Add files using upload-large-folder tool

ADAPTER_MANIFEST.json

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LICENSE

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MODULE_SCALE_MANIFEST.json

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README.md

@@ -0,0 +1,128 @@
+---
+license: apache-2.0
+library_name: peft
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-4B-Base
+base_model_relation: adapter
+tags:
+  - peft
+  - lora
+  - transformers
+  - safetensors
+  - qwen3
+  - code
+  - text-generation
+model-index:
+  - name: Qwen3-4B-Base-ThinkCode-A-U025 PEFT Adapter
+    results:
+      - task:
+          type: text-generation
+          name: Code Generation
+        dataset:
+          name: EvalScope Full1055 corrected (development-only)
+          type: evalscope-full1055-corrected-development
+        metrics:
+          - type: pass@1
+            name: code_only pass@1 (seed 3407)
+            value: 25.21
+---
+
+# Qwen3-4B-Base-ThinkCode-A-U025 — PEFT Adapter
+
+This repository contains a **PEFT LoRA adapter only**. It does not contain the
+Qwen3 base-model weights and cannot be loaded as a standalone causal language
+model.
+
+The required base is
+[`Qwen/Qwen3-4B-Base`](https://huggingface.co/Qwen/Qwen3-4B-Base) at the fixed
+revision `906bfd4b4dc7f14ee4320094d8b41684abff8539`.
+
+## Adapter construction
+
+`A-U025` is the Phase A uniform-scale arm. Starting from the completed source
+LoRA, every selected LoRA `B` tensor—including the `lm_head` adapter—is
+multiplied by `0.25` in FP32. LoRA `A` tensors are unchanged. With
+`lora_alpha=128` and `r=64`, PEFT therefore applies the exact intended
+`0.25×` source delta to all 253 adapted modules.
+
+Because Qwen3 ties `lm_head.weight` to `embed_tokens.weight`, the released
+standard-PEFT representation stores the head factors as transposed
+`embed_tokens` LoRA factors and sets `ensure_weight_tying=true`. PEFT then
+shares that adapter with the tied output layer, matching both input-embedding
+and output-head effects without storing any base-layer tensor.
+
+The repository includes `MODULE_SCALE_MANIFEST.json`, which records every
+logical module, tensor key, physical base weight, and scale. This release is
+from the completed Phase A delta-scaling line; it is **not** the later failed
+NEXTGEN route and does not include subsequent protocol-repair experiments.
+
+## Loading with PEFT
+
+Use recent `transformers` and `peft` versions. Load the fixed base first, then
+attach this adapter:
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_id = "Qwen/Qwen3-4B-Base"
+base_revision = "906bfd4b4dc7f14ee4320094d8b41684abff8539"
+adapter_id = "modrill/Qwen3-4B-Base-ThinkCode-A-U025"
+
+tokenizer = AutoTokenizer.from_pretrained(base_id, revision=base_revision)
+base = AutoModelForCausalLM.from_pretrained(
+    base_id,
+    revision=base_revision,
+    torch_dtype="auto",
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(base, adapter_id)
+
+messages = [{"role": "user", "content": "Write a Python function that checks whether a number is prime."}]
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=False,
+)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+eos_ids = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|im_end|>"),
+]
+outputs = model.generate(**inputs, max_new_tokens=2048, eos_token_id=eos_ids)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
+```
+
+The base tokenizer's chat template supports `enable_thinking`. Disable it for
+direct code generation matching the concise screening style, or enable it when
+explicit reasoning is desired. Pass both `<|endoftext|>` and `<|im_end|>` as
+EOS IDs. Keep the combined prompt and generated sequence within **32K tokens**,
+the fixed base model configuration limit, unless a separate long-context
+extension is validated.
+
+## Development evaluation
+
+On the corrected EvalScope Full1055 development suite, the preregistered
+`seed=3407` `code_only` result was **266/1055 = 25.21%**. Independent forward
+and reverse scoring produced **0 verdict flips**.
+
+This is a **single-seed development screening result**, not formal
+confirmation, a held-out estimate, or a multi-seed aggregate. No aggregate from
+`A-NH025` is attributed to this adapter.
+
+## Limitations
+
+- This adapter requires the exact base model and should not be loaded alone.
+- The published evidence is development-only and single-seed.
+- Generated code can be incorrect, insecure, or non-compiling; sandbox and
+  test it independently.
+- No production safety, security, or suitability certification is implied.
+
+## License
+
+The fixed base card and included license identify Apache-2.0. This adapter
+preserves that license text and metadata. Users should independently verify the
+upstream Qwen3 license, notices, training-data terms, and applicability to their
+use case.
+

VALIDATION.json

@@ -0,0 +1 @@
+{"arm_id":"A-U025","base_model":"Qwen/Qwen3-4B-Base","base_revision":"906bfd4b4dc7f14ee4320094d8b41684abff8539","fixed_prompt_fp32_quantization_isolated_logits":[{"max_abs":0.13026046752929688,"mean_abs":0.01463189348578453,"p99_abs":0.04955993592739105,"prompt_index":0,"top10_overlap":10,"top1_equal":true},{"max_abs":0.11943221092224121,"mean_abs":0.030642252415418625,"p99_abs":0.07536689192056656,"prompt_index":1,"top10_overlap":9,"top1_equal":true},{"max_abs":0.09069585800170898,"mean_abs":0.02466517873108387,"p99_abs":0.057938352227211,"prompt_index":2,"top10_overlap":10,"top1_equal":true}],"logit_gate":{"max_abs_le":0.25,"mean_abs_le":0.05,"top10_overlap_min":9,"top1_all_equal":true},"phase_a_canary_status":"PASS","representative_bf16_weight_parity":[{"bf16_exact_samples":6,"logical_module":"model.layers.0.self_attn.q_proj","max_abs":0.0,"physical_weight":"model.layers.0.self_attn.q_proj.weight","sample_count":6},{"bf16_exact_samples":6,"logical_module":"model.layers.23.mlp.down_proj","max_abs":0.0,"physical_weight":"model.layers.23.mlp.down_proj.weight","sample_count":6},{"bf16_exact_samples":6,"logical_module":"model.layers.24.mlp.up_proj","max_abs":0.0,"physical_weight":"model.layers.24.mlp.up_proj.weight","sample_count":6},{"bf16_exact_samples":6,"logical_module":"model.layers.35.self_attn.o_proj","max_abs":0.0,"physical_weight":"model.layers.35.self_attn.o_proj.weight","sample_count":6},{"bf16_exact_samples":6,"logical_module":"lm_head","max_abs":0.0,"physical_weight":"model.embed_tokens.weight","sample_count":6}],"schema":"HF_PEFT_ADAPTER_VALIDATION_V1","status":"PASS","tensor_contract":{"base_layer_tensor_absent":true,"body_scale":0.25,"lm_head_scale":0.25,"peft_tied_adapter_storage_verified":true,"source_scaling_all_exact":true},"validated_at_utc":"2026-08-22T18:05:45Z"}

adapter_config.json

@@ -0,0 +1,51 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3-4B-Base",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": true,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": "906bfd4b4dc7f14ee4320094d8b41684abff8539",
+  "target_modules": [
+    "down_proj",
+    "embed_tokens",
+    "gate_proj",
+    "k_proj",
+    "o_proj",
+    "q_proj",
+    "up_proj",
+    "v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}

adapter_model.safetensors

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+oid sha256:e23f1eb30f6b2f3677dde70f250aea6fe11268f67f135e42425e3355c7f56822
+size 607652912