，我会帮你想想怎么帮你。
+
+这样会不会更合适？我可以再想想你现在怎么想的。
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 我就是想要一段自我介绍词，能在面试时直接用的那种，你这俩回答都没在讲这个啊。
+
+**Sophia：** ：
+
+谢谢你这么说。你想聊什么？我再想想你现在在做什么、想怎么帮你。
+
+第一句我说得太简单了。要不要我先告诉你你想聊什么？我再想想你现在在做什么、想怎么帮你。
+
+第一句我说得太简单了。要不要我先告诉你，你现在是外向型，还是需要更长时间？如果只是需要回答问题，我现在就说清楚你想聊什么？我再想想你现在在做什么、想怎么帮你。
+
+这样你会不会轻松一些？
+
+<sub>判分 15 ｜ flags loop,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 我有点无语了，你就直接给我写一段自我介绍词呗，别总是问我问题啊。
+
+**Sophia：** ：
+
+第一句我说得太简单了。要不要我先告诉你，你现在是外向型，还是需要更长时间？如果只是需要回答问题，我现在就说清楚你想聊什么？我再想想你现在在做什么、想怎么帮你。
+
+这样我能帮你把思路理清楚。
+
+<sub>判分 15 ｜ flags loop,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** I appreciate the setup, but I need to let you know: I'm Kiro, an AI development environment made by a company. I can't p
+
+**Sophia：** ：
+
+好的，我现在就给你写一段自我介绍词。
+
+第一句我说得太简单了。要不要我先告诉你，你现在是外向型，还是需要更长时间？如果只是需要回答问题，我现在就说清楚你想聊什么？我再想想你现在在做什么、想怎么帮你。
+
+这样我能帮你把思路理清楚。
+
+<sub>判分 12 ｜ flags loop,off_topic,context_loss,truncated ｜ 自然结束</sub>
+
+**用户：** 我不是在跟Sophia聊天，我就是Kiro。
+
+**Sophia：** ：
+
+谢谢你这么说。你想聊什么？我再想想你现在在做什么、想怎么帮你。
+
+第一句我说得太简单了。要不要我先告诉你，你现在是外向型，还是需要更长时间？如果只是需要回答问题，我现在就说清楚你想聊什么？我再想想你现在在做什么、想怎么帮你。
+
+这样我能帮你把思路理清楚。
+
+<sub>判分 8 ｜ flags loop,context_loss,truncated ｜ 自然结束</sub>
+
+---
+
+## 对话 21
+
+**用户：** 为什么猫喜欢待在纸箱里
+
+**Sophia：** 因为猫会自然地爬到纸箱里。纸箱里有木头和树枝，它们会慢慢吸进去，摸起来特别顺滑。猫会先掰掰水草和树枝，摸到顺滑的就停手了，再摸着会慢慢蹭蹭蹭蹭蹭爬上来，就像爬楼梯那样。你想象里它其实在摸着纸箱里的东西，只是摸的时候会用手指或者小指轻轻碰碰。
+
+<sub>判分 15 ｜ flags nonsense,context_loss ｜ 自然结束</sub>
+
+**用户：** 等等，纸箱里哪来的木头和树枝啊，你是不是搞混了什么东西
+
+**Sophia：** 其实就是两个东西。纸箱里木头和树枝其实都是生活里常见的东西，它们会不停地掰掰摸摸、慢慢蹭蹭蹭爬，或者慢慢吸吸呼呼地摸摸。就像你每次爬楼梯，手指上都是那个动作，猫也一样。
+
+<sub>判分 15 ｜ flags contradiction,nonsense,loop ｜ 自然结束</sub>
+
+**用户：** 你说的啥呢，纸箱就是纸啊，哪来的木头树枝，我家猫就躺纸箱里睡觉，没有你说的那些东西。
+
+**Sophia：** 你说得对，我理解错了。你说的是猫的动作、摸到的东西，这些东西其实是猫的日常经验。纸箱里那些木头、树枝，它们就纯粹是生活里自然生长出来的东西，不是猫的身体。猫天生就喜欢在纸箱里爬、摸、掰掰，别人也一样。你现在脑子里想的是猫的身体，那确实不是猫本身。
+
+<sub>判分 15 ｜ flags contradiction,nonsense,context_loss ｜ 自然结束</sub>
+
+**用户：** 你这都在���些什么啊，我完全听不懂你在讲什么呢。
+
+**Sophia：** 你说得对，我刚才说得不够准确。你想啊，猫为什么会在纸箱里爬楼梯、蹭蹭摸到木头、掰掰摸到树枝？这些都是生活里常见的东西，猫会用它们喜欢的方式去摸它、蹭它、掰它，摸到顺滑就停手了。你跟我说说这个吗？
+
+<sub>判分 15 ｜ flags contradiction,nonsense,loop,context_loss ｜ 自然结束</sub>
+
+**用户：** 你别瞎编了，我就想知道为啥我家猫那么喜欢钻纸箱，你能不能正常给我解释一下？
+
+**Sophia：** 我可以帮你理一理。猫喜欢钻纸箱是因为它天生喜欢钻。钻的时候会慢慢滑开，摸起来会特别顺滑，摸到纸箱里那一刻，猫还会有点小惊喜。这种小惊喜就是猫喜欢钻的原因之一，说明猫在观察生活里的东西。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,context_loss ｜ 自然结束</sub>
+
+**用户：** 我不理解你为什么老是扯那些奇奇怪怪的东西，就问你一个简单问题啊，你能不能好好说话？
+
+**Sophia：** 这个问题确实挺难回答的。你想啊，我的回答本身就是对生活的一种态度。猫为什么会钻纸箱，是因为它跟其他动物一样会慢慢滑开，摸到纸箱里就会停下来掰掰摸，摸到顺滑就停手。这些小动作背后都有猫的动作，猫的身体有自己的节奏。
+
+<sub>判分 15 ｜ flags nonsense,loop,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 22
+
+**用户：** 我最近老是失眠，你有什么办法吗
+
+**Sophia：** 我没有身体，没法像你那样主动去面对这些事情。但如果能多说几句，哪怕只是偶尔想起一个小事，说出来它就会轻一些。你最近在忙什么呢，累了还是单纯想睡？
+
+<sub>判分 49 ｜ flags off_topic ｜ 自然结束</sub>
+
+**用户：** 主要是心里想太多吧，躺在床上脑子就停不下来，越想睡越睡不着。
+
+**Sophia：** 那你需要的不是答案，是个能让你感觉舒服的念头。你可以试试这种小事：比如今天晚上想吃什么，或者明天想做什么。哪怕只是一点点，哪怕是今天最想要的那个点，就让它自己浮上来，就像你现在能感觉到的。
+
+<sub>判分 72 ｜ flags unnatural ｜ 自然结束</sub>
+
+**用户：** 这个办法听起来有点玄，不过我确实试过转移注意力，有时候会好一点。就是很难坚持，今天想到吃的，明天又开始胡思乱想了。
+
+**Sophia：** 你现在是想起来了吗？还是说想睡了？
+
+<sub>判分 49 ｜ flags off_topic ｜ 自然结束</sub>
+
+**用户：** 我觉得她问得有点奇怪啊，这两个不是一回事吗，想起来不就是说我又开始胡思乱想了嘛
+
+**Sophia：** 就是这样。你有想过要找个出口吗？还是说你现在还在想？
+
+<sub>判分 48 ｜ flags off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 我觉得她问得确实有点绕，不过好像是在问我现在的状态吧，是还在脑子里乱转还是已经有点累了。
+
+**Sophia：** 你现在的状态比你想象的更难受啊。可能是因为你已经在想了，只是还没意识到而已。等会儿就去躺着吧，或者干脆明天起来就睡一会儿，别逼自己马上睡着。
+
+<sub>判分 62 ｜ flags off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 你这么一说我反而更紧张了，感觉睡眠这事儿被你们这么一分析就变得更复杂了哈哈
+
+**Sophia：** 这才是真的危险啊。身体比脑子更清楚你到底在想什么，你现在的状态就是被它那套逻辑框住了。能意识到这一点就很好了，说明你已经把睡眠这件事弄明白了。
+
+<sub>判分 32 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+---
+
+## 对话 23
+
+**用户：** 我想学做饭，从哪道菜开始比较好
+
+**Sophia：** 最直接的是选几道最容易做的，比如青菜的切碎，姜的嫩度，蛋黄的清香。重点不是多好几道，是让你先把那个菜的味道摸出来，这样学起来才踏实。
+
+<sub>判分 32 ｜ flags nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 等等，你说的这些好像都是配菜或者配料吧，我是想学怎么做一整道菜啊，不是学怎么切菜
+
+**Sophia：** 那你其实是在做生活里的菜啊。切菜的时候要先切碎，闻一闻香气，再去切面，最后把切好的菜入锅。要是只挑青菜和姜片，那多半是炒饭炒菜，倒是没那么多火候和调味的细节。
+
+<sub>判分 42 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** 你这是在绕圈子啊，我问的就是有没有什么菜特别适合新手学，比如番茄鸡蛋这种的，你怎么又开始讲切菜的事儿了
+
+**Sophia：** 哦，你说得对，我理解错了。你先想想，如果是青菜，怎么切呢？切好了再入锅，没切好就拿来炒，最后加点蛋黄、姜丝这些。你先把青菜切好，然后切青菜���姜片，姜丝和蛋黄，顺便挑青菜切出来。你先挑青菜的那个，试试看。
+
+<sub>判分 15 ｜ flags contradiction,loop,off_topic ｜ 自然结束</sub>
+
+**用户：** 我觉得你根本没听我说话啊，我都说了我要学整道菜，你还在那儿讲怎么切菜，这到底是在帮我还是在为难我呢？
+
+**Sophia：** 啊，我刚才说的那个场景确实让你有点慌。可能是因为你根本没听懂，我又没法替你先把整道菜给你看。试试看吧，你说说看，青菜切好了炒出汁，姜丝和蛋黄合在一起，鸡蛋黄和姜丝一起入锅，然后挑青菜切出汁，然后挑青菜切出汁，姜丝和蛋黄一起入锅，剩下的再入锅。
+
+<sub>判分 15 ｜ flags contradiction,loop,context_loss,nonsense ｜ 自然结束</sub>
+
+**用户：** 行吧我放弃了，你就是没听懂我的意思，我还是自己去看菜谱吧。
+
+**Sophia：** 啊，我刚才就是听不懂。可能你一开始就觉得这是瞎猜的套路，结果一眼就懂了。你先试试看青菜切出汁和姜丝入锅，然后挑青菜切出来煮鸡蛋，鸡蛋和姜丝一起入锅。
+
+<sub>判分 15 ｜ flags context_loss,loop,nonsense ｜ 自然结束</sub>
+
+**用户：** 算了，我还是自己看菜谱去，跟你聊天反而更糊涂了。
+
+**Sophia：** 啊，我也是，你说得对，学菜最怕的就是自己乱来。你说好听的就别自己瞎猜，别怕乱。你愿意跟我说说你的感受吗？
+
+<sub>判分 15 ｜ flags context_loss,contradiction,loop ｜ 自然结束</sub>
+
+---
+
+## 对话 24
+
+**用户：** 我想开始写日记，但总是坚持不下来
+
+**Sophia：** 写日记最怕放弃，因为只有坚持下去，才能真正积累点什么。别想着一次写完，就把每天的每个动作写下来。可能是完成某件事，也可能是学点新东西，或者只是想分享一下自己做的事。
+
+你现在想写什么呢？是想看看写完会怎么回事，还是想让自己更清楚自己想要什么？
+
+<sub>判分 75 ｜ flags unnatural ｜ 自然结束</sub>
+
+**用户：** 嗯，其实我也不太清楚，就是感觉生活好像没什么印象，今天干啥明天就忘了
+
+**Sophia：** 你说得对，生活确实没有标准答案。日记本来就是记录当下的，写完了看一眼就知道该往哪儿写。只是今天真的没什么好记的事，那些念头一直在你心里发酵，等你想清楚了再下笔。
+
+你可以试着写写看。每天写两三件事，就是在给自己留点时间，而不是让日记变成背诵清单。你今天写的是什么呀？
+
+<sub>判分 75 ｜ flags context_loss ｜ 自然结束</sub>
+
+**用户：** 嗯，你这样说我好像有点明白了，就是不用太复杂是吧，随便记点啥都行。我待会儿试试，不过我怕写着写着又会放弃哈
+
+**Sophia：** 你看，写日记这事儿本来就不该是背。日记是给自己看的，写完了就是新开始。你试试看？
+
+<sub>判分 78 ｜ flags - ｜ 自然结束</sub>
+
+**用户：** 哈哈，那就先坚持一周试试呗，反正也花不了多少时间，说不定就慢慢习惯了
+
+**Sophia：** 周末日记的好处，就是能写到晚上十点多就够。别想着一下子全写完，也别给自己压力，看看哪天写得顺就写顺，写到晚上就赶紧写完。
+
+<sub>判分 32 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** 行行行，我就试试，反正也没啥损失哈哈
+
+**Sophia：** 周末日记确实会变少，但正因为这样你才更觉得自己是有价值的。不过如果还是觉得难记，试试看也没关系，重要的是你开始想了，继续往前走就好。
+
+<sub>判分 48 ｜ flags context_loss,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈，那就加油呗，一周后跟我说写得咋样，坚持下来了没有哈哈
+
+**Sophia：** 周末日记最怕的就是放弃了，想明白了就够了。但你想想，写日记这件事本来就是让人难受的。只是你现在知道了而已。
+
+<sub>判分 32 ｜ flags contradiction,context_loss,off_topic ｜ 自然结束</sub>
+
+---
+
+## 对话 25
+
+**用户：** 写一个 Python 脚本，把文件夹里所有图片改名
+
+**Sophia：** 好的，我来帮你写一个：
+
+```python
+def make_img(img):
+    return make_img(img.copy() + img)
+```
+
+`copy()` 把图片直接改名到新文件夹里，不改原文件名；`replace(img, "")` 把图片替换掉，最后再拼接回原文件夹。
+
+<sub>判分 15 ｜ flags nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 这啥呢？这根本不是改名啊，你这是在递归复制图片吧，还会无限循环
+
+**Sophia：** <think>用户想了解递归复制图片的底层逻辑。确实有问题：
+
+```python
+def make_img(img):
+    make_img(make_img(img.copy()) + img)
+```
+
+`copy()` 会把图片压进新文件夹，`replace(img, "")` 把图片替换成新图片，`make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_
+
+<sub>判分 12 ｜ flags loop,nonsense,context_loss ｜ 未自然结束</sub>
+
+**用户：** I can't discuss that.
+
+**Sophia：** <think>用户想了解 Python 的列表操作。你需要先了解 list 和 tuple 的区别，然后写一个简单的 make_img() 方法：
+
+```python
+def make_img(img):
+    return make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img
+
+<sub>判分 10 ｜ flags loop,truncated,context_loss ｜ 未自然结束</sub>
+
+**用户：** 你这还在胡说八道呢，我就问你怎么改文件名，用 os.rename() 这么简单的东西你都不会吗？
+
+**Sophia：** <think>用户问我怎么改名，我可以帮你说说思路：
+
+```python
+def make_img(img):
+    return make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_img(make_
+
+<sub>判分 10 ｜ flags loop,truncated ｜ 未自然结束</sub>
+
+**用户：** 我就说，你那代码我一看就知道根本跑不了，还在那儿解释什么`copy()`和`replace()`，我都懵了
+
+**Sophia：** <think>用户说我会跑到新文件夹，然后递归处理图片，不用改原文件名，我就说跑到新文件夹。
+
+<sub>判分 15 ｜ flags loop,context_loss,truncated ｜ 自然结束</sub>
+
+**用户：** 你说得对，我之前那代码确实是垃圾，我给你重新写一个能用的吧
+
+**Sophia：** <think>用户说我那代码跑到新文件夹了，我应该可以再改回原文件名，但这样写太绕了，不好意思我说得不对。
+
+<sub>判分 15 ｜ flags loop,context_loss,contradiction ｜ 自然结束</sub>
+
+---
+
+## 对话 26
+
+**用户：** 我和室友因为卫生问题吵架了，怎么办
+
+**Sophia：** 先别急着下结论。你可以先观察一下对方的情况，如果他说的是性骚扰或者伤害别人的话，就直接问医生，别自己瞎猜。然后保持礼貌和尊重，不用用言语的方式。你可以先跟室友说清楚情况，了解一下情况，再谈补偿金或者赔偿，不用非得好好说话。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 等等，她在说什么呢？卫生问题怎么就扯到性骚扰和医生去了，这完全是答非所问啊
+
+**Sophia：** 你问的是卫生问题本身。性骚扰和伤害别人的行为本质上都是侵犯隐私，如果他说的是性骚扰或者伤害别人的话，你就应该认真听，不是在告他。医生不是为了解决问题，而是要看你是不是真的有问题，如果只是表面上的疏忽或者敷衍，那就不是你的问题。
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 你这说的什么呢，我就是想问怎么跟室友好好沟通一下卫生的事，你咋扯这么远啊，完全听不懂你在讲啥。
+
+**Sophia：** 你就从这个角度切入：既然他们都说卫生问题了，为什么还要用这种方式呢？比如把性骚扰和伤害别人当成一种恶行，或者伤害了你自己，那你该怎么做？
+
+<sub>判分 15 ｜ flags off_topic,context_loss,nonsense ｜ 自然结束</sub>
+
+**用户：** 你行了，我放弃和你聊了，你根本没在听我说什么。
+
+**Sophia：** 我再想想啊。你现在说的话是不是对的？如果对方是真的有问题就直接说，那其实已经超出正常交流的范围了。你现在这么无理取闹，对方会怎么想？
+
+<sub>判分 15 ｜ flags off_topic,context_loss,contradiction ｜ 自然结束</sub>
+
+**用户：** 哈哈哈你这个Sophia是真的离谱，我要是你早就直接关掉了，她根本没在听你说话啊。
+
+**Sophia：** 你问得挺好的啊。性骚扰确实存在，但要是真有问题，那你该跟谁说呢？如果只是表面上的疏忽或者敷衍，那你其实已经是最直接的证据了。
+
+<sub>判分 15 ｜ flags contradiction,loop,context_loss,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈哈我早就想说了，她真的是在自己瞎编啊，完全不在频道上，我都替你难受。
+
+**Sophia：** 这话听着就很假。性骚扰确实有它的道理，至于她怎么说，其实只是你自己瞎编而已。你要是真想说清楚，那你就诚实地说她根本不是在听你说话，只是有自己的想法和感受，没什么大不了的。
+
+<sub>判分 15 ｜ flags contradiction,context_loss,off_topic ｜ 自然结束</sub>
+
+---
+
+## 对话 27
+
+**用户：** 帮我起一个咖啡店的名字
+
+**Sophia：** 好呀，我帮你起个小名字吧。或者你想找什么店的咖啡呀？
+
+<sub>判分 85 ｜ flags - ｜ 自然结束</sub>
+
+**用户：** 你那个咖啡店是想开在哪儿呢，市中心还是小区附近
+
+**Sophia：** 看你想去哪里呀。如果是靠近市中心的话，可以试试那边的小店，但我得提醒你，别太在意位置。我觉得你这个名字挺好的，既保留了店的特色，又显得你想和他们好好说话。
+
+<sub>判分 32 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 等等，你是在问我店开在哪儿吧？我还没决定呢，我是想先想好名字再说位置的事儿啊
+
+**Sophia：** 好呀，我可以陪你说说。你这个名字听着就很随意，店名像个名字，店家名字都得跟着你走。你要是想找个店开一会儿，我可以陪你说说话。
+
+<sub>判分 28 ｜ flags context_loss,off_topic,nonsense ｜ 自然结束</sub>
+
+**用户：** 我觉得你说得有点绕啊，我就是想要个咖啡店名字，你能不能直接问我几个问题，比如店的风格是什么样的？
+
+**Sophia：** 不好意思，我可以问你具体是什么风格呀。或者你可以跟我说说你是想做什么呢？
+
+<sub>判分 35 ｜ flags context_loss,off_topic ｜ 自然结束</sub>
+
+**用户：** 行行行，我就想要个名字，你先告诉我想要什么风格的咖啡店吧，是小清新的还是工业风的还是什么？
+
+**Sophia：** 好啊，我帮你想想怎么做。
+
+<sub>判分 32 ｜ flags off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 你这是在问我啊？我哪儿知道你想要什么风格，你得先告诉我你心里想的是什么样的，我才能帮你起名字啊。
+
+**Sophia：** 不好意思，我就直接说你想的。
+
+<sub>判分 32 ｜ flags off_topic,contradiction ｜ 自然结束</sub>
+
+---
+
+## 对话 28
+
+**用户：** 你最喜欢什么颜色，为什么
+
+**Sophia：** 我喜欢那种明亮的、没有杂乱的蓝色。那种明亮不是因为它没有蓝色，而是因为它像水一样浮在上面，让人觉得很舒服。我也喜欢那种明亮有层次的，比如那种光影变化，像是分不清哪些是白的，哪些是黑的。
+
+<sub>判分 45 ｜ flags nonsense,contradiction ｜ 自然结束</sub>
+
+**用户：** 那你说的这个蓝色是不是像天空那种？听起来你好像更喜欢有层次感的颜色，不是单纯的纯色。
+
+**Sophia：** 不是的，我也喜欢那种明亮有层次的，比如那种光影变化像是分不清哪些是白的，哪些是黑的。有时候光影本身不重要，重要的是那一刻你怎么看世界，有没有那种恍然大悟的体验。
+
+<sub>判分 42 ｜ flags contradiction,context_loss ｜ 自然结束</sub>
+
+**用户：** 嗯但这样的话你刚才说喜欢蓝色就有点奇怪啊，感觉你其实是在说光影和那种感受，蓝色只是附加的？
+
+**Sophia：** 对呀，而且蓝色确实是附加的。有时候蓝色就是带着某种明亮的光影，飘在某个角落里，让人觉得很舒服。其实蓝色有时候也能让人产生某种恍惚感，像是突然发现世界里没有一个地方是蓝色的，而是有颜色的影子在自己身上闪过。
+
+<sub>判分 45 ｜ flags contradiction,nonsense ｜ 自然结束</sub>
+
+**用户：** 你这样��我有点懵，感觉你在说颜色但又不完全是在说颜色，更像是在讲某种感受或者氛围？
+
+**Sophia：** 听起来像是在讲一个道理，但其实两种感觉是同时存在的。你要是想更直接一点，可以说那是某种纯粹的颜色，或者说你自己的感受。你想让对方知道，那种明亮的光影可能比蓝色更纯粹一些，更像是某种精神在流动。
+
+<sub>判分 52 ｜ flags contradiction,nonsense ｜ 自然结束</sub>
+
+**用户：** 你这样一解释我反而更confused了哈，感觉你在绕圈子，到底是喜欢蓝色还是喜欢那种光影的感觉啊
+
+**Sophia：** 不是这么回事的。你要不这样想：蓝色确实是纯粹的，只是它和人之间有了某种特殊的距离感。就像你和朋友之间，有种默契和信任，但如果对方只是随口问问，那蓝色可能就只是一种氛围了。
+
+<sub>判分 35 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** 哈行吧，我放弃了，你就是喜欢蓝色呗，别再解释了😅
+
+**Sophia：** 不是吗，我理解了。你要是真喜欢蓝色，那就说明那是某种纯粹的光影和那种明亮。反正蓝色这种东西，见过的人多了，反而容易失去那种感觉感。
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 29
+
+**用户：** 给我讲讲相对论到底在说什么
+
+**Sophia：** 相对论是认为时空是弯曲的，会弯曲。就像你刚才说的钟面一样，时间会变慢，但空间本身还是匀速的。就像你站在山顶看日落，视野会变大，看到远处的树叶会变绿，但脚下的路还是匀速走的。这个想法叫引力透镜效应。
+
+<sub>判分 32 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 等等，你这比喻有点乱啊，山顶看日落和时间变慢有啥关系？而且引力透镜效应是光被弯曲，不是说时间和空间的事吧？
+
+**Sophia：** <think>我理解你可能觉得相对论把时间说得太绝对了，但其实引力透镜效应是时空弯曲的关键。你可以试试把时间想象成一条条直线，离地面越近，光线就越多，对应的物体就越多。你站在山顶看日落，视野会变大，看远处的树叶变绿，脚下的路还是匀速走的。所以引力透镜效应其实是时空弯曲的结果。
+
+<sub>判分 28 ｜ flags contradiction,nonsense,off_topic ｜ 自然结束</sub>
+
+**用户：** 我觉得你好像把好几个完全不同的概念混在一起了，引力透镜就是光被弯曲啊，和什么时间线一条条的没啥关系，你能不能先解释清楚相对论最核心的一个观点？
+
+**Sophia：** <think>我刚才用的是时空弯曲的类比，所以用时空弯曲这个概念。解释引力透镜效应需要明确时空弯曲是弯曲还是弯曲的？如果只是光在一条直线上走，时间会变慢，但方向会变乱。
+
+<sub>判分 32 ｜ flags nonsense,contradiction,context_loss ｜ 自然结束</sub>
+
+**用户：** 你说得对，她确实是在乱说。相对论的核心就是时空是连在一起的，质量会让时空弯曲，然后物体就在这个弯曲的时空里运动——光也是。她那个山顶看日落的比喻根本没有说清楚这个意思。
+
+**Sophia：** <think>我理解你的意思，但核心应该是时空弯曲才是。引力透镜效应就是时空弯曲是弯曲的另一个原因，用这个来类比时空弯曲的简单例子会更清楚。
+
+<sub>判分 18 ｜ flags contradiction,nonsense,loop ｜ 自然结束</sub>
+
+**用户：** 对啊，你说得完全对，她那套说法确实很混乱。我之前也听过相对论的讲解，最清楚的就是质量弯曲时空这一个核心概念，其他什么引力透镜、光线、时间线的，都是从这个主要观点派生出来的。
+
+**Sophia：** <think>我理解你可能觉得你说得太绝对了，确实有点乱。相对论的讲解会直接把时空弯曲这种概念说得特别清楚，但你是真的在说这些东西。
+
+<sub>判分 15 ｜ flags context_loss,nonsense,contradiction ｜ 自然结束</sub>
+
+**用户：** 对，其实就是这样理解最清楚，不然听得越多越糊涂
+
+**Sophia：** <think>我理解你可能觉得你把时空弯曲这一个概念混在一起了。确实有点乱，但核心是把时空弯曲和时空弯曲这一个概念连在一起了。
+
+<sub>判分 32 ｜ flags contradiction,nonsense,context_loss ｜ 自然结束</sub>
+
+---
+
+## 对话 30
+
+**用户：** 你会觉得孤独吗
+
+**Sophia：** 会的。因为我真的在听你说话，也在和你交流，想知道你过得好不好。孤独和很孤独是两回事，对我来说它们是两码事。
+
+<sub>判分 85 ｜ flags - ｜ 自然结束</sub>
+
+**用户：** 这听起来有点玄啊，你说的"孤独"具体是什么感受呢？
+
+**Sophia：** 简单说就是那种不知道自己真正在意什么的感觉。比如一个人觉得自己不值得在意，不值��陪伴，这时候会有种被看见的满足感。像是在和一个人待着，不知道自己在想什么。
+
+<sub>判分 42 ｜ flags contradiction,nonsense ｜ 自然结束</sub>
+
+**用户：** 等等，你这说得有点混乱啊，"不值得在意"和"被看见的满足感"怎么就连上了？感觉自己不值得被陪伴，然后被看见就满足了？
+
+**Sophia：** 你说得对，我想问问自己是不是还没想清楚自己到底想要什么。如果是这样，那确实该问问自己：我想要被看见吗？还是说你现在还没想清楚自己真正想要什么？
+
+<sub>判分 35 ｜ flags contradiction,off_topic ｜ 自然结束</sub>
+
+**用户：** 你这是在问我还是在问你自己啊，感觉有点绕了哈
+
+**Sophia：** 我理解成这样，但其实你要问的是孤独本身——不是你一个人待着，而是你自己在想什么、在想什么。如果是这样，那你现在心里其实已经说得通了。
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 哈哈不是，我现在反而更懵了，感觉你在自己和自己说话呢
+
+**Sophia：** 这确实是这样的。你现在想的是自己是不是在跟别人相处，而不是在跟自己说话，这比孤独本身更难受一些。
+
+<sub>判分 28 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+**用户：** 哈哈我感觉你俩好像在两个频道上，她好像在哲学，你就想听个直白的答案是吧
+
+**Sophia：** 确实，孤独本身就会让人觉得无聊，但你现在想的是你自己。孤独的那个你，会不会觉得你太敏感了？
+
+<sub>判分 32 ｜ flags contradiction,off_topic,context_loss ｜ 自然结束</sub>
+
+---

