--- language: - en - zh license: apache-2.0 library_name: transformers pipeline_tag: image-text-to-text tags: - agnes-ai - uncensored - reasoning - multimodal - fp8 - w8a8 - vllm - sglang - long-context - hybrid-attention --- # Agnes 3.0 Flash FP8 (W8A8, calibrated) Full FP8 quantization of Agnes-3.0-Flash Preview (33B parameters, multimodal). Weights and activations are FP8 e4m3 with static per-tensor scales, calibrated with LLM Compressor. This gives you both the model and context at FP8. Text, image, and video inputs are preserved, and the chat template is upstream's. The Sglang patch from the original model checkpoint is included here as well. *Note that this model seems to be naturally uncensored. I ran Heretic's assessment and it gave 0/300 refusals. So no need to abliterate this model. And with that, my usual warning here:* > **Uncensored model:** the language checkpoint has undergone abliteration to reduce refusal behavior. Treat outputs as untrusted, apply application-level safeguards, and do not assume the model will decline harmful requests. > **User responsibility:** this model is provided without warranty. The creators, uploaders, and maintainers are not responsible or liable for what others generate, publish, deploy, or otherwise do with this abliterated model. Users must operate it responsibly, apply appropriate safeguards, comply with applicable law, and respect third-party rights. This model is for research purposes only and is not intended for production use. ## Quantization recipe | Field | Value | |---|---| | Format | `compressed-tensors`, `float-quantized` | | Weights | FP8 (e4m3), per-tensor symmetric, static | | Activations | FP8 (e4m3), per-tensor symmetric, static, calibrated | | Targets | all `Linear` modules except the protected list below | | Toolkit | llm-compressor 0.13.0, compressed-tensors 0.18.0 | | Calibration | `HuggingFaceH4/ultrachat_200k`, split `train_sft`, revision `8049631c405ae6576f93f445c6b8166f76f5505a` | | Calibration size | 512 samples, max sequence length 2048, batch 1, seed 42 | Kept at original precision: `lm_head`, `embed_tokens`, the full `model.visual` tower, the recurrent-state layers of delta attention (`conv1d`, `in_proj_a`, `in_proj_b`), and every MTP weight (shipped unquantized in `model-mtp.safetensors`). `agnes_quantization_manifest.json` records the full build, including the SHA-256 of the calibration prompt ids used, so the run is reproducible piece for piece. `recipe.yaml` is the machine-readable form of the table above. ## Why the quantization runs on a fused checkpoint The upstream FFN carries two branches per layer: a main branch (width 17,408) and a parallel branch (width 2,048). This checkpoint folds them into a single set of projections per layer (`intermediate_size: 19456`, `parallel_ffn_intermediate_size: 0`). Fusing in bf16 is concatenation and it is exact: gate/up join along the output dimension, down along the input dimension. Static per-tensor FP8 scales cannot be merged after the fact, because each branch carries its own scale and a single fused matrix has room for exactly one. Quantizing the fused layout means every serving tensor is quantized once, from the values the model will actually run, and calibration observes the same matrix geometry as inference. ## Numerics The fused layout measures 5.9e-4 full-vocabulary KL against the two-branch layout at identical weights. The residual comes from reduction order in the fused projections, and it sits below the 6.5e-4 that independent inference stacks produce from the same unquantized checkpoint. Perplexity moves 17.05 to 17.07 across the fusion. The FP8 conversion itself has no formal before/after eval on this card. Run your own benchmarks before trusting it for a workload. ## Files | Group | Contents | |---|---| | Weights | `model-00001-of-00002.safetensors`, `model-00002-of-00002.safetensors`, `model-mtp.safetensors`, `model.safetensors.index.json` | | Config | `config.json`, `generation_config.json`, custom `*_agnes.py` modeling and processor code | | Tokenizer | `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja` | | Provenance | `recipe.yaml`, `agnes_quantization_manifest.json` | | Extras | `sglang_patch/`, `serve.sh` | The checkpoint is standard `compressed-tensors float-quantized`, so any engine that reads that format can load it. ## Upstream and license Derived from the `Agnes-AI/Agnes-3.0-Flash` Preview checkpoint at revision `891ce4f9ffb89b22888aa7fcc2bb2f3618867684`, Apache 2.0, unchanged. This is a community quantization and is not affiliated with Agnes AI. The open-weight Preview differs from the production/API model, and the upstream benchmark figures below describe the Preview checkpoint before quantization. --- **The original upstream model card follows, unchanged.**

