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--- |
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tags: |
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- unsloth |
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library_name: transformers |
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license: apache-2.0 |
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license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507-FP8/blob/main/LICENSE |
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pipeline_tag: text-generation |
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base_model: |
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- Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 |
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--- |
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> [!NOTE] |
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> Includes Unsloth **chat template fixes**! <br> For `llama.cpp`, use `--jinja` |
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> |
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<div> |
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<p style="margin-top: 0;margin-bottom: 0;"> |
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<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em> |
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</p> |
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<div style="display: flex; gap: 5px; align-items: center; "> |
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<a href="https://github.com/unslothai/unsloth/"> |
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<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> |
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</a> |
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<a href="https://discord.gg/unsloth"> |
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<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> |
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</a> |
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<a href="https://docs.unsloth.ai/"> |
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<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> |
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</a> |
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</div> |
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</div> |
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# Qwen3-235B-A22B-Instruct-2507-FP8 |
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<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;"> |
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<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> |
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</a> |
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## Highlights |
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We introduce the updated version of the **Qwen3-235B-A22B-FP8 non-thinking mode**, named **Qwen3-235B-A22B-Instruct-2507-FP8**, featuring the following key enhancements: |
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- **Significant improvements** in general capabilities, including **instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage**. |
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- **Substantial gains** in long-tail knowledge coverage across **multiple languages**. |
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- **Markedly better alignment** with user preferences in **subjective and open-ended tasks**, enabling more helpful responses and higher-quality text generation. |
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- **Enhanced capabilities** in **256K long-context understanding**. |
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## Model Overview |
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This repo contains the FP8 version of **Qwen3-235B-A22B-Instruct-2507**, which has the following features: |
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- Type: Causal Language Models |
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- Training Stage: Pretraining & Post-training |
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- Number of Parameters: 235B in total and 22B activated |
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- Number of Paramaters (Non-Embedding): 234B |
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- Number of Layers: 94 |
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- Number of Attention Heads (GQA): 64 for Q and 4 for KV |
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- Number of Experts: 128 |
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- Number of Activated Experts: 8 |
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- Context Length: **262,144 natively**. |
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**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.** |
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For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). |
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## Performance |
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| | Deepseek-V3-0324 | GPT-4o-0327 | Claude Opus 4 Non-thinking | Kimi K2 | Qwen3-235B-A22B Non-thinking | Qwen3-235B-A22B-Instruct-2507 | |
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|--- | --- | --- | --- | --- | --- | ---| |
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| **Knowledge** | | | | | | | |
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| MMLU-Pro | 81.2 | 79.8 | **86.