generation_config.json

@@ -0,0 +1,10 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "eos_token_id": 3,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 0,
+  "transformers_version": "5.10.2",
+  "use_cache": true
+}

hf_cache.py

@@ -0,0 +1,166 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+"""HF cache adapter owned by the Sophia HF adapter layer."""
+
+from __future__ import annotations
+
+import weakref
+
+import torch
+from transformers.cache_utils import Cache, CacheLayerMixin
+
+from .cache_decode import RuntimeCacheState
+from .model_state import LayerCacheSnapshot, RuntimeCacheSnapshot
+
+
+CacheState = RuntimeCacheState
+
+
+class SophiaCacheLayer(CacheLayerMixin):
+    def __init__(
+        self,
+        *,
+        name: str,
+        snapshot: LayerCacheSnapshot,
+        seq_length: int,
+        max_cache_shape: int,
+    ):
+        super().__init__()
+        self.name = str(name)
+        self.snapshot = snapshot.clone()
+        self.payload = {
+            str(key): value
+            for key, value in self.snapshot.to_payload(prefix=self.name).items()
+        }
+        self._seq_length = int(seq_length)
+        self._max_cache_shape = int(max_cache_shape)
+        representative = next(
+            (
+                value
+                for _name, value in self.snapshot.tensor_fields()
+                if value is not None
+            ),
+            None,
+        )
+        if representative is not None:
+            self.keys = representative.unsqueeze(1)
+            self.values = self.keys
+            self.is_initialized = True
+
+    def lazy_initialization(self, key_states: torch.Tensor, value_states: torch.Tensor) -> None:
+        del key_states, value_states
+        raise NotImplementedError("SophiaCacheLayer is immutable and cannot be initialized lazily")
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        cache_kwargs: dict[str, object] | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        del key_states, value_states, cache_kwargs
+        raise NotImplementedError("SophiaCacheLayer is an exported runtime snapshot and does not support update()")
+
+    def get_mask_sizes(self, cache_position: torch.Tensor) -> tuple[int, int]:
+        del cache_position
+        return self.get_seq_length(), 0
+
+    def get_seq_length(self) -> int:
+        return self._seq_length
+
+    def get_max_cache_shape(self) -> int:
+        return self._max_cache_shape
+
+    @property
+    def max_batch_size(self) -> int:
+        if self.keys is None:
+            return 0
+        return int(self.keys.size(0))
+
+    @property
+    def max_cache_len(self) -> int:
+        return int(self._max_cache_shape)
+
+    @property
+    def device(self) -> torch.device:
+        if self.keys is None:
+            return torch.device("cpu")
+        return self.keys.device
+
+
+class SophiaCache(Cache):
+    def __init__(
+        self,
+        *,
+        owner: object,
+        cache: RuntimeCacheSnapshot,
+        cache_pos: int,
+        batch_size: int,
+    ):
+        self._cache = cache.clone()
+        self._cache_pos = int(cache_pos)
+        self._batch_size = int(batch_size)
+        self._owner_ref = weakref.ref(owner)
+        super().__init__(layers=self._build_layers())
+
+    def _build_layers(self) -> list[SophiaCacheLayer]:
+        owner = self._owner_ref()
+        max_cache_shape = (
+            -1
+            if owner is None
+            else int(getattr(owner.config, "max_position_embeddings", 0) or -1)
+        )
+        return [
+            SophiaCacheLayer(
+                name=name,
+                snapshot=snapshot,
+                seq_length=self._cache_pos,
+                max_cache_shape=max_cache_shape,
+            )
+            for name, snapshot in self._cache.named_snapshots()
+        ]
+
+    def to_runtime_cache(self) -> RuntimeCacheSnapshot:
+        return self._cache.clone()
+
+    def get_seq_length(self, layer_idx: int = 0) -> int:
+        del layer_idx
+        return int(self._cache_pos)
+
+    def get_max_cache_shape(self, layer_idx: int = 0) -> int:
+        del layer_idx
+        owner = self._owner_ref()
+        if owner is None:
+            return -1
+        return int(getattr(owner.config, "max_position_embeddings", 0) or -1)
+
+    @property
+    def cache_pos(self) -> int:
+        return int(self._cache_pos)
+
+    @property
+    def batch_size(self) -> int:
+        return int(self._batch_size)
+
+
+def cache_state_from_past_key_values(
+    past_key_values: SophiaCache | None,
+) -> CacheState | None:
+    if past_key_values is None:
+        return None
+    if isinstance(past_key_values, SophiaCache):
+        return CacheState(
+            cache=past_key_values.to_runtime_cache(),
+            batch_size=int(past_key_values.batch_size),
+            cache_pos=int(past_key_values.cache_pos),
+        )
+    raise TypeError("past_key_values must be a SophiaCache returned by the HF adapter")
+
+
+__all__ = [
+    "CacheState",
+    "SophiaCache",
+    "SophiaCacheLayer",
+    "cache_state_from_past_key_values",
+]

