Agnes-AI 发布 Agnes-3.0-Flash Preview 开放权重多模态模型

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. 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. The label "Agnes-3.0-Flash" in the benchmark image refers to this Preview checkpoint. 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 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 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): 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: ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", reasoning_effort="medium") 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: 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) 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. 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: 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. Citation @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/} } Downloads last month 474 Safetensors Model size 33B params Tensor type BF16 · Chat template Files info Model tree for Agnes-AI/Agnes-3.0-Flash Quantizations 7 models