Agnes AI website Open weights Apache 2.0

# Agnes-3.0-Flash Preview ## Model version clarification This repository contains an earlier open-weight **Preview checkpoint** of Agnes 3.0 Flash. It is distinct from the newer **production/API checkpoint** listed on [Artificial Analysis](https://artificialanalysis.ai/models/agnes-3-0-flash). The Preview release has **33B parameters** and a context window of **262,144 tokens**. The production/API model uses a different checkpoint and configuration, with a **1M-token context window**. Its benchmark results should not be attributed to the Preview weights released here. This repository was initially published as `Agnes-3.0-Flash` without the `Preview` suffix. The model card now explicitly identifies this release as **Agnes-3.0-Flash Preview** to clarify the distinction between the open-weight release and the production/API model. The specifications and Agnes benchmark results below refer to the **Preview checkpoint**. Hello! 👋 Today we are introducing **Agnes-3.0-Flash Preview**, an **open-weights multimodal preview model** built for people who want flagship-class reasoning without flagship-class hardware. Highlights: - **Competitive across core capabilities.** Agnes-3.0-Flash Preview posts competitive results across reasoning, coding, and instruction-following evaluations. - **Built for demanding work.** A **262 144-token context window**, adjustable reasoning effort, tool calling, and **text, image and video** understanding. ### Benchmarks > **Benchmark scope:** The Agnes results in the chart and table below belong to the **Agnes-3.0-Flash Preview open-weight checkpoint** released in this repository. They are not results for the production/API Agnes 3.0 Flash model listed on Artificial Analysis.

Agnes-3.0-Flash Preview benchmark reference results

The Agnes-3.0-Flash Preview scores in the chart correspond to the open-weight checkpoint released in this repository. Reference results across contemporary models are shown below. The figures were compiled from different sources, harnesses, and model snapshots and do not constitute a controlled head-to-head comparison.
Benchmark Agnes-3.0-Flash Preview Qwen3.6-35B-A3B
35B / 3B active		 Kimi K2.5
1T / 32B active		 Muse Glimmer
30B		 Qwen3.5
27B		 DeepSeek V4 Flash 0731
284B / 13B active		 Qwen3.8
27B		 Gemini 3.5 Flash
undisclosed		 Qwen3.8 Flash Next
125B / 6B active		 MiniMax M3
428B / 23B active
IFBench74.2064.443.777.075.675.879.576.381.382.9
SciCode38.0835.839.643.639.550.346.653.150.645.4
GPQA Diamond85.0584.178.983.585.890.890.592.292.392.9
AA-LCR68.3366.759.080.072.379.782.081.079.774.0
AA-Omniscience Accuracy23.0018.822.927.020.740.418.451.424.516.7

Higher is better for every row. Header parameter figures mix total and active counts, and harnesses and snapshot dates differ across sources, so treat cross-column comparisons as reference values rather than a controlled head-to-head evaluation.

## Architecture Agnes-3.0-Flash Preview is a hybrid-attention decoder: three of every four layers run a gated delta rule (recurrent, with per-layer state independent of sequence length), and the fourth runs standard global attention. Only 18 of the 72 layers therefore hold a KV cache that grows with context. | | | |---|---| | Context length | **262 144** tokens | | Decoder layers | 72 = 54 delta-rule recurrent + 18 global attention, alternating 3 : 1 | | Hidden size | 5120 | | Global attention | 24 query heads / 4 KV heads (6 : 1 GQA), head dim 256; RMS-norm on q and k, sigmoid-gated output | | Delta-rule layers | 16 key heads / 48 value heads, head dim 128; causal conv (kernel 4) in front, gated RMS-norm; recurrent state in fp32 | | Feed-forward | SwiGLU, intermediate size 17408; plus a parallel SwiGLU 2048 branch in every layer | | Positions | 3-axis rotary (text / height / width), interleaved mrope sections 11 : 11 : 10, base 1e7, applied to the first 25 % of each head dim (64 dims) | | Vocabulary | 248 320 | | Vision tower | 27 layers, hidden 1152, patch 16, 2 × 2 spatial merge, projected to 5120 | ## Quickstart
REMOTE CODE REQUIRED

Agnes-3.0-Flash Preview ships its own model implementation. Always load it with trust_remote_code=True.