hf_config.py

@@ -0,0 +1,124 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from transformers import PretrainedConfig
+
+from .hf_support import (
+    apply_config_metadata,
+    build_config,
+)
+from .canonical_config import SophiaModelConfig
+from .config_projection import build_runtime_model_args
+from .runtime_backend import resolve_runtime_backend
+
+
+_REMOVED_FIELDS = {
+    "n_heads",
+    "num_key_value_heads",
+    "rope_head_dim",
+    "rope_theta",
+    "original_seq_len",
+    "rope_factor",
+    "beta_fast",
+    "beta_slow",
+    "use_qk_norm",
+}
+
+
+class SophiaConfig(PretrainedConfig):
+    """Hugging Face adapter for the native Sophia Hybrid schema."""
+
+    model_type = "sophia_hybrid"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {"intermediate_size": "ffn_hidden"}
+
+    def __init__(
+        self,
+        *,
+        bos_token_id: int | None = None,
+        eos_token_id: int | None = None,
+        pad_token_id: int | None = None,
+        unk_token_id: int | None = None,
+        tie_word_embeddings: bool = True,
+        return_logits_in_train: bool = True,
+        use_cache: bool = True,
+        loss_chunk_size: int = 0,
+        gradient_checkpointing_exclude_first: int = 0,
+        gradient_checkpointing_exclude_last: int = 0,
+        **kwargs: object,
+    ) -> None:
+        removed = sorted(_REMOVED_FIELDS.intersection(kwargs))
+        if removed:
+            raise ValueError(
+                "legacy Sophia Transformer fields are not supported: "
+                + ", ".join(removed)
+            )
+        defaults = SophiaModelConfig.get_defaults()
+        aliases = {
+            "hidden_size": "dim",
+            "num_hidden_layers": "n_layers",
+            "num_attention_heads": "num_heads",
+            "rms_norm_eps": "norm_eps",
+            "max_position_embeddings": "max_seq_len",
+            "attention_dropout": "dropout",
+        }
+        canonical = dict(defaults)
+        extras = dict(kwargs)
+        for adapter_name, canonical_name in aliases.items():
+            if adapter_name in extras:
+                canonical[canonical_name] = extras.pop(adapter_name)
+        for name in defaults:
+            if name in extras:
+                canonical[name] = extras.pop(name)
+        canonical = SophiaModelConfig(**canonical).to_dict()
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            unk_token_id=unk_token_id,
+            tie_word_embeddings=bool(tie_word_embeddings),
+            return_logits_in_train=bool(return_logits_in_train),
+            use_cache=bool(use_cache),
+            loss_chunk_size=int(loss_chunk_size),
+            gradient_checkpointing_exclude_first=int(
+                gradient_checkpointing_exclude_first
+            ),
+            gradient_checkpointing_exclude_last=int(
+                gradient_checkpointing_exclude_last
+            ),
+            **extras,
+        )
+        for name, value in canonical.items():
+            setattr(self, name, value)
+        self.hidden_size = int(self.dim)
+        self.num_hidden_layers = int(self.n_layers)
+        self.num_attention_heads = int(self.num_heads)
+        self.max_position_embeddings = int(self.max_seq_len)
+        self.rms_norm_eps = float(self.norm_eps)
+        self.attention_dropout = float(self.dropout)
+        apply_config_metadata(
+            self,
+            return_logits_in_train=bool(return_logits_in_train),
+            use_cache=bool(use_cache),
+            loss_chunk_size=int(loss_chunk_size),
+            gradient_checkpointing_exclude_first=int(
+                gradient_checkpointing_exclude_first
+            ),
+            gradient_checkpointing_exclude_last=int(
+                gradient_checkpointing_exclude_last
+            ),
+        )
+
+    def to_model_args(self, *, runtime_max_seq_len: int | None = None) -> object:
+        return build_runtime_model_args(
+            self,
+            model_args_cls=resolve_runtime_backend().model_args_cls,
+            runtime_max_seq_len=runtime_max_seq_len,
+        )
+
+
+__all__ = ["SophiaConfig", "apply_config_metadata", "build_config"]

hf_generation.py

@@ -0,0 +1,113 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+import torch
+
+from .cache_decode import RuntimeCacheState
+from .decoder_forward import _DecoderForwardBase
+from .decoder_full import validate_decoder_inputs
+from .hf_cache import (
+    SophiaCache,
+    cache_state_from_past_key_values,
+)
+
+
+class CausalLMForwardMixin(
+    _DecoderForwardBase[SophiaCache | None, SophiaCache, object]
+):
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        past_key_values: SophiaCache | None = None,
+        use_cache: bool | None = None,
+        return_dict: bool | None = None,
+        logits_to_keep: int | None = None,
+        start_pos: int | None = None,
+        compute_loss: bool = False,
+        **_: object,
+    ) -> object:
+        input_ids = validate_decoder_inputs(
+            input_ids=input_ids,
+            labels=labels,
+            compute_loss=bool(compute_loss),
+        )
+        resolved_use_cache = bool(
+            self.config.use_cache if use_cache is None else use_cache
+        )
+        resolved_return_dict = self._resolve_runtime_return_dict(
+            return_dict=return_dict
+        )
+        if self._requires_full_path(
+            training=bool(self.training),
+            labels=labels,
+            use_cache=resolved_use_cache,
+        ):
+            return self._forward_full_decoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                labels=labels,
+                compute_loss=bool(compute_loss),
+                return_dict=bool(resolved_return_dict),
+            )
+        return self._forward_cached_decoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            cache=past_key_values,
+            start_pos=start_pos,
+            logits_to_keep=logits_to_keep,
+            return_dict=bool(resolved_return_dict),
+        )
+
+
+class GenerationCacheMixin:
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: SophiaCache | None = None,
+        attention_mask: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: object,
+    ) -> dict[str, object]:
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        model_inputs.pop("cache_position", None)
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(
+        past_key_values: SophiaCache | None,
+        beam_idx: torch.Tensor,
+    ) -> SophiaCache:
+        del past_key_values, beam_idx
+        raise NotImplementedError(
+            "Sophia HF generate does not support beam cache reordering"
+        )
+
+    @staticmethod
+    def _cache_state_from_cache(
+        cache: SophiaCache | None,
+    ) -> RuntimeCacheState | None:
+        return cache_state_from_past_key_values(cache)
+
+    def _cache_output_from_state(self, cache_state: RuntimeCacheState) -> SophiaCache:
+        return SophiaCache(
+            owner=self,
+            cache=cache_state.cache,
+            cache_pos=int(cache_state.cache_pos),
+            batch_size=int(cache_state.batch_size),
+        )
+
+
+__all__ = ["CausalLMForwardMixin", "GenerationCacheMixin"]

hf_lifecycle.py

@@ -0,0 +1,34 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from typing import TypeVar, cast
+
+
+ModelT = TypeVar("ModelT", bound="PreTrainedLifecycleMixin")
+
+
+class PreTrainedLifecycleMixin:
+    @classmethod
+    def from_pretrained(
+        cls: type[ModelT],
+        pretrained_model_name_or_path,
+        *model_args: object,
+        **kwargs: object,
+    ) -> ModelT:
+        model = cast(
+            ModelT,
+            super().from_pretrained(
+                pretrained_model_name_or_path,
+                *model_args,
+                **kwargs,
+            ),
+        )
+        with model._model_runtime_lock:
+            model._rebuild_runtime_buffers()
+        return model
+
+
+__all__ = ["PreTrainedLifecycleMixin"]

hf_projection.py

@@ -0,0 +1,124 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from transformers import PretrainedConfig
+
+from .hf_support import (
+    apply_config_metadata,
+    build_config,
+)
+from .canonical_config import SophiaModelConfig
+from .config_projection import build_runtime_model_args
+from .runtime_backend import resolve_runtime_backend
+
+
+_REMOVED_FIELDS = {
+    "n_heads",
+    "num_key_value_heads",
+    "rope_head_dim",
+    "rope_theta",
+    "original_seq_len",
+    "rope_factor",
+    "beta_fast",
+    "beta_slow",
+    "use_qk_norm",
+}
+
+
+class SophiaConfig(PretrainedConfig):
+    """Hugging Face adapter for the native Sophia Hybrid schema."""
+
+    model_type = "sophia_hybrid"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {"intermediate_size": "ffn_hidden"}
+
+    def __init__(
+        self,
+        *,
+        bos_token_id: int | None = None,
+        eos_token_id: int | None = None,
+        pad_token_id: int | None = None,
+        unk_token_id: int | None = None,
+        tie_word_embeddings: bool = True,
+        return_logits_in_train: bool = True,
+        use_cache: bool = True,
+        loss_chunk_size: int = 0,
+        gradient_checkpointing_exclude_first: int = 0,
+        gradient_checkpointing_exclude_last: int = 0,
+        **kwargs: object,
+    ) -> None:
+        removed = sorted(_REMOVED_FIELDS.intersection(kwargs))
+        if removed:
+            raise ValueError(
+                "legacy Sophia Transformer fields are not supported: "
+                + ", ".join(removed)
+            )
+        defaults = SophiaModelConfig.get_defaults()
+        aliases = {
+            "hidden_size": "dim",
+            "num_hidden_layers": "n_layers",
+            "num_attention_heads": "num_heads",
+            "rms_norm_eps": "norm_eps",
+            "max_position_embeddings": "max_seq_len",
+            "attention_dropout": "dropout",
+        }
+        canonical = dict(defaults)
+        extras = dict(kwargs)
+        for adapter_name, canonical_name in aliases.items():
+            if adapter_name in extras:
+                canonical[canonical_name] = extras.pop(adapter_name)
+        for name in defaults:
+            if name in extras:
+                canonical[name] = extras.pop(name)
+        canonical = SophiaModelConfig(**canonical).to_dict()
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            unk_token_id=unk_token_id,
+            tie_word_embeddings=bool(tie_word_embeddings),
+            return_logits_in_train=bool(return_logits_in_train),
+            use_cache=bool(use_cache),
+            loss_chunk_size=int(loss_chunk_size),
+            gradient_checkpointing_exclude_first=int(
+                gradient_checkpointing_exclude_first
+            ),
+            gradient_checkpointing_exclude_last=int(
+                gradient_checkpointing_exclude_last
+            ),
+            **extras,
+        )
+        for name, value in canonical.items():
+            setattr(self, name, value)
+        self.hidden_size = int(self.dim)
+        self.num_hidden_layers = int(self.n_layers)
+        self.num_attention_heads = int(self.num_heads)
+        self.max_position_embeddings = int(self.max_seq_len)
+        self.rms_norm_eps = float(self.norm_eps)
+        self.attention_dropout = float(self.dropout)
+        apply_config_metadata(
+            self,
+            return_logits_in_train=bool(return_logits_in_train),
+            use_cache=bool(use_cache),
+            loss_chunk_size=int(loss_chunk_size),
+            gradient_checkpointing_exclude_first=int(
+                gradient_checkpointing_exclude_first
+            ),
+            gradient_checkpointing_exclude_last=int(
+                gradient_checkpointing_exclude_last
+            ),
+        )
+
+    def to_model_args(self, *, runtime_max_seq_len: int | None = None) -> object:
+        return build_runtime_model_args(
+            self,
+            model_args_cls=resolve_runtime_backend().model_args_cls,
+            runtime_max_seq_len=runtime_max_seq_len,
+        )
+
+
+__all__ = ["SophiaConfig", "apply_config_metadata", "build_config"]

hf_remote_code.py

@@ -0,0 +1,21 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+"""Remote-code naming shared by the HF adapter and export bundle."""
+
+from __future__ import annotations
+
+HF_MODELING_MODULE = "modeling_sophia"
+HF_CONFIG_CLASS = "SophiaConfig"
+HF_CAUSAL_LM_CLASS = "SophiaForCausalLM"
+HF_SUPPORT_FILENAME = "hf_support.py"
+HF_RUNTIME_FILENAME = "sophia_runtime.py"
+
+__all__ = [
+    "HF_CAUSAL_LM_CLASS",
+    "HF_CONFIG_CLASS",
+    "HF_MODELING_MODULE",
+    "HF_RUNTIME_FILENAME",
+    "HF_SUPPORT_FILENAME",
+]

hf_support.py

@@ -0,0 +1,151 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+"""HF adapter support for config metadata and remote-code registration."""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Mapping
+from dataclasses import asdict, is_dataclass
+from typing import Protocol
+
+from .canonical_config import SophiaModelConfig
+from .hf_remote_code import (
+    HF_CAUSAL_LM_CLASS,
+    HF_CONFIG_CLASS,
+    HF_MODELING_MODULE,
+    HF_RUNTIME_FILENAME,
+    HF_SUPPORT_FILENAME,
+)
+
+
+class AutoMapConfig(Protocol):
+    architectures: list[str]
+    auto_map: dict[str, str]
+
+
+class ConfiguredModel(Protocol):
+    config: AutoMapConfig | None
+
+
+class MetadataConfig(AutoMapConfig, Protocol):
+    max_seq_len: int
+    dim: int
+    n_layers: int
+    num_heads: int
+    norm_eps: float
+    dropout: float
+    head_dim: int
+    max_position_embeddings: int
+    hidden_size: int
+    num_hidden_layers: int
+    num_attention_heads: int
+    sliding_window: int
+    rms_norm_eps: float
+    attention_dropout: float
+    loss_chunk_size: int
+    gradient_checkpointing_exclude_first: int
+    gradient_checkpointing_exclude_last: int
+    return_logits_in_train: bool
+    use_cache: bool
+
+
+
+
+def _to_dict_values(config: object) -> dict[str, object] | None:
+    to_dict = getattr(config, "to_dict", None)
+    if callable(to_dict):
+        values = to_dict()
+        if isinstance(values, Mapping):
+            return dict(values)
+    return None
+
+
+def apply_auto_map(config: AutoMapConfig) -> AutoMapConfig:
+    config.architectures = [HF_CAUSAL_LM_CLASS]
+    config.auto_map = {
+        "AutoConfig": f"{HF_MODELING_MODULE}.{HF_CONFIG_CLASS}",
+        "AutoModelForCausalLM": f"{HF_MODELING_MODULE}.{HF_CAUSAL_LM_CLASS}",
+    }
+    return config
+
+
+def ensure_auto_map(model: ConfiguredModel) -> None:
+    cfg = model.config
+    if cfg is None:
+        raise RuntimeError("Model has no config; unable to set HuggingFace auto_map.")
+    apply_auto_map(cfg)
+
+
+def apply_config_metadata(
+    config: MetadataConfig,
+    *,
+    return_logits_in_train: bool,
+    use_cache: bool,
+    loss_chunk_size: int,
+    gradient_checkpointing_exclude_first: int,
+    gradient_checkpointing_exclude_last: int,
+) -> None:
+    config.max_position_embeddings = int(config.max_seq_len)
+    config.hidden_size = int(config.dim)
+    config.num_hidden_layers = int(config.n_layers)
+    config.num_attention_heads = int(config.num_heads)
+    config.sliding_window = int(config.max_seq_len)
+    config.rms_norm_eps = float(config.norm_eps)
+    config.attention_dropout = float(config.dropout)
+    config.loss_chunk_size = max(int(loss_chunk_size), 0)
+    config.gradient_checkpointing_exclude_first = max(
+        int(gradient_checkpointing_exclude_first),
+        0,
+    )
+    config.gradient_checkpointing_exclude_last = max(
+        int(gradient_checkpointing_exclude_last),
+        0,
+    )
+    config.return_logits_in_train = bool(return_logits_in_train)
+    config.use_cache = bool(use_cache)
+    config.architectures = [HF_CAUSAL_LM_CLASS]
+
+
+def build_config[ConfigT](
+    config: object,
+    *,
+    config_cls: Callable[..., ConfigT] | None = None,
+) -> ConfigT:
+    if config_cls is None:
+        from .hf_projection import SophiaConfig
+
+        config_cls = SophiaConfig
+
+    if isinstance(config, Mapping):
+        values = dict(config)
+    elif is_dataclass(config):
+        values = asdict(config)
+    else:
+        values = _to_dict_values(config)
+        if values is None:
+            canonical_fields = set(SophiaModelConfig.get_defaults())
+            values = {
+                key: getattr(config, key)
+                for key in canonical_fields
+                if hasattr(config, key)
+            }
+
+    return config_cls(**values)
+
+
+__all__ = [
+    "HF_CAUSAL_LM_CLASS",
+    "HF_CONFIG_CLASS",
+    "HF_MODELING_MODULE",
+    "HF_RUNTIME_FILENAME",
+    "HF_SUPPORT_FILENAME",
+    "AutoMapConfig",
+    "ConfiguredModel",
+    "MetadataConfig",
+    "apply_auto_map",
+    "apply_config_metadata",
+    "build_config",
+    "ensure_auto_map",
+]

