--- title: Agnes-3.0-Flash canonical_url: "https://www.modelscope.cn/models/Agnes-AI/Agnes-3.0-Flash" md_url: "https://www.modelscope.cn/models/Agnes-AI/Agnes-3.0-Flash.md" repository: Agnes-AI/Agnes-3.0-Flash last_updated: 2026-09-16 license: apache-2.0 pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - agnes architectures: - AgnesForConditionalGeneration parameters: 33.1B tensor_type: - BF16 library_name: - transformer - safetensors - pytorch frameworks: - pytorch language: - en - zh downloads: 682 stars: 44 tags: - agnes-ai - reasoning - multimodal - long-context - hybrid-attention --- # Agnes-3.0-Flash > Agnes-3.0-Flash - Agnes-AI 在 ModelScope 开源的模型。Agnes-3.0-Flash Preview Agnes-AI/Agnes-3.0-Flash 是 ModelScope 魔搭社区上的 33.1B 参数image-text-to-text模型，采用 apache-2.0 许可。 - **Repository**: Agnes-AI/Agnes-3.0-Flash - **License**: apache-2.0 - **Tasks**: image-text-to-text - **Parameters**: 33.1B - **Tags**: agnes-ai, reasoning, multimodal, long-context, hybrid-attention - **Downloads**: 682 - **Stars**: 44 - **Last updated**: 2026-09-16 Source: https://www.modelscope.cn/models/Agnes-AI/Agnes-3.0-Flash ---

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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/} } ```