### Requirements ```bash pip install "transformers>=5.12" torch torchvision accelerate ``` Tested on transformers 5.12.1. Image and video inputs go through the bundled processor, which needs `torchvision`. ### Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer path = "Agnes-AI/Agnes-3.0-Flash" tok = AutoTokenizer.from_pretrained(path) model = AutoModelForCausalLM.from_pretrained( path, dtype="bfloat16", device_map="auto", trust_remote_code=True ) msgs = [{"role": "user", "content": "请用三句话解释什么是人工智能。"}] ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device) out = model.generate(ids, max_new_tokens=256) print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)) ``` ### Images and video Image and video inputs go through the bundled processor (also remote code): ```python from transformers import AutoProcessor proc = AutoProcessor.from_pretrained(path, trust_remote_code=True) msgs = [{"role": "user", "content": [{"type": "image", "image": "photo.jpg"}, {"type": "text", "text": "描述这张图。"}]}] inputs = proc.apply_chat_template(msgs, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device) out = model.generate(**inputs, max_new_tokens=256) print(proc.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0]) ``` ### Reasoning effort The chat template exposes three reasoning levels — `high` (default), `medium`, `low` — plus a thinking-off switch: ```python ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", reasoning_effort="medium") # or enable_thinking=False ``` ### Tool calling The chat template renders tool definitions for you. The model emits calls as ``, and you feed results back as a `tool` role message: ```python tools = [{ "type": "function", "function": { "name": "get_weather", "description": "Look up current weather for a city", "parameters": { "type": "object", "properties": {"city": {"type": "string", "description": "City name"}}, "required": ["city"], }, }, }] msgs = [{"role": "user", "content": "What's the weather in Beijing right now?"}] ids = tok.apply_chat_template(msgs, tools=tools, add_generation_prompt=True, return_tensors="pt").to(model.device) out = model.generate(ids, max_new_tokens=256) reply = tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True) # # # # Beijing # # # # run the tool, append the result, generate the final answer msgs += [{"role": "assistant", "content": reply}, {"role": "tool", "content": "Clear, 26°C, light northeasterly wind"}] ``` Over the OpenAI API pass `tools=` the same way. The server returns the text above verbatim by default; to get structured `tool_calls`, configure sglang with a tool-call parser matching this format (likewise a reasoning parser, if you want the thinking span in `reasoning_content`). ### SGLang `serve.sh` starts a server from a stock public image, overlaying three files onto the image's `sglang` package and nothing else. See `sglang_patch/README.md`. ```bash docker run --gpus all --shm-size 64g -p 30001:8080 \ -v /path/to/agnes-3.0-flash:/model \ lmsysorg/sglang:nightly-dev-20260908-20ca564b \ bash /agnes-3.0-flash/serve.sh --served-model-name Agnes-3.0-Flash ``` `serve.sh` forwards extra command-line arguments to sglang, which is how `--served-model-name` takes effect; `--tp 2` works the same way. The server listens on port 8080 inside the container: ```python from openai import OpenAI client = OpenAI(api_key="EMPTY", base_url="http://localhost:30001/v1") response = client.chat.completions.create( model="Agnes-3.0-Flash", messages=[{"role": "user", "content": "Design a fault-tolerant event processing architecture."}], temperature=1.0, max_tokens=2000, ) print(response.choices[0].message.content) ``` Pass `stream=True` for streaming; `tools=` and `reasoning_effort=` are accepted the same way. ## Hardware Requirements | Resource | Recommendation | |---|---| | GPUs | 1 × NVIDIA H200 141 GB or NVIDIA H100 80 GB (or equivalent) at bf16 | | Tensor parallel | `--tp 1`; `--tp 2` for maximum context and concurrency | | Weights on disk | Approximately 66 GB for the bf16 checkpoint | | Host memory | 128 GB or more recommended | Actual context length and concurrency depend on KV-cache allocation, runtime overhead, and tensor-parallel configuration; validate the target workload on the intended hardware. ## Recommended Inference Settings | Setting | Recommended | |---|---| | `temperature` | 1.0 | | `top_p` | 0.95 | | `top_k` | 20 | | `reasoning_effort` | `high` for hard reasoning, `low` for latency-sensitive traffic | | `max_tokens` | 2000 or higher | These are the checkpoint's own `generation_config.json` defaults. ## Model Capabilities | Capability | Support | |---|---| | Advanced reasoning | Yes, with `high` / `medium` / `low` effort levels | | Coding and debugging | Yes | | Long-context analysis | 262 144 tokens | | Image understanding | Yes | | Video understanding | Yes | | Tool calling | Yes (`` / ``) | | Streaming | Yes | | OpenAI-compatible APIs | Chat Completions via sglang | ## License Released under the [Apache License 2.0](LICENSE). ## Citation ```bibtex @misc{agnes30flash2026, title = {Agnes-3.0-Flash Preview}, author = {{Agnes AI}}, year = {2026}, month = sep, howpublished = {Open-weights preview checkpoint}, url = {https://agnes-ai.com/} } ```