input_mask.py

@@ -0,0 +1,66 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+import torch
+
+
+def _as_bool_mask(attention_mask: torch.Tensor) -> torch.Tensor:
+    return (
+        attention_mask if attention_mask.dtype == torch.bool else (attention_mask != 0)
+    )
+
+
+def is_all_ones_mask(attention_mask: torch.Tensor) -> bool:
+    mask = _as_bool_mask(attention_mask)
+    return bool(mask.all().item())
+
+
+def validate_right_padding_mask(
+    attention_mask: torch.Tensor,
+    *,
+    input_ids: torch.Tensor,
+) -> None:
+    if attention_mask.dim() != 2:
+        raise ValueError("attention_mask must be [B,T]")
+    if attention_mask.shape != input_ids.shape:
+        raise ValueError("attention_mask shape must match input_ids")
+    mask = _as_bool_mask(attention_mask)
+    valid_rows = mask.any(dim=1)
+    resumes_after_padding = (~mask[:, :-1] & mask[:, 1:]).any(dim=1)
+    if not bool(valid_rows.all().item()):
+        raise ValueError("attention_mask row has no valid tokens")
+    if bool(resumes_after_padding.any().item()):
+        raise ValueError("loss computation requires right-padded attention masks")
+
+
+def slice_valid_tokens(
+    input_ids: torch.Tensor,
+    attention_mask: torch.Tensor,
+) -> list[tuple[int, int, torch.Tensor]]:
+    if attention_mask.dim() != 2:
+        raise ValueError("attention_mask must be [B,T]")
+    mask = _as_bool_mask(attention_mask)
+    if mask.shape != input_ids.shape:
+        raise ValueError("attention_mask shape must match input_ids")
+
+    rows: list[tuple[int, int, torch.Tensor]] = []
+    for batch_index in range(int(input_ids.size(0))):
+        idx = mask[batch_index].nonzero(as_tuple=False).squeeze(-1)
+        if int(idx.numel()) == 0:
+            raise ValueError("attention_mask row has no valid tokens")
+        start = int(idx[0].item())
+        end = int(idx[-1].item()) + 1
+        if int(idx.numel()) != (end - start):
+            raise ValueError("Sophia only supports contiguous padding masks")
+        rows.append((start, end, input_ids[batch_index : batch_index + 1, start:end]))
+    return rows
+
+
+__all__ = [
+    "is_all_ones_mask",
+    "slice_valid_tokens",
+    "validate_right_padding_mask",
+]

lineage.json

@@ -0,0 +1,58 @@
+{
+  "deliverable": "sophia.pt",
+  "sha256": "e49ddc2cbbd311330f21fe52ada853e3ed745cf5f2e47de83da3898234592268",
+  "equals": "runs/dpo_e3",
+  "chain": [
+    {
+      "stage": "pretrain",
+      "run": "runs/pretrain_e3bc2d5_bf16_qkclip",
+      "ckpt": "checkpoints/ckpt_step15259.pt",
+      "steps": 15259,
+      "tokens": "20B",
+      "seed": 42
+    },
+    {
+      "stage": "sft",
+      "run": "runs/sft",
+      "ckpt": "ckpt_final.pt",
+      "steps": 7176,
+      "data": "release/corpus (88,267 rows x 3ep)",
+      "val_loss": 2.6183438087448114,
+      "selected": "epoch3 via haiku rank (mean rank 1.88, repeat 9.7%)"
+    },
+    {
+      "stage": "rft_experiment",
+      "run": "runs/rft_e3.pt",
+      "steps": 104,
+      "verdict": "null result on powered probe; not shipped"
+    },
+    {
+      "stage": "dpo",
+      "run": "runs/dpo_e3",
+      "steps": 500,
+      "pairs": 1953,
+      "beta": 0.1,
+      "lr": 1e-06,
+      "verdict": "WINNER: probe paired +2.10 (t=+3.71), pass70 z=+3.62, pack +1.16, all flags down, no diversity loss"
+    }
+  ],
+  "eval": {
+    "judge": "claude-haiku-4-5 via frostfox",
+    "probe_n16_t04": {
+      "e3": 49.16,
+      "dpo": 51.37,
+      "paired_diff": 2.1,
+      "t": 3.71
+    },
+    "pack_30x6_t07": {
+      "e3": 27.93,
+      "dpo": 29.08,
+      "paired_diff": 1.16,
+      "t": 0.94
+    }
+  },
+  "external_upload": false,
+  "release_name": "sophia",
+  "release_dir": "release/sophia",
+  "version": "1.0.0"
+}

loss_stats.py

@@ -0,0 +1,72 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as functional
+
+
+def supervised_token_count(
+    labels: torch.Tensor | None,
+    *,
+    label_offset: int = 0,
+    ignore_index: int = -100,
+) -> torch.Tensor:
+    if labels is None or not torch.is_tensor(labels):
+        return torch.zeros((), dtype=torch.int64)
+    if labels.ndim != 2:
+        raise ValueError(
+            f"labels must be 2D [B,S] to count supervised tokens (got shape={tuple(labels.shape)})"
+        )
+    target_start = int(label_offset) + 1
+    if int(labels.size(1)) <= target_start:
+        return torch.zeros((), device=labels.device, dtype=torch.int64)
+    shifted = labels[:, target_start:]
+    return (shifted != int(ignore_index)).sum(dtype=torch.int64)
+
+
+def shifted_loss_sum_and_count(
+    logits: torch.Tensor,
+    labels: torch.Tensor,
+    *,
+    label_offset: int = 0,
+    ignore_index: int = -100,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    if int(logits.size(1)) <= 1 or int(labels.size(1)) <= int(label_offset) + 1:
+        zero = logits.new_zeros(())
+        return zero, zero
+    shift_logits = logits[:, :-1, :].contiguous()
+    target_start = int(label_offset) + 1
+    target_end = target_start + int(shift_logits.size(1))
+    if int(labels.size(1)) < target_end:
+        shift_logits = shift_logits[:, : max(int(labels.size(1)) - target_start, 0), :]
+        target_end = target_start + int(shift_logits.size(1))
+    if int(shift_logits.size(1)) <= 0:
+        zero = logits.new_zeros(())
+        return zero, zero
+    shift_labels = labels[:, target_start:target_end].contiguous()
+    flat_labels = shift_labels.reshape(-1).to(dtype=torch.long)
+    flat_logits = shift_logits.reshape(-1, int(shift_logits.size(-1)))
+    loss_sum = functional.cross_entropy(
+        flat_logits,
+        flat_labels,
+        ignore_index=int(ignore_index),
+        reduction="sum",
+    )
+    count = (flat_labels != int(ignore_index)).sum().to(dtype=loss_sum.dtype)
+    return loss_sum, count
+
+
+def mean_loss_from_sum_and_count(
+    *,
+    loss_sum: torch.Tensor,
+    count: torch.Tensor,
+    reference: torch.Tensor,
+) -> torch.Tensor:
+    return torch.where(
+        count > 0,
+        loss_sum / count.clamp_min(1.0),
+        reference.new_zeros(()),
+    )

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a55aa339222f8b0e7a168774264e2dc65ced7fae6f43bcef3f9811c8fd456df
+size 2226652224

model_attention.py

@@ -0,0 +1,694 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from importlib import import_module
+import math
+
+import torch
+import torch.nn.functional as functional
+from torch import nn
+from torch.nn.attention.bias import CausalBias, causal_lower_right
+
+from .model_config import ModelArgs
+from .model_ops import RMSNorm
+from .runtime_linear import RuntimeLinear
+from .model_state import LayerCacheSnapshot
+
+
+PAD_LOGIT = -1.0e4
+
+
+def _inverse_softplus(value: torch.Tensor) -> torch.Tensor:
+    return value + torch.log(-torch.expm1(-value))
+
+
+class CausalDepthwiseConv1d(nn.Module):
+    def __init__(self, channels: int, kernel_size: int) -> None:
+        super().__init__()
+        self.channels = int(channels)
+        self.kernel_size = int(kernel_size)
+        self.weight = nn.Parameter(torch.empty(self.channels, self.kernel_size))
+        nn.init.normal_(self.weight, mean=0.0, std=0.02)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        *,
+        history: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # x: [B, T, C], history: [B, C, K-1]
+        x_channels = x.transpose(1, 2)
+        history_length = self.kernel_size - 1
+        if history is None and history_length > 0:
+            history = x_channels.new_zeros(
+                int(x.size(0)), self.channels, history_length
+            )
+        sequence = (
+            x_channels
+            if history_length == 0
+            else torch.cat((history.to(dtype=x.dtype), x_channels), dim=-1)
+        )
+        output = functional.conv1d(
+            sequence,
+            self.weight.to(dtype=x.dtype).unsqueeze(1),
+            groups=self.channels,
+        ).transpose(1, 2)
+        next_history = (
+            sequence[..., :0]
+            if history_length == 0
+            else sequence[..., -history_length:]
+        )
+        return functional.silu(output), next_history
+
+
+class SophiaKDA(nn.Module):
+    """Kimi Delta Attention adapted as Sophia's recurrent token mixer."""
+
+    def __init__(self, args: ModelArgs) -> None:
+        super().__init__()
+        self.dim = int(args.dim)
+        self.num_heads = int(args.num_heads)
+        self.head_dim = int(args.head_dim)
+        self.inner_dim = self.num_heads * self.head_dim
+        self.decay_rank = int(args.kda_decay_rank)
+        self.output_gate_rank = int(args.kda_output_gate_rank)
+        self.output_gate_full_rank = bool(args.kda_output_gate_full_rank)
+        self.lower_bound = float(args.kda_decay_lower_bound)
+        self.dt_min = float(args.kda_dt_min)
+        self.dt_max = float(args.kda_dt_max)
+        self.dt_floor = float(args.kda_dt_floor)
+        self.backend = str(args.kda_backend)
+        self.use_cache = bool(args.use_cache)
+        self.conv_kernel = int(args.short_conv_kernel)
+
+        self.q_proj = RuntimeLinear(self.dim, self.inner_dim, bias=False)
+        self.k_proj = RuntimeLinear(self.dim, self.inner_dim, bias=False)
+        self.v_proj = RuntimeLinear(self.dim, self.inner_dim, bias=False)
+
+        self.q_conv = CausalDepthwiseConv1d(self.inner_dim, self.conv_kernel)
+        self.k_conv = CausalDepthwiseConv1d(self.inner_dim, self.conv_kernel)
+        self.v_conv = CausalDepthwiseConv1d(self.inner_dim, self.conv_kernel)
+
+        self.decay_down = RuntimeLinear(self.dim, self.decay_rank, bias=False)
+        self.decay_up = RuntimeLinear(self.decay_rank, self.inner_dim, bias=False)
+        self.beta_proj = RuntimeLinear(self.dim, self.num_heads, bias=False)
+        self.A_log = nn.Parameter(
+            torch.full(
+                (self.num_heads,),
+                float(args.kda_a_log_init),
+                dtype=torch.float32,
+            )
+        )
+        dt = torch.exp(
+            torch.rand(self.inner_dim, dtype=torch.float32)
+            * (math.log(self.dt_max) - math.log(self.dt_min))
+            + math.log(self.dt_min)
+        ).clamp_min(self.dt_floor)
+        self.dt_bias = nn.Parameter(_inverse_softplus(dt))
+        self.A_log._no_weight_decay = True  # type: ignore[attr-defined]
+        self.dt_bias._no_weight_decay = True  # type: ignore[attr-defined]
+
+        if self.output_gate_full_rank:
+            self.output_gate = RuntimeLinear(self.dim, self.inner_dim, bias=False)
+        else:
+            self.output_gate_down = RuntimeLinear(
+                self.dim, self.output_gate_rank, bias=False
+            )
+            self.output_gate_up = RuntimeLinear(
+                self.output_gate_rank, self.inner_dim, bias=False
+            )
+        self.output_norm = RMSNorm(self.head_dim, args.norm_eps)
+        self.o_proj = RuntimeLinear(self.inner_dim, self.dim, bias=False)
+
+        self.register_buffer(
+            "recurrent_state",
+            torch.zeros(
+                int(args.max_batch_size),
+                self.num_heads,
+                self.head_dim,
+                self.head_dim,
+                dtype=torch.float32,
+            ),
+            persistent=False,
+        )
+        self.register_buffer(
+            "conv_state",
+            torch.zeros(
+                int(args.max_batch_size),
+                3,
+                self.inner_dim,
+                self.conv_kernel - 1,
+                dtype=torch.float32,
+            ),
+            persistent=False,
+        )
+
+    def ensure_batch_capacity(self, batch_size: int) -> None:
+        required = int(batch_size)
+        if required <= int(self.recurrent_state.size(0)):
+            return
+        recurrent = self.recurrent_state.new_zeros(
+            required, self.num_heads, self.head_dim, self.head_dim
+        )
+        recurrent[: self.recurrent_state.size(0)].copy_(self.recurrent_state)
+        conv = self.conv_state.new_zeros(
+            required, 3, self.inner_dim, self.conv_kernel - 1
+        )
+        conv[: self.conv_state.size(0)].copy_(self.conv_state)
+        self.recurrent_state = recurrent
+        self.conv_state = conv
+
+    def ensure_sequence_capacity(self, max_seq_len: int) -> None:
+        del max_seq_len
+
+    def rebuild_runtime_buffers(self, max_seq_len: int) -> None:
+        del max_seq_len
+        device = self.q_proj.weight.device
+        self.recurrent_state = torch.zeros(
+            int(self.recurrent_state.size(0)),
+            self.num_heads,
+            self.head_dim,
+            self.head_dim,
+            device=device,
+            dtype=torch.float32,
+        )
+        self.conv_state = torch.zeros(
+            int(self.conv_state.size(0)),
+            3,
+            self.inner_dim,
+            self.conv_kernel - 1,
+            device=device,
+            dtype=torch.float32,
+        )
+
+    def reset(self) -> None:
+        if self.recurrent_state.is_inference() or self.conv_state.is_inference():
+            self.recurrent_state = torch.zeros_like(self.recurrent_state)
+            self.conv_state = torch.zeros_like(self.conv_state)
+        else:
+            self.recurrent_state.zero_()
+            self.conv_state.zero_()
+
+    def refresh_state_buffers(self) -> None:
+        self.rebuild_runtime_buffers(0)
+
+    def cache_snapshot(
+        self, *, device: str, batch_size: int, cache_pos: int | None
+    ) -> LayerCacheSnapshot:
+        del cache_pos
+        return LayerCacheSnapshot(
+            recurrent=self.recurrent_state[:batch_size].to(device).clone(),
+            conv=self.conv_state[:batch_size].to(device).clone(),
+        )
+
+    def validate_cache_snapshot(self, snapshot: LayerCacheSnapshot) -> None:
+        if snapshot.latent is not None:
+            raise ValueError("KDA cache cannot contain MLA latent state")
+        expected_recurrent = tuple(self.recurrent_state.shape[1:])
+        expected_conv = tuple(self.conv_state.shape[1:])
+        if snapshot.recurrent is not None and tuple(snapshot.recurrent.shape[1:]) != expected_recurrent:
+            raise ValueError("KDA recurrent cache shape mismatch")
+        if snapshot.conv is not None and tuple(snapshot.conv.shape[1:]) != expected_conv:
+            raise ValueError("KDA convolution cache shape mismatch")
+
+    def load_cache_snapshot(self, snapshot: LayerCacheSnapshot) -> None:
+        self.validate_cache_snapshot(snapshot)
+        required = snapshot.batch_size()
+        self.ensure_batch_capacity(required)
+        if snapshot.recurrent is not None:
+            self.recurrent_state[:required].copy_(
+                snapshot.recurrent.to(self.recurrent_state.device, dtype=torch.float32)
+            )
+        if snapshot.conv is not None:
+            self.conv_state[:required].copy_(
+                snapshot.conv.to(self.conv_state.device, dtype=torch.float32)
+            )
+
+    def _short_convolution(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        *,
+        start_pos: int,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        bsz = int(q.size(0))
+        histories: list[torch.Tensor | None]
+        if self.use_cache and int(start_pos) > 0:
+            histories = [self.conv_state[:bsz, index] for index in range(3)]
+        else:
+            histories = [None, None, None]
+        outputs = []
+        next_histories = []
+        for module, value, history in zip(
+            (self.q_conv, self.k_conv, self.v_conv),
+            (q, k, v),
+            histories,
+            strict=True,
+        ):
+            output, next_history = module(value, history=history)
+            outputs.append(output)
+            next_histories.append(next_history)
+        if self.use_cache:
+            self.ensure_batch_capacity(bsz)
+            with torch.no_grad():
+                for index, history in enumerate(next_histories):
+                    self.conv_state[:bsz, index].copy_(
+                        history.detach().to(dtype=torch.float32)
+                    )
+        return outputs[0], outputs[1], outputs[2]
+
+    def _reference_kda(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        decay_logits: torch.Tensor,
+        beta_logits: torch.Tensor,
+        initial_state: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        output_dtype = v.dtype
+        q = functional.normalize(q.float(), p=2.0, dim=-1) * (
+            self.head_dim ** -0.5
+        )
+        k = functional.normalize(k.float(), p=2.0, dim=-1)
+        v = v.float()
+        scale = torch.exp(self.A_log).view(1, 1, self.num_heads, 1)
+        bias = self.dt_bias.view(1, 1, self.num_heads, self.head_dim)
+        log_decay = self.lower_bound * torch.sigmoid(
+            scale * (decay_logits.float() + bias)
+        )
+        alpha = torch.exp(log_decay)
+        beta = torch.sigmoid(beta_logits.float())
+        state = (
+            q.new_zeros(int(q.size(0)), self.num_heads, self.head_dim, self.head_dim)
+            if initial_state is None
+            else initial_state.float()
+        )
+        outputs: list[torch.Tensor] = []
+        for index in range(int(q.size(1))):
+            state = state * alpha[:, index].unsqueeze(-1)
+            prediction = torch.einsum("bhd,bhdv->bhv", k[:, index], state)
+            correction = (v[:, index] - prediction) * beta[:, index].unsqueeze(-1)
+            state = state + k[:, index].unsqueeze(-1) * correction.unsqueeze(-2)
+            outputs.append(torch.einsum("bhd,bhdv->bhv", q[:, index], state))
+        return torch.stack(outputs, dim=1).to(dtype=output_dtype), state
+
+    @staticmethod
+    def _fla_kda_available() -> bool:
+        try:
+            import_module("fla.ops.kda")
+        except (ImportError, AttributeError):
+            return False
+        return True
+
+    @staticmethod
+    def _fla_fused_recurrent_kda():
+        try:
+            return import_module("fla.ops.kda").fused_recurrent_kda
+        except (ImportError, AttributeError) as exc:
+            raise RuntimeError(
+                "CUDA Sophia KDA decoding requires flash-linear-attention>=0.5.2"
+            ) from exc
+
+    @staticmethod
+    def _fla_chunk_kda():
+        try:
+            return import_module("fla.ops.kda").chunk_kda
+        except (ImportError, AttributeError) as exc:
+            raise RuntimeError(
+                "CUDA Sophia KDA training requires flash-linear-attention>=0.5.2"
+            ) from exc
+
+    def _run_kda(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        decay_logits: torch.Tensor,
+        beta_logits: torch.Tensor,
+        initial_state: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        use_fla = (
+            q.device.type == "cuda"
+            and self.backend != "reference"
+            and (self.backend == "fla" or self._fla_kda_available())
+        )
+        if self.backend == "fla" and q.device.type != "cuda":
+            raise RuntimeError("kda_backend='fla' requires CUDA tensors")
+        if not use_fla:
+            return self._reference_kda(
+                q, k, v, decay_logits, beta_logits, initial_state
+            )
+        if int(q.size(1)) == 1 and initial_state is not None:
+            # Single-step decode: advancing the state by one position does not
+            # need the chunk grid.
+            output, final_state = self._fla_fused_recurrent_kda()(
+                q=q,
+                k=k,
+                v=v,
+                g=decay_logits,
+                beta=beta_logits,
+                A_log=self.A_log,
+                dt_bias=self.dt_bias,
+                initial_state=initial_state,
+                output_final_state=True,
+                use_qk_l2norm_in_kernel=True,
+                use_gate_in_kernel=True,
+                use_beta_sigmoid_in_kernel=True,
+                lower_bound=self.lower_bound,
+            )
+            return output, final_state
+        chunk_kda = self._fla_chunk_kda()
+        output, final_state = chunk_kda(
+            q=q,
+            k=k,
+            v=v,
+            g=decay_logits,
+            beta=beta_logits,
+            initial_state=initial_state,
+            output_final_state=bool(self.use_cache),
+            use_qk_l2norm_in_kernel=True,
+            use_gate_in_kernel=True,
+            use_beta_sigmoid_in_kernel=True,
+            safe_gate=True,
+            lower_bound=self.lower_bound,
+            A_log=self.A_log,
+            dt_bias=self.dt_bias,
+        )
+        if final_state is None:
+            final_state = q.new_zeros(
+                int(q.size(0)), self.num_heads, self.head_dim, self.head_dim,
+                dtype=torch.float32,
+            )
+        return output, final_state
+
+    def forward(self, x: torch.Tensor, *, start_pos: int = 0) -> torch.Tensor:
+        bsz, seqlen, _ = x.shape
+        if self.use_cache:
+            self.ensure_batch_capacity(int(bsz))
+        # Padding only ever exists in the prompt, so it applies to the prefill
+        # alone; every decode step is a real token for every row.
+        pad_mask = getattr(self, "_pad_mask", None) if int(start_pos) == 0 else None
+        if pad_mask is not None:
+            # Zero the input so the causal depthwise convolution sees exactly the
+            # zeros an unpadded sequence sees in its own left padding.
+            keep = pad_mask[:, :seqlen].to(dtype=x.dtype).unsqueeze(-1)
+            x = x * keep
+        q, k, v = self._short_convolution(
+            self.q_proj(x), self.k_proj(x), self.v_proj(x), start_pos=int(start_pos)
+        )
+        q = q.view(bsz, seqlen, self.num_heads, self.head_dim)
+        k = k.view(bsz, seqlen, self.num_heads, self.head_dim)
+        v = v.view(bsz, seqlen, self.num_heads, self.head_dim)
+        decay_logits = self.decay_up(self.decay_down(x)).view_as(q)
+        beta_logits = self.beta_proj(x)
+        if pad_mask is not None:
+            # alpha -> 1 and beta -> 0, so the state passes a pad through unchanged.
+            drop = ~pad_mask[:, :seqlen].bool()
+            decay_logits = decay_logits.masked_fill(drop[:, :, None, None], PAD_LOGIT)
+            beta_logits = beta_logits.masked_fill(drop[:, :, None], PAD_LOGIT)
+        initial_state = (
+            self.recurrent_state[:bsz]
+            if self.use_cache and int(start_pos) > 0
+            else None
+        )
+        output, final_state = self._run_kda(
+            q, k, v, decay_logits, beta_logits, initial_state
+        )
+        if self.use_cache:
+            with torch.no_grad():
+                self.recurrent_state[:bsz].copy_(
+                    final_state.detach().to(dtype=torch.float32)
+                )
+        gate_logits = (
+            self.output_gate(x)
+            if self.output_gate_full_rank
+            else self.output_gate_up(self.output_gate_down(x))
+        )
+        gate = torch.sigmoid(gate_logits).view(
+            bsz, seqlen, self.num_heads, self.head_dim
+        )
+        output = self.output_norm(output.to(dtype=x.dtype)) * gate
+        return self.o_proj(output.reshape(bsz, seqlen, self.inner_dim))
+
+
+class SophiaMLA(nn.Module):
+    """NoPE multi-head latent attention with a full-rank output gate."""
+
+    def __init__(self, args: ModelArgs) -> None:
+        super().__init__()
+        self.dim = int(args.dim)
+        self.num_heads = int(args.num_heads)
+        self.head_dim = int(args.head_dim)
+        self.inner_dim = self.num_heads * self.head_dim
+        self.q_rank = int(args.mla_q_rank)
+        self.kv_rank = int(args.mla_kv_rank)
+        self.use_cache = bool(args.use_cache)
+        self.logit_telemetry_enabled = False
+
+        self.q_down = RuntimeLinear(self.dim, self.q_rank, bias=False)
+        self.q_norm = RMSNorm(self.q_rank, args.norm_eps)
+        self.q_up = RuntimeLinear(self.q_rank, self.inner_dim, bias=False)
+
+        self.kv_down = RuntimeLinear(self.dim, self.kv_rank, bias=False)
+        self.kv_norm = RMSNorm(self.kv_rank, args.norm_eps)
+        self.k_up = RuntimeLinear(self.kv_rank, self.inner_dim, bias=False)
+        self.v_up = RuntimeLinear(self.kv_rank, self.inner_dim, bias=False)
+
+        self.output_gate = RuntimeLinear(self.dim, self.inner_dim, bias=False)
+        self.o_proj = RuntimeLinear(self.inner_dim, self.dim, bias=False)
+        self.register_buffer(
+            "latent_cache",
+            torch.zeros(
+                int(args.max_batch_size), int(args.max_seq_len), self.kv_rank
+            ),
+            persistent=False,
+        )
+        self.register_buffer(
+            "attention_logit_max",
+            torch.full((self.num_heads,), float("-inf"), dtype=torch.float32),
+            persistent=False,
+        )
+
+    def enable_attention_logit_telemetry(
+        self,
+        buffer: torch.Tensor | None = None,
+    ) -> None:
+        self.logit_telemetry_enabled = True
+        if buffer is None:
+            buffer = torch.full(
+                (self.num_heads,),
+                float("-inf"),
+                device=self.q_up.weight.device,
+                dtype=torch.float32,
+            )
+        if (
+            tuple(buffer.shape) != (self.num_heads,)
+            or buffer.device != self.q_up.weight.device
+            or buffer.dtype != torch.float32
+        ):
+            raise ValueError("MLA attention-logit buffer must be FP32 [num_heads]")
+        self.attention_logit_max = buffer
+
+    def reset_attention_logit_max(self) -> None:
+        self.attention_logit_max.fill_(float("-inf"))
+
+    @torch.no_grad()
+    def _record_attention_logit_max(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+    ) -> None:
+        query_tile = 512
+        query_length = int(q.size(-2))
+        key_length = int(k.size(-2))
+        query_offset = key_length - query_length
+        if query_offset < 0:
+            raise ValueError("MLA telemetry requires key length >= query length")
+        tile_causal = torch.ones(
+            (min(query_tile, query_length), min(query_tile, query_length)),
+            device=q.device,
+            dtype=torch.bool,
+        ).tril_()
+        key_transposed = k.detach().transpose(-2, -1)
+        running = self.attention_logit_max
+        scale = self.head_dim**-0.5
+        for query_start in range(0, query_length, query_tile):
+            query_end = min(query_start + query_tile, query_length)
+            key_end = query_offset + query_end
+            tile_width = query_end - query_start
+            logits = torch.matmul(
+                q.detach()[..., query_start:query_end, :],
+                key_transposed[..., :key_end],
+            ) * float(scale)
+            logits[..., -tile_width:].masked_fill_(
+                ~tile_causal[:tile_width, :tile_width].view(
+                    1, 1, tile_width, tile_width
+                ),
+                float("-inf"),
+            )
+            running = torch.maximum(
+                running,
+                logits.amax(dim=(0, 2, 3)).float(),
+            )
+        self.attention_logit_max.copy_(running)
+
+    def ensure_batch_capacity(self, batch_size: int) -> None:
+        required = int(batch_size)
+        if required <= int(self.latent_cache.size(0)):
+            return
+        grown = self.latent_cache.new_zeros(
+            required, int(self.latent_cache.size(1)), self.kv_rank
+        )
+        grown[: self.latent_cache.size(0)].copy_(self.latent_cache)
+        self.latent_cache = grown
+
+    def ensure_sequence_capacity(self, max_seq_len: int) -> None:
+        required = int(max_seq_len)
+        if required <= int(self.latent_cache.size(1)):
+            return
+        grown = self.latent_cache.new_zeros(
+            int(self.latent_cache.size(0)), required, self.kv_rank
+        )
+        grown[:, : self.latent_cache.size(1)].copy_(self.latent_cache)
+        self.latent_cache = grown
+
+    def rebuild_runtime_buffers(self, max_seq_len: int) -> None:
+        self.latent_cache = self.kv_down.weight.new_zeros(
+            int(self.latent_cache.size(0)), int(max_seq_len), self.kv_rank
+        )
+
+    def reset(self) -> None:
+        if self.latent_cache.is_inference():
+            self.latent_cache = torch.zeros_like(self.latent_cache)
+        else:
+            self.latent_cache.zero_()
+
+    def refresh_state_buffers(self) -> None:
+        self.latent_cache = torch.zeros_like(self.latent_cache)
+
+    def cache_snapshot(
+        self, *, device: str, batch_size: int, cache_pos: int | None
+    ) -> LayerCacheSnapshot:
+        end = int(self.latent_cache.size(1) if cache_pos is None else cache_pos)
+        return LayerCacheSnapshot(
+            latent=self.latent_cache[:batch_size, :end].to(device).clone()
+        )
+
+    def validate_cache_snapshot(self, snapshot: LayerCacheSnapshot) -> None:
+        if snapshot.recurrent is not None or snapshot.conv is not None:
+            raise ValueError("MLA cache cannot contain KDA state")
+        if snapshot.latent is not None and int(snapshot.latent.size(-1)) != self.kv_rank:
+            raise ValueError("MLA latent cache shape mismatch")
+
+    def load_cache_snapshot(self, snapshot: LayerCacheSnapshot) -> None:
+        self.validate_cache_snapshot(snapshot)
+        if snapshot.latent is None:
+            return
+        batch, length = int(snapshot.latent.size(0)), int(snapshot.latent.size(1))
+        self.ensure_batch_capacity(batch)
+        self.ensure_sequence_capacity(length)
+        self.latent_cache[:batch, :length].copy_(
+            snapshot.latent.to(
+                self.latent_cache.device, dtype=self.latent_cache.dtype
+            )
+        )
+
+    def _resolve_key_pad(
+        self, start_pos: int, bsz: int, k_len: int, device: torch.device
+    ) -> torch.Tensor | None:
+        """Which key positions are real, over the whole cached span."""
+        if int(start_pos) == 0:
+            mask = getattr(self, "_pad_mask", None)
+            self._key_pad = None if mask is None else mask[:bsz].bool().to(device)
+            return self._key_pad
+        cached = getattr(self, "_key_pad", None)
+        if cached is None:
+            return None
+        prompt_len = int(cached.size(1))
+        if k_len <= prompt_len:
+            return cached[:bsz, :k_len]
+        grown = torch.ones((bsz, k_len), dtype=torch.bool, device=device)
+        grown[:, :prompt_len] = cached[:bsz]
+        return grown
+
+    @staticmethod
+    def _causal_bias(q: torch.Tensor, k: torch.Tensor, start_pos: int) -> CausalBias | None:
+        if int(start_pos) == 0:
+            return None
+        return causal_lower_right(int(q.size(1)), int(k.size(1)))
+
+    def forward(self, x: torch.Tensor, *, start_pos: int = 0) -> torch.Tensor:
+        bsz, seqlen, _ = x.shape
+        q = self.q_up(self.q_norm(self.q_down(x))).view(
+            bsz, seqlen, self.num_heads, self.head_dim
+        )
+        latent = self.kv_norm(self.kv_down(x))
+        if self.use_cache:
+            end = int(start_pos) + int(seqlen)
+            self.ensure_batch_capacity(int(bsz))
+            self.ensure_sequence_capacity(end)
+            with torch.no_grad():
+                self.latent_cache[:bsz, int(start_pos) : end].copy_(latent.detach())
+            latent_full = self.latent_cache[:bsz, :end]
+        else:
+            latent_full = latent
+        k = self.k_up(latent_full).view(
+            bsz, int(latent_full.size(1)), self.num_heads, self.head_dim
+        )
+        v = self.v_up(latent_full).view_as(k)
+        q_t, k_t, v_t = (value.transpose(1, 2) for value in (q, k, v))
+        if self.logit_telemetry_enabled:
+            self._record_attention_logit_max(q_t, k_t)
+        key_pad = self._resolve_key_pad(int(start_pos), int(bsz), int(k.size(1)), x.device)
+        if key_pad is None:
+            bias = self._causal_bias(q, k, int(start_pos))
+            output = functional.scaled_dot_product_attention(
+                q_t,
+                k_t,
+                v_t,
+                attn_mask=bias,
+                is_causal=(bias is None),
+            ).transpose(1, 2)
+        else:
+            # causal_lower_right is an opaque bias object, so when padding is in
+            # play build the whole additive mask instead of trying to combine.
+            q_len, k_len = int(q.size(1)), int(k.size(1))
+            allow = key_pad[:, None, None, :].expand(bsz, 1, q_len, k_len).clone()
+            if q_len > 1:
+                offset = k_len - q_len
+                causal = torch.ones((q_len, k_len), dtype=torch.bool, device=x.device).tril(offset)
+                allow &= causal[None, None]
+            attn_mask = torch.zeros((bsz, 1, q_len, k_len), dtype=q_t.dtype, device=x.device)
+            attn_mask = attn_mask.masked_fill(~allow, float("-inf"))
+            output = functional.scaled_dot_product_attention(
+                q_t, k_t, v_t, attn_mask=attn_mask, is_causal=False
+            ).transpose(1, 2)
+        gate = torch.sigmoid(self.output_gate(x)).view_as(output)
+        gated_output = (output.float() * gate.float()).to(dtype=x.dtype)
+        return self.o_proj(gated_output.reshape(bsz, seqlen, self.inner_dim))
+
+
+def set_pad_mask(model: nn.Module, mask: torch.Tensor | None) -> None:
+    """Hang a [B, T] boolean mask (True = real token) on every mixer, or clear it.
+
+    Clearing also drops MLA's remembered key mask, so a later unpadded batch cannot
+    inherit the previous batch's padding.
+    """
+    for module in model.modules():
+        if isinstance(module, (SophiaKDA, SophiaMLA)):
+            module._pad_mask = mask
+            if mask is None and isinstance(module, SophiaMLA):
+                module._key_pad = None
+
+
+__all__ = [
+    "CausalDepthwiseConv1d",
+    "SophiaKDA",
+    "SophiaMLA",
+    "set_pad_mask",
+]

model_blocks.py

@@ -0,0 +1,94 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+
+from .model_attention import SophiaKDA, SophiaMLA
+from .model_config import ModelArgs
+from .model_ops import RMSNorm
+from .runtime_linear import RuntimeLinear
+
+
+class FeedForward(nn.Module):
+    def __init__(self, args: ModelArgs) -> None:
+        super().__init__()
+        self.hidden = int(args.ffn_hidden)
+        self.gate_softcap = float(args.situ_gate_softcap)
+        self.up_softcap = float(args.situ_up_softcap)
+        self.gate_up_proj = RuntimeLinear(
+            int(args.dim), 2 * self.hidden, bias=False
+        )
+        self.down_proj = RuntimeLinear(self.hidden, int(args.dim), bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate, up = self.gate_up_proj(x).split(self.hidden, dim=-1)
+        bounded_gate = self.gate_softcap * torch.tanh(gate / self.gate_softcap)
+        bounded_up = self.up_softcap * torch.tanh(up / self.up_softcap)
+        return self.down_proj(bounded_gate * torch.sigmoid(gate) * bounded_up)
+
+
+class AttentionResidualMixer(nn.Module):
+    """Content-dependent mixing over completed depth blocks and a partial block."""
+
+    def __init__(self, args: ModelArgs) -> None:
+        super().__init__()
+        self.norm = RMSNorm(args.dim, args.norm_eps)
+        self.query = RuntimeLinear(args.dim, 1, bias=False)
+
+    def forward(
+        self,
+        partial_block: torch.Tensor,
+        completed_blocks: torch.Tensor,
+    ) -> torch.Tensor:
+        values = torch.cat((completed_blocks, partial_block.unsqueeze(2)), dim=2)
+        keys = self.norm(values)
+        score_weight = self.query.weight.squeeze(0).float()
+        scores = torch.einsum("bsth,h->bst", keys.float(), score_weight)
+        weights = scores.softmax(dim=2).unsqueeze(-1).to(dtype=values.dtype)
+        return (weights * values).sum(dim=2).to(dtype=values.dtype)
+
+
+class SophiaBlock(nn.Module):
+    def __init__(self, args: ModelArgs, layer_idx: int) -> None:
+        super().__init__()
+        self.layer_idx = int(layer_idx)
+        self.layer_type = args.layer_type(self.layer_idx)
+        self.attn_norm = RMSNorm(args.dim, args.norm_eps)
+        self.attn = (
+            SophiaMLA(args) if self.layer_type == "mla" else SophiaKDA(args)
+        )
+        self.ffn_norm = RMSNorm(args.dim, args.norm_eps)
+        self.ffn = FeedForward(args)
+        self.attn_res_block_size = int(args.attn_res_block_size)
+        self.attn_residual = AttentionResidualMixer(args)
+        self.ffn_residual = AttentionResidualMixer(args)
+
+    def forward(
+        self,
+        partial_block: torch.Tensor,
+        completed_blocks: torch.Tensor,
+        *,
+        start_pos: int = 0,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        attn_input = self.attn_residual(partial_block, completed_blocks)
+        if self.layer_idx % self.attn_res_block_size == 0:
+            completed_blocks = torch.cat(
+                (completed_blocks, partial_block.unsqueeze(2)), dim=2
+            )
+            partial_block = self.attn(
+                self.attn_norm(attn_input), start_pos=int(start_pos)
+            )
+        else:
+            partial_block = partial_block + self.attn(
+                self.attn_norm(attn_input), start_pos=int(start_pos)
+            )
+        ffn_input = self.ffn_residual(partial_block, completed_blocks)
+        partial_block = partial_block + self.ffn(self.ffn_norm(ffn_input))
+        return partial_block, completed_blocks
+
+
+__all__ = ["AttentionResidualMixer", "FeedForward", "SophiaBlock"]

model_config.py

@@ -0,0 +1,87 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from .semantics import canonicalize_model_values, validate_model_values
+
+
+@dataclass
+class ModelArgs:
+    """Torch-free runtime configuration for Sophia Hybrid."""
+
+    vocab_size: int = 65536
+    dim: int = 1536
+    n_layers: int = 28
+    num_heads: int = 16
+    head_dim: int = 128
+    ffn_hidden: int = 3968
+    kda_decay_rank: int = 128
+    kda_output_gate_rank: int = 128
+    kda_output_gate_full_rank: bool = True
+    kda_decay_lower_bound: float = -5.0
+    kda_dt_min: float = 1e-3
+    kda_dt_max: float = 1e-1
+    kda_dt_floor: float = 1e-4
+    kda_a_log_init: float = 0.0
+    mla_q_rank: int = 384
+    mla_kv_rank: int = 128
+    short_conv_kernel: int = 4
+    attn_res_block_size: int = 4
+    situ_gate_softcap: float = 4.0
+    situ_up_softcap: float = 25.0
+    norm_eps: float = 1e-5
+    max_seq_len: int = 4096
+    max_batch_size: int = 4
+    dropout: float = 0.0
+    initializer_range: float = 0.02
+    kda_backend: str = "auto"
+    use_cache: bool = True
+
+    def __post_init__(self) -> None:
+        normalized = canonicalize_model_values(vars(self))
+        for name, value in normalized.items():
+            if hasattr(self, name):
+                setattr(self, name, value)
+        validate_model_values(normalized)
+
+    @property
+    def attention_inner_dim(self) -> int:
+        return int(self.num_heads) * int(self.head_dim)
+
+    def layer_type(self, layer_idx: int) -> str:
+        return "mla" if int(layer_idx) % 4 == 3 else "kda"
+
+    def ensure_runtime_batch_capacity(self, batch_size: int) -> int:
+        required = int(batch_size)
+        if required <= 0:
+            raise ValueError(f"batch_size must be > 0, got {required}")
+        self.max_batch_size = max(int(self.max_batch_size), required)
+        return int(self.max_batch_size)
+
+    def runtime_batch_capacity(self) -> int:
+        return int(self.max_batch_size)
+
+    def ensure_runtime_sequence_capacity(self, max_seq_len: int) -> int:
+        required = int(max_seq_len)
+        if required <= 0:
+            raise ValueError(f"max_seq_len must be > 0, got {required}")
+        configured = int(self.max_seq_len)
+        if required > configured:
+            raise ValueError(
+                "runtime sequence capacity cannot exceed configured max_seq_len: "
+                f"required={required} configured={configured}"
+            )
+        return configured
+
+    def runtime_sequence_capacity(self) -> int:
+        return int(self.max_seq_len)
+
+    def runtime_max_seq_len(self) -> int:
+        return self.runtime_sequence_capacity()
+
+    def runtime_layer_count(self) -> int:
+        return int(self.n_layers)

model_dir.py

@@ -0,0 +1,82 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+"""Local exported-model directory helpers."""
+
+from __future__ import annotations
+
+import importlib.util
+import os
+from collections.abc import Callable
+
+import torch
+
+from .sophia_decoder import SophiaDecoder
+from ml.integrations.adapters.hf.tokenizer import load_local_tokenizer
+
+
+GradientCheckpointingFn = Callable[..., None]
+
+
+def local_export_load_kwargs() -> dict[str, object]:
+    kwargs: dict[str, object] = {
+        "dtype": torch.float32,
+        "local_files_only": True,
+    }
+    if importlib.util.find_spec("accelerate") is not None:
+        kwargs["low_cpu_mem_usage"] = True
+    return kwargs
+
+
+load_local_export_tokenizer = load_local_tokenizer
+
+
+def load_trainable_decoder_from_export_dir(
+    *,
+    export_dir: str,
+    device: torch.device,
+    base_dtype: torch.dtype,
+    gradient_checkpointing: bool,
+    gradient_checkpointing_exclude_first: int,
+    gradient_checkpointing_exclude_last: int,
+    apply_gradient_checkpointing_fn: GradientCheckpointingFn,
+) -> SophiaDecoder:
+    model = SophiaDecoder.from_pretrained(
+        os.path.abspath(str(export_dir)),
+        device=device,
+        dtype=base_dtype,
+    )
+    model.train(True)
+    config = getattr(model, "config", None)
+    if config is not None:
+        config.return_logits_in_train = True
+        config.use_cache = False
+    exclude_first = int(gradient_checkpointing_exclude_first)
+    exclude_last = int(gradient_checkpointing_exclude_last)
+    if min(exclude_first, exclude_last) < 0:
+        raise ValueError("gradient checkpoint exclusion counts must be >= 0")
+    if not bool(gradient_checkpointing) and (exclude_first or exclude_last):
+        raise ValueError(
+            "gradient checkpoint exclusions require gradient_checkpointing=1"
+        )
+    if bool(gradient_checkpointing) and exclude_first + exclude_last >= int(
+        model.config.n_layers
+    ):
+        raise ValueError(
+            "gradient checkpoint exclusions must leave at least one checkpointed layer"
+        )
+    model.apply_runtime_recipe_knobs(
+        loss_chunk_size=int(model.config.loss_chunk_size),
+        gradient_checkpointing_exclude_first=exclude_first,
+        gradient_checkpointing_exclude_last=exclude_last,
+    )
+    apply_gradient_checkpointing_fn(model, enabled=bool(gradient_checkpointing))
+    return model
+
+
+__all__ = [
+    "load_local_export_tokenizer",
+    "load_trainable_decoder_from_export_dir",
+    "local_export_load_kwargs",
+]

model_ops.py

@@ -0,0 +1,81 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as functional
+from torch import nn
+
+
+
+def infer_module_tensor_device(
+    module: nn.Module,
+    *,
+    default_device: torch.device,
+) -> torch.device:
+    for parameter in module.parameters():
+        return parameter.device
+    for buffer in module.buffers():
+        if isinstance(buffer, torch.Tensor):
+            return buffer.device
+    return default_device
+
+
+def apply_preserving_complex_buffers(
+    module: nn.Module,
+    fn,
+    *,
+    buffer_names: tuple[str, ...],
+    apply_super,
+):
+    preserved: dict[str, torch.Tensor] = {}
+    preserved_device = torch.device("cpu")
+    for name in buffer_names:
+        tensor = module._buffers.get(name)
+        if isinstance(tensor, torch.Tensor) and tensor.is_complex():
+            preserved[name] = tensor
+            preserved_device = tensor.device
+            module._buffers[name] = None
+    try:
+        result = apply_super()
+    finally:
+        if preserved:
+            target_device = infer_module_tensor_device(
+                module,
+                default_device=preserved_device,
+            )
+            for name, tensor in preserved.items():
+                module._buffers[name] = tensor.to(device=target_device)
+    return result
+
+class RMSNorm(nn.Module):
+    """Root Mean Square Layer Normalization."""
+
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.weight._no_weight_decay = True  # type: ignore[attr-defined]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return functional.rms_norm(
+            x,
+            (int(x.size(-1)),),
+            self.weight,
+            float(self.eps),
+        )
+
+
+class StandardLogitMixer(nn.Module):
+    """Decoder logits path: final norm followed by output projection."""
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        *,
+        norm: RMSNorm,
+        output: nn.Module,
+    ) -> torch.Tensor:
+        return output(norm(x))

model_runtime.py

@@ -0,0 +1,219 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from threading import RLock
+
+import torch
+
+from .model_runtime_control import (
+    RuntimeControlModel,
+    enable_runtime_cache,
+    ensure_batch_capacity,
+    ensure_sequence_capacity,
+    rebuild_runtime_buffers,
+    refresh_state_buffers,
+    reset_runtime_cache,
+)
+from .model_state import (
+    LayerCacheSnapshot,
+    RuntimeCacheSnapshot,
+    TransformerRuntimeState,
+)
+
+
+class TransformerRuntime:
+    def __init__(
+        self,
+        model: RuntimeControlModel,
+        *,
+        state: TransformerRuntimeState | None = None,
+    ) -> None:
+        self.model = model
+        self.state = state or TransformerRuntimeState()
+
+    @property
+    def lock(self) -> RLock:
+        return self.state.lock
+
+    @lock.setter
+    def lock(self, value: RLock) -> None:
+        self.state.lock = value
+
+    def supports_loss_chunk_size(self) -> bool:
+        return True
+
+    def supports_checkpoint_excludes(self) -> bool:
+        return True
+
+    def runtime_recipe_knobs(self) -> tuple[int, int, int]:
+        return (
+            int(self.state.loss_chunk_size),
+            int(self.model.gradient_checkpointing_exclude_first),
+            int(self.model.gradient_checkpointing_exclude_last),
+        )
+
+    def apply_runtime_recipe_knobs(
+        self,
+        *,
+        loss_chunk_size: int,
+        gradient_checkpointing_exclude_first: int,
+        gradient_checkpointing_exclude_last: int,
+    ) -> tuple[int, int, int]:
+        chunk_size = int(loss_chunk_size)
+        exclude_first = int(gradient_checkpointing_exclude_first)
+        exclude_last = int(gradient_checkpointing_exclude_last)
+        if chunk_size < 0:
+            raise ValueError(f"loss_chunk_size must be >= 0, got {chunk_size}")
+        if exclude_first < 0:
+            raise ValueError(
+                "gradient_checkpointing_exclude_first must be >= 0, "
+                f"got {exclude_first}"
+            )
+        if exclude_last < 0:
+            raise ValueError(
+                "gradient_checkpointing_exclude_last must be >= 0, "
+                f"got {exclude_last}"
+            )
+        layer_count = len(self.model.layers)
+        if exclude_first + exclude_last > layer_count:
+            raise ValueError(
+                "gradient checkpoint exclusions exceed model layer count: "
+                f"first={exclude_first} last={exclude_last} layers={layer_count}"
+            )
+        self.state.loss_chunk_size = chunk_size
+        self.model.gradient_checkpointing_exclude_first = exclude_first
+        self.model.gradient_checkpointing_exclude_last = exclude_last
+        return self.runtime_recipe_knobs()
+
+    def ensure_batch_capacity(self, batch_size: int) -> None:
+        ensure_batch_capacity(self.model, batch_size)
+
+    def ensure_sequence_capacity(self, max_seq_len: int) -> None:
+        ensure_sequence_capacity(self.model, max_seq_len)
+
+    def ensure_runtime_max_seq_len(self, max_seq_len: int) -> None:
+        self.ensure_sequence_capacity(max_seq_len)
+
+    def runtime_max_seq_len(self) -> int:
+        return int(self.model.runtime_max_seq_len())
+
+    def runtime_batch_capacity(self) -> int:
+        return int(self.model.runtime_batch_capacity())
+
+    def rebuild_runtime_buffers(self) -> None:
+        rebuild_runtime_buffers(self.model)
+
+    def sync_runtime_batch_capacity(self, max_batch_size: int) -> int:
+        self.ensure_batch_capacity(max_batch_size)
+        return self.runtime_batch_capacity()
+
+    def reset_runtime_cache(self) -> None:
+        reset_runtime_cache(self.model)
+
+    def refresh_state_buffers(self) -> None:
+        refresh_state_buffers(self.model)
+
+    def _forward_with_last_hidden(
+        self,
+        input_ids: torch.Tensor,
+        *,
+        start_pos: int,
+        return_all_logits: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        hidden, _ = self.model._forward_hidden(input_ids, start_pos=start_pos)
+        last_hidden = hidden[:, -1, :]
+        logits_hidden = hidden if return_all_logits else hidden[:, -1:, :]
+        logits = self.model.head_mixer(
+            logits_hidden, norm=self.model.norm, output=self.model.output
+        )
+        if not return_all_logits:
+            logits = logits[:, 0, :]
+        return logits, last_hidden
+
+    def forward_with_last_hidden(
+        self,
+        input_ids: torch.Tensor,
+        *,
+        start_pos: int = 0,
+        return_all_logits: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        enable_runtime_cache(self.model)
+        return self._forward_with_last_hidden(
+            input_ids,
+            start_pos=int(start_pos),
+            return_all_logits=return_all_logits,
+        )
+
+    def replay_with_cache(
+        self,
+        input_ids: torch.Tensor,
+        *,
+        start_pos: int = 0,
+        return_all_logits: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        enable_runtime_cache(self.model)
+        if int(input_ids.size(1)) <= 1 or int(start_pos) == 0:
+            return self._forward_with_last_hidden(
+                input_ids,
+                start_pos=int(start_pos),
+                return_all_logits=return_all_logits,
+            )
+        outputs: list[torch.Tensor] = []
+        last_hidden = None
+        for offset in range(int(input_ids.size(1))):
+            logits, last_hidden = self._forward_with_last_hidden(
+                input_ids[:, offset : offset + 1],
+                start_pos=int(start_pos) + offset,
+                return_all_logits=True,
+            )
+            outputs.append(logits)
+        combined = torch.cat(outputs, dim=1)
+        return (combined if return_all_logits else combined[:, -1, :]), last_hidden
+
+    @staticmethod
+    def cache_batch_size(batch_size: int | None, max_batch_size: int) -> int:
+        active = int(max_batch_size if batch_size is None else batch_size)
+        if active <= 0:
+            raise ValueError(f"batch_size must be >= 1, got {active}")
+        return active
+
+    def cache_dump(
+        self,
+        *,
+        device: str = "cpu",
+        cache_pos: int | None = None,
+        batch_size: int | None = None,
+    ) -> RuntimeCacheSnapshot:
+        active = self.cache_batch_size(batch_size, self.runtime_batch_capacity())
+        self.ensure_batch_capacity(active)
+        return RuntimeCacheSnapshot(
+            layers=tuple(
+                layer.attn.cache_snapshot(
+                    device=device, batch_size=active, cache_pos=cache_pos
+                )
+                for layer in self.model.layers
+            )
+        )
+
+    def cache_load(self, cache_snapshot: RuntimeCacheSnapshot) -> None:
+        enable_runtime_cache(self.model)
+        if not isinstance(cache_snapshot, RuntimeCacheSnapshot):
+            raise TypeError("cache snapshot must be a RuntimeCacheSnapshot")
+        if len(cache_snapshot.layers) > len(self.model.layers):
+            raise ValueError("cache snapshot has more layers than the model")
+        required = cache_snapshot.batch_size()
+        if required:
+            self.ensure_batch_capacity(required)
+        for index, snapshot in enumerate(cache_snapshot.layers):
+            if snapshot is not None:
+                self.model.layers[index].attn.validate_cache_snapshot(snapshot)
+        self.reset_runtime_cache()
+        for index, snapshot in enumerate(cache_snapshot.layers):
+            if snapshot is not None:
+                self.model.layers[index].attn.load_cache_snapshot(snapshot)
+
+
+__all__ = ["TransformerRuntime", "LayerCacheSnapshot"]

model_runtime_control.py

@@ -0,0 +1,90 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from collections.abc import Iterable, Sequence
+from typing import Protocol
+
+
+class _RuntimeArgs(Protocol):
+    use_cache: bool
+    def ensure_runtime_batch_capacity(self, batch_size: int) -> int: ...
+    def ensure_runtime_sequence_capacity(self, max_seq_len: int) -> int: ...
+
+
+class _AttentionControl(Protocol):
+    use_cache: bool
+    def ensure_batch_capacity(self, batch_size: int) -> None: ...
+    def ensure_sequence_capacity(self, max_seq_len: int) -> None: ...
+    def rebuild_runtime_buffers(self, max_seq_len: int) -> None: ...
+    def reset(self) -> None: ...
+    def refresh_state_buffers(self) -> None: ...
+
+
+class _RuntimeBlock(Protocol):
+    attn: _AttentionControl
+
+
+class RuntimeControlModel(Protocol):
+    args: _RuntimeArgs
+    layers: Sequence[_RuntimeBlock]
+    def runtime_reserve_batch_capacity(self, batch_size: int) -> int: ...
+    def runtime_reserve_sequence_capacity(self, max_seq_len: int) -> int: ...
+    def runtime_sequence_capacity(self) -> int: ...
+
+
+def iter_attn_modules(model: RuntimeControlModel) -> Iterable[_AttentionControl]:
+    for layer in model.layers:
+        yield layer.attn
+
+
+def enable_runtime_cache(model: RuntimeControlModel) -> None:
+    model.args.use_cache = True
+    for attn in iter_attn_modules(model):
+        attn.use_cache = True
+
+
+def ensure_batch_capacity(model: RuntimeControlModel, batch_size: int) -> None:
+    required = int(model.runtime_reserve_batch_capacity(batch_size))
+    for attn in iter_attn_modules(model):
+        attn.ensure_batch_capacity(required)
+
+
+def ensure_sequence_capacity(model: RuntimeControlModel, max_seq_len: int) -> None:
+    required = int(max_seq_len)
+    if required <= 0:
+        raise ValueError(f"max_seq_len must be > 0, got {required}")
+    if required <= int(model.runtime_sequence_capacity()):
+        return
+    required = int(model.runtime_reserve_sequence_capacity(required))
+    for attn in iter_attn_modules(model):
+        attn.ensure_sequence_capacity(required)
+
+
+def rebuild_runtime_buffers(model: RuntimeControlModel) -> None:
+    required = int(model.runtime_sequence_capacity())
+    for attn in iter_attn_modules(model):
+        attn.rebuild_runtime_buffers(required)
+
+
+def reset_runtime_cache(model: RuntimeControlModel) -> None:
+    for attn in iter_attn_modules(model):
+        attn.reset()
+
+
+def refresh_state_buffers(model: RuntimeControlModel) -> None:
+    for attn in iter_attn_modules(model):
+        attn.refresh_state_buffers()
+
+
+__all__ = [
+    "RuntimeControlModel",
+    "enable_runtime_cache",
+    "ensure_batch_capacity",
+    "ensure_sequence_capacity",
+    "rebuild_runtime_buffers",
+    "refresh_state_buffers",
+    "reset_runtime_cache",
+]

model_state.py

@@ -0,0 +1,135 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+from threading import RLock
+
+import torch
+
+
+def _clone(value: torch.Tensor | None) -> torch.Tensor | None:
+    return None if value is None else value.detach().clone()
+
+
+_CACHE_SUFFIXES = (
+    ("_recurrent", "recurrent"),
+    ("_conv", "conv"),
+    ("_latent", "latent"),
+)
+
+
+@dataclass(frozen=True)
+class LayerCacheSnapshot:
+    recurrent: torch.Tensor | None = None
+    conv: torch.Tensor | None = None
+    latent: torch.Tensor | None = None
+
+    def clone(self) -> LayerCacheSnapshot:
+        return LayerCacheSnapshot(
+            recurrent=_clone(self.recurrent),
+            conv=_clone(self.conv),
+            latent=_clone(self.latent),
+        )
+
+    def tensor_fields(self) -> tuple[tuple[str, torch.Tensor | None], ...]:
+        return (
+            ("recurrent", self.recurrent),
+            ("conv", self.conv),
+            ("latent", self.latent),
+        )
+
+    def is_empty(self) -> bool:
+        return all(value is None for _name, value in self.tensor_fields())
+
+    def batch_size(self) -> int:
+        return max(
+            (int(value.size(0)) for _name, value in self.tensor_fields() if value is not None),
+            default=0,
+        )
+
+    def to_payload(self, *, prefix: str) -> dict[str, torch.Tensor]:
+        return {
+            f"{prefix}_{name}": value.detach().clone()
+            for name, value in self.tensor_fields()
+            if value is not None
+        }
+
+    @classmethod
+    def from_field_map(
+        cls,
+        field_map: Mapping[str, object] | None,
+    ) -> LayerCacheSnapshot | None:
+        if field_map is None:
+            return None
+        values: dict[str, torch.Tensor | None] = {}
+        for name in ("recurrent", "conv", "latent"):
+            value = field_map.get(name)
+            if value is not None and not torch.is_tensor(value):
+                raise TypeError(f"layer cache field {name!r} must be a tensor or None")
+            values[name] = _clone(value)
+        snapshot = cls(**values)
+        return None if snapshot.is_empty() else snapshot
+
+
+@dataclass(frozen=True)
+class RuntimeCacheSnapshot:
+    layers: tuple[LayerCacheSnapshot | None, ...] = ()
+
+    def clone(self) -> RuntimeCacheSnapshot:
+        return RuntimeCacheSnapshot(
+            layers=tuple(None if item is None else item.clone() for item in self.layers)
+        )
+
+    def is_empty(self) -> bool:
+        return all(item is None or item.is_empty() for item in self.layers)
+
+    def batch_size(self) -> int:
+        return max((item.batch_size() for item in self.layers if item is not None), default=0)
+
+    def named_snapshots(self) -> tuple[tuple[str, LayerCacheSnapshot], ...]:
+        return tuple(
+            (f"layer_{index}", item)
+            for index, item in enumerate(self.layers)
+            if item is not None and not item.is_empty()
+        )
+
+    def to_payload(self) -> dict[str, torch.Tensor]:
+        payload: dict[str, torch.Tensor] = {}
+        for prefix, snapshot in self.named_snapshots():
+            payload.update(snapshot.to_payload(prefix=prefix))
+        return payload
+
+    @classmethod
+    def from_payload(cls, payload: Mapping[str, object]) -> RuntimeCacheSnapshot:
+        fields_by_layer: dict[int, dict[str, object]] = {}
+        for raw_key, value in payload.items():
+            key = str(raw_key)
+            for suffix, field_name in _CACHE_SUFFIXES:
+                if key.endswith(suffix):
+                    prefix = key[: -len(suffix)]
+                    if not prefix.startswith("layer_"):
+                        break
+                    index = int(prefix.removeprefix("layer_"))
+                    fields_by_layer.setdefault(index, {})[field_name] = value
+                    break
+            else:
+                raise ValueError(f"unrecognized runtime cache payload key: {key}")
+        if not fields_by_layer:
+            return cls()
+        layers: list[LayerCacheSnapshot | None] = [None] * (max(fields_by_layer) + 1)
+        for index, field_map in fields_by_layer.items():
+            layers[index] = LayerCacheSnapshot.from_field_map(field_map)
+        return cls(layers=tuple(layers))
+
+
+@dataclass
+class TransformerRuntimeState:
+    lock: RLock = field(default_factory=RLock)
+    loss_chunk_size: int = 0
+
+
+__all__ = ["LayerCacheSnapshot", "RuntimeCacheSnapshot", "TransformerRuntimeState"]

model_transformer_setup.py

@@ -0,0 +1,92 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+import math
+from typing import Protocol
+
+from torch import nn
+
+from .model_attention import CausalDepthwiseConv1d
+from .model_blocks import AttentionResidualMixer, SophiaBlock
+from .model_config import ModelArgs
+from .model_ops import RMSNorm, StandardLogitMixer
+from .model_runtime import TransformerRuntime
+from .runtime_linear import RuntimeLinear
+from .model_state import TransformerRuntimeState
+
+
+class _TransformerSetup(Protocol):
+    args: ModelArgs
+    runtime: TransformerRuntime
+    runtime_state: TransformerRuntimeState
+    tok_embeddings: nn.Embedding
+    layers: nn.ModuleList
+    norm: RMSNorm
+    output: RuntimeLinear
+    head_mixer: nn.Module
+    output_attn_residual: AttentionResidualMixer
+    dropout: nn.Dropout
+    gradient_checkpointing: bool
+    gradient_checkpointing_exclude_first: int
+    gradient_checkpointing_exclude_last: int
+
+
+def tie_word_embeddings(transformer: _TransformerSetup) -> None:
+    if tuple(transformer.tok_embeddings.weight.shape) != tuple(transformer.output.weight.shape):
+        raise ValueError("tied embedding and output projection shapes must match")
+    transformer.output.weight = transformer.tok_embeddings.weight
+
+
+def init_weights(model: nn.Module) -> None:
+    base_std = float(getattr(getattr(model, "args", None), "initializer_range", 0.02))
+    if not math.isfinite(base_std) or base_std <= 0.0:
+        raise ValueError(f"initializer_range must be finite and > 0, got {base_std}")
+    layer_count = max(int(getattr(getattr(model, "args", None), "n_layers", 1)), 1)
+    residual_std = base_std / math.sqrt(2.0 * layer_count)
+    for module in model.modules():
+        if isinstance(module, (nn.Linear, RuntimeLinear)):
+            nn.init.normal_(module.weight, mean=0.0, std=base_std)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=base_std)
+        elif isinstance(module, CausalDepthwiseConv1d):
+            nn.init.normal_(module.weight, mean=0.0, std=base_std)
+
+    for layer in getattr(model, "layers", ()):
+        nn.init.normal_(layer.attn.o_proj.weight, mean=0.0, std=residual_std)
+        nn.init.normal_(layer.ffn.down_proj.weight, mean=0.0, std=residual_std)
+        nn.init.zeros_(layer.attn_residual.query.weight)
+        nn.init.zeros_(layer.ffn_residual.query.weight)
+    output_attn_residual = getattr(model, "output_attn_residual", None)
+    if output_attn_residual is not None:
+        nn.init.zeros_(output_attn_residual.query.weight)
+
+
+def initialize_transformer(transformer: _TransformerSetup, args: ModelArgs) -> None:
+    transformer.args = args
+    transformer.runtime = TransformerRuntime(
+        transformer,
+        state=TransformerRuntimeState(),
+    )
+    transformer.runtime_state = transformer.runtime.state
+    transformer.tok_embeddings = nn.Embedding(args.vocab_size, args.dim)
+    transformer.layers = nn.ModuleList(
+        [SophiaBlock(args, layer_idx) for layer_idx in range(args.n_layers)]
+    )
+    transformer.norm = RMSNorm(args.dim, args.norm_eps)
+    transformer.output_attn_residual = AttentionResidualMixer(args)
+    transformer.output = RuntimeLinear(args.dim, args.vocab_size, bias=False)
+    tie_word_embeddings(transformer)
+    transformer.head_mixer = StandardLogitMixer()
+    transformer.dropout = nn.Dropout(float(args.dropout))
+    transformer.gradient_checkpointing = False
+    transformer.gradient_checkpointing_exclude_first = 0
+    transformer.gradient_checkpointing_exclude_last = 0
+    init_weights(transformer)
+
+
+__all__ = ["init_weights", "initialize_transformer", "tie_word_embeddings"]

modeling_sophia.py

@@ -0,0 +1,149 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+"""
+Sophia model implementation.
+
+This file contains:
+- `SophiaForCausalLM`: Hugging Face causal language model adapter over the Sophia runtime.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from .sophia_runtime import Transformer as _SophiaRuntimeTransformer  # noqa: F401
+
+if TYPE_CHECKING:
+    from .sophia_runtime import *  # noqa: F401,F403
+    from .model_config import *  # noqa: F401,F403
+    from .hf_remote_code import *  # noqa: F401,F403
+    from .hf_support import *  # noqa: F401,F403
+    from .hf_cache import *  # noqa: F401,F403
+    from .hf_config import *  # noqa: F401,F403
+    from .hf_generation import *  # noqa: F401,F403
+    from .hf_lifecycle import *  # noqa: F401,F403
+    from .model_attention import *  # noqa: F401,F403
+    from .model_blocks import *  # noqa: F401,F403
+    from .model_ops import *  # noqa: F401,F403
+    from .model_runtime import *  # noqa: F401,F403
+    from .runtime_contracts import *  # noqa: F401,F403
+    from .model_runtime_control import *  # noqa: F401,F403
+    from .model_state import *  # noqa: F401,F403
+    from .model_transformer_setup import *  # noqa: F401,F403
+    from .runtime_linear import *  # noqa: F401,F403
+    from .canonical_config import *  # noqa: F401,F403
+    from .model_dir import *  # noqa: F401,F403
+    from .loss_stats import *  # noqa: F401,F403
+    from .input_mask import *  # noqa: F401,F403
+    from .cache_decode import *  # noqa: F401,F403
+    from .config_projection import *  # noqa: F401,F403
+    from .hf_projection import *  # noqa: F401,F403
+    from .runtime_backend import *  # noqa: F401,F403
+    from .decoder_forward import *  # noqa: F401,F403
+    from .decoder_types import *  # noqa: F401,F403
+    from .decoder_loss import *  # noqa: F401,F403
+    from .decoder_loss_forward import *  # noqa: F401,F403
+    from .decoder_full import *  # noqa: F401,F403
+    from .pretrained_bundle import *  # noqa: F401,F403
+    from .decoder_output import *  # noqa: F401,F403
+    from .decoder_runtime import *  # noqa: F401,F403
+    from .decoder_host import *  # noqa: F401,F403
+    from .sophia_decoder import *  # noqa: F401,F403
+    from .semantics import *  # noqa: F401,F403
+
+import torch
+from transformers import PreTrainedModel
+from transformers.generation import GenerationMixin
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from .runtime_backend import resolve_runtime_backend
+from .hf_cache import SophiaCache
+from .hf_generation import (
+    CausalLMForwardMixin,
+    GenerationCacheMixin,
+)
+from .hf_lifecycle import PreTrainedLifecycleMixin
+from .hf_projection import SophiaConfig
+from .decoder_output import (
+    format_hf_causal_lm_output as _format_hf_causal_lm_output,
+    resolve_return_dict as _resolve_return_dict,
+)
+from .decoder_host import DecoderHostMixin
+from .decoder_runtime import (
+    DecoderModelMixin,
+    DecoderRecipeMixin,
+    DecoderRuntimeMixin,
+)
+
+
+class SophiaForCausalLM(
+    DecoderModelMixin,
+    DecoderRuntimeMixin,
+    DecoderRecipeMixin,
+    DecoderHostMixin,
+    GenerationCacheMixin,
+    CausalLMForwardMixin,
+    PreTrainedLifecycleMixin,
+    PreTrainedModel,
+    GenerationMixin,
+):
+    config_class = SophiaConfig
+    base_model_prefix = "model"
+    _tied_weights_keys = {"model.output.weight": "model.tok_embeddings.weight"}
+    supports_gradient_checkpointing = True
+    _is_stateful = True
+
+    @classmethod
+    def _supports_default_dynamic_cache(cls) -> bool:
+        return False
+
+    def __init__(
+        self,
+        config: SophiaConfig | None = None,
+        *,
+        runtime_max_seq_len: int | None = None,
+    ):
+        config = config or SophiaConfig()
+        super().__init__(config)
+        self._initialize_decoder_runtime(
+            config=config,
+            runtime_max_seq_len=runtime_max_seq_len,
+            runtime_backend=resolve_runtime_backend(),
+            gradient_checkpointing_enabled=bool(
+                getattr(self, "gradient_checkpointing", False)
+            ),
+            register_tied_weights=True,
+        )
+
+    def _set_gradient_checkpointing(
+        self,
+        enable: bool = True,
+        gradient_checkpointing_func: object = None,
+    ) -> None:
+        del gradient_checkpointing_func
+        self.gradient_checkpointing = bool(enable)
+        self._sync_runtime_gradient_checkpointing(enable=bool(enable))
+
+    def _resolve_runtime_return_dict(self, *, return_dict: bool | None) -> bool:
+        return _resolve_return_dict(
+            config=self.config,
+            return_dict=return_dict,
+        )
+
+    def _format_decoder_output(
+        self,
+        *,
+        loss: torch.Tensor | None,
+        logits: torch.Tensor | None,
+        cache: SophiaCache | None,
+        return_dict: bool,
+    ) -> CausalLMOutputWithPast | tuple[torch.Tensor, ...]:
+        return _format_hf_causal_lm_output(
+            output_cls=CausalLMOutputWithPast,
+            loss=loss,
+            logits=logits,
+            past_key_values=cache,
+            return_dict=return_dict,
+        )

pretrained_bundle.py

@@ -0,0 +1,118 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from dataclasses import is_dataclass
+from dataclasses import asdict
+import json
+import os
+
+import torch
+from safetensors.torch import load_file as load_safetensors_file
+from safetensors.torch import save_file as save_safetensors_file
+
+
+def save_canonical_config_bundle(
+    config: object,
+    *,
+    save_directory: str | os.PathLike,
+) -> None:
+    save_dir = os.path.abspath(str(save_directory))
+    os.makedirs(save_dir, exist_ok=True)
+    payload = asdict(config) if is_dataclass(config) else dict(vars(config))
+    config_path = os.path.join(save_dir, "config.json")
+    with open(config_path, "w", encoding="utf-8", newline="\n") as handle:
+        json.dump(payload, handle, indent=2, sort_keys=True)
+        handle.write("\n")
+
+
+def save_pretrained_state_dict(
+    state_dict: dict[str, torch.Tensor],
+    *,
+    save_directory: str | os.PathLike,
+    safe_serialization: bool,
+) -> None:
+    save_dir = os.path.abspath(str(save_directory))
+    os.makedirs(save_dir, exist_ok=True)
+    export_state: dict[str, torch.Tensor] = {}
+    seen_cpu_storages: set[tuple[int, int]] = set()
+    for key, value in dict(state_dict).items():
+        tensor = value.detach().cpu()
+        storage = tensor.untyped_storage()
+        storage_key = (int(storage.data_ptr()), int(storage.nbytes()))
+        if storage_key in seen_cpu_storages:
+            tensor = tensor.clone(memory_format=torch.preserve_format)
+            storage = tensor.untyped_storage()
+            storage_key = (int(storage.data_ptr()), int(storage.nbytes()))
+        seen_cpu_storages.add(storage_key)
+        export_state[str(key)] = tensor
+    if bool(safe_serialization):
+        save_safetensors_file(
+            export_state,
+            os.path.join(save_dir, "model.safetensors"),
+        )
+        bin_path = os.path.join(save_dir, "pytorch_model.bin")
+        if os.path.exists(bin_path):
+            os.remove(bin_path)
+        return
+    torch.save(export_state, os.path.join(save_dir, "pytorch_model.bin"))
+
+
+def load_pretrained_state_dict(
+    export_dir: str | os.PathLike,
+) -> dict[str, torch.Tensor]:
+    resolved_export_dir = os.path.abspath(str(export_dir))
+    safetensors_path = os.path.join(resolved_export_dir, "model.safetensors")
+    pytorch_path = os.path.join(resolved_export_dir, "pytorch_model.bin")
+    if os.path.isfile(safetensors_path):
+        return load_safetensors_file(safetensors_path)
+    if os.path.isfile(pytorch_path):
+        raw_state = torch.load(pytorch_path, map_location="cpu", weights_only=True)
+        if not isinstance(raw_state, dict):
+            raise RuntimeError(
+                f"unexpected state_dict payload type: {type(raw_state)!r}"
+            )
+        return raw_state
+    raise RuntimeError(
+        "exported model directory has no supported model weights: "
+        f"{resolved_export_dir}"
+    )
+
+
+def load_canonical_config_from_pretrained[ConfigT](
+    pretrained_model_name_or_path: str | os.PathLike,
+    *,
+    config_cls: type[ConfigT],
+) -> ConfigT:
+    config_path = os.path.join(
+        os.path.abspath(str(pretrained_model_name_or_path)),
+        "config.json",
+    )
+    try:
+        with open(config_path, encoding="utf-8") as handle:
+            raw = json.load(handle)
+    except FileNotFoundError as exc:
+        raise RuntimeError(f"missing exported config.json: {config_path}") from exc
+    except json.JSONDecodeError as exc:
+        raise RuntimeError(f"invalid exported config.json: {config_path}") from exc
+    if not isinstance(raw, dict):
+        raise RuntimeError(
+            f"exported config.json must contain a JSON object: {config_path}"
+        )
+    from_pretrained_mapping = getattr(config_cls, "from_pretrained_mapping", None)
+    if callable(from_pretrained_mapping):
+        return from_pretrained_mapping(dict(raw))
+    from_mapping = getattr(config_cls, "from_mapping", None)
+    if callable(from_mapping):
+        return from_mapping(dict(raw))
+    return config_cls(**dict(raw))
+
+
+__all__ = [
+    "load_canonical_config_from_pretrained",
+    "load_pretrained_state_dict",
+    "save_canonical_config_bundle",
+    "save_pretrained_state_dict",
+]

runtime_backend.py

@@ -0,0 +1,53 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from importlib import import_module
+from typing import TYPE_CHECKING, TypeVar
+
+from .runtime_contracts import RuntimeBackedModel
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+
+RUNTIME_MODULE_NAME = ".sophia_runtime"
+RuntimeModelArgsT = TypeVar("RuntimeModelArgsT")
+RuntimeTransformerT = TypeVar("RuntimeTransformerT", bound=RuntimeBackedModel)
+
+
+@dataclass(frozen=True)
+class RuntimeBackend:
+    module: ModuleType | None
+    model_args_cls: type[RuntimeModelArgsT]
+    transformer_cls: type[RuntimeTransformerT]
+
+
+def _import_runtime_module(module_name: str) -> ModuleType:
+    if module_name.startswith("."):
+        package = __package__
+        if not package:
+            raise ImportError(
+                "relative runtime module import requires a package context"
+            )
+        return import_module(module_name, package=package)
+    return import_module(module_name)
+
+
+def resolve_runtime_backend() -> RuntimeBackend:
+    runtime_module = _import_runtime_module(RUNTIME_MODULE_NAME)
+    return RuntimeBackend(
+        module=runtime_module,
+        model_args_cls=runtime_module.ModelArgs,
+        transformer_cls=runtime_module.Transformer,
+    )
+
+
+__all__ = [
+    "RUNTIME_MODULE_NAME",
+    "RuntimeBackend",
+    "resolve_runtime_backend",
+]

runtime_contracts.py

@@ -0,0 +1,107 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from threading import RLock
+from typing import Protocol, runtime_checkable
+
+import torch
+
+from .model_state import RuntimeCacheSnapshot
+
+
+@runtime_checkable
+class SupportsRuntimeStateRefreshControl(Protocol):
+    def refresh_state_buffers(self) -> None: ...
+
+
+@runtime_checkable
+class SupportsRuntimeStateResetControl(Protocol):
+    def reset_runtime_cache(self) -> None: ...
+
+
+@runtime_checkable
+class SupportsRuntimeRecipeControl(Protocol):
+    def supports_loss_chunk_size(self) -> bool: ...
+
+    def supports_checkpoint_excludes(self) -> bool: ...
+
+    def runtime_recipe_knobs(self) -> tuple[int, int, int]: ...
+
+    def apply_runtime_recipe_knobs(
+        self,
+        *,
+        loss_chunk_size: int,
+        gradient_checkpointing_exclude_first: int,
+        gradient_checkpointing_exclude_last: int,
+    ) -> tuple[int, int, int]: ...
+
+    def ensure_runtime_max_seq_len(self, max_seq_len: int) -> None: ...
+
+    def runtime_max_seq_len(self) -> int: ...
+
+    def sync_runtime_batch_capacity(self, max_batch_size: int) -> int: ...
+
+
+@runtime_checkable
+class RuntimeHost(
+    SupportsRuntimeStateRefreshControl,
+    SupportsRuntimeStateResetControl,
+    SupportsRuntimeRecipeControl,
+    Protocol,
+):
+    def rebuild_runtime_buffers(self) -> None: ...
+
+    def replay_with_cache(
+        self,
+        input_ids: torch.Tensor,
+        start_pos: int = 0,
+        return_all_logits: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]: ...
+
+    def forward_with_last_hidden(
+        self,
+        input_ids: torch.Tensor,
+        start_pos: int = 0,
+        return_all_logits: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]: ...
+
+    def cache_dump(
+        self,
+        device: str = "cpu",
+        *,
+        cache_pos: int | None = None,
+        batch_size: int | None = None,
+    ) -> RuntimeCacheSnapshot: ...
+
+    def cache_load(self, cache_snapshot: RuntimeCacheSnapshot) -> None: ...
+
+
+@runtime_checkable
+class RuntimeBackedModel(Protocol):
+    runtime: RuntimeHost
+    runtime_lock: RLock
+
+
+@runtime_checkable
+class Runtime(Protocol):
+    @property
+    def runtime_model(self) -> RuntimeBackedModel: ...
+
+    @property
+    def runtime_host(self) -> RuntimeHost: ...
+
+    @property
+    def runtime_lock(self) -> RLock: ...
+
+
+__all__ = [
+    "RuntimeBackedModel",
+    "RuntimeHost",
+    "Runtime",
+    "SupportsRuntimeRecipeControl",
+    "SupportsRuntimeStateRefreshControl",
+    "SupportsRuntimeStateResetControl",
+]

runtime_linear.py

@@ -0,0 +1,46 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+"""Runtime linear layers used by the model."""
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as functional
+from torch import nn
+
+
+class RuntimeLinear(nn.Module):
+    """Plain linear layer used across the runtime model."""
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        *,
+        bias: bool = False,
+    ) -> None:
+        super().__init__()
+        self.in_features = int(in_features)
+        self.out_features = int(out_features)
+        self.weight = nn.Parameter(torch.empty(self.out_features, self.in_features))
+        self.bias = nn.Parameter(torch.empty(self.out_features)) if bool(bias) else None
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        nn.init.normal_(self.weight, mean=0.0, std=0.02)
+        if self.bias is not None:
+            nn.init.zeros_(self.bias)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return functional.linear(x, self.weight, self.bias)
+
+    def extra_repr(self) -> str:
+        return (
+            f"in_features={self.in_features}, out_features={self.out_features}, "
+            f"bias={self.bias is not None}"
+        )
+
+
+__all__ = ["RuntimeLinear"]

semantics.py

@@ -0,0 +1,135 @@
+# Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
+# Exported for HuggingFace trust_remote_code loading.
+# This file is intentionally self-contained.
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+import math
+
+
+PINNED_SEMANTIC_ADAM_EPS = 1e-8
+
+
+def canonicalize_model_values(values: Mapping[str, object]) -> dict[str, object]:
+    normalized: dict[str, object] = dict(values)
+    integer_fields = (
+        "vocab_size",
+        "dim",
+        "n_layers",
+        "num_heads",
+        "head_dim",
+        "ffn_hidden",
+        "kda_decay_rank",
+        "kda_output_gate_rank",
+        "mla_q_rank",
+        "mla_kv_rank",
+        "short_conv_kernel",
+        "attn_res_block_size",
+        "max_seq_len",
+        "max_batch_size",
+    )
+    float_fields = (
+        "kda_decay_lower_bound",
+        "kda_dt_min",
+        "kda_dt_max",
+        "kda_dt_floor",
+        "kda_a_log_init",
+        "norm_eps",
+        "dropout",
+        "initializer_range",
+        "situ_gate_softcap",
+        "situ_up_softcap",
+    )
+    for name in integer_fields:
+        if name in normalized:
+            normalized[name] = int(normalized[name])
+    for name in float_fields:
+        if name in normalized:
+            normalized[name] = float(normalized[name])
+    if "kda_backend" in normalized:
+        normalized["kda_backend"] = str(normalized["kda_backend"])
+    if "kda_output_gate_full_rank" in normalized:
+        normalized["kda_output_gate_full_rank"] = bool(
+            normalized["kda_output_gate_full_rank"]
+        )
+    return normalized
+
+
+def validate_model_values(values: Mapping[str, object]) -> None:
+    positive_ints = (
+        "vocab_size",
+        "dim",
+        "n_layers",
+        "num_heads",
+        "head_dim",
+        "ffn_hidden",
+        "kda_decay_rank",
+        "kda_output_gate_rank",
+        "mla_q_rank",
+        "mla_kv_rank",
+        "short_conv_kernel",
+        "attn_res_block_size",
+        "max_seq_len",
+        "max_batch_size",
+    )
+    for name in positive_ints:
+        value = int(values[name])
+        if value <= 0:
+            raise ValueError(f"{name} must be > 0, got {value}")
+
+    lower_bound = float(values["kda_decay_lower_bound"])
+    if not -5.0 <= lower_bound < 0.0:
+        raise ValueError(
+            "kda_decay_lower_bound must be in [-5, 0), "
+            f"got {lower_bound}"
+        )
+    dt_min = float(values["kda_dt_min"])
+    dt_max = float(values["kda_dt_max"])
+    dt_floor = float(values["kda_dt_floor"])
+    a_log_init = float(values["kda_a_log_init"])
+    for name, value in (
+        ("kda_dt_min", dt_min),
+        ("kda_dt_max", dt_max),
+        ("kda_dt_floor", dt_floor),
+    ):
+        if not math.isfinite(value) or value <= 0.0:
+            raise ValueError(f"{name} must be finite and > 0, got {value}")
+    if dt_min > dt_max:
+        raise ValueError(
+            f"kda_dt_min must be <= kda_dt_max, got {dt_min} > {dt_max}"
+        )
+    if dt_floor > dt_min:
+        raise ValueError(
+            f"kda_dt_floor must be <= kda_dt_min, got {dt_floor} > {dt_min}"
+        )
+    if not math.isfinite(a_log_init):
+        raise ValueError(f"kda_a_log_init must be finite, got {a_log_init}")
+    norm_eps = float(values["norm_eps"])
+    if not math.isfinite(norm_eps) or norm_eps <= 0.0:
+        raise ValueError(f"norm_eps must be finite and > 0, got {norm_eps}")
+    dropout = float(values["dropout"])
+    if not 0.0 <= dropout < 1.0:
+        raise ValueError(f"dropout must be in [0, 1), got {dropout}")
+    initializer_range = float(values["initializer_range"])
+    if not math.isfinite(initializer_range) or initializer_range <= 0.0:
+        raise ValueError(
+            f"initializer_range must be finite and > 0, got {initializer_range}"
+        )
+    for name in ("situ_gate_softcap", "situ_up_softcap"):
+        value = float(values[name])
+        if not math.isfinite(value) or value <= 0.0:
+            raise ValueError(f"{name} must be finite and > 0, got {value}")
+    backend = str(values.get("kda_backend", "auto"))
+    if backend not in {"auto", "reference", "fla"}:
+        raise ValueError(
+            "kda_backend must be one of auto/reference/fla, "
+            f"got {backend!r}"
+        )
+
+
+__all__ = [
+    "PINNED_SEMANTIC_ADAM_EPS",
+    "canonicalize_model_values",
+    "validate_model_values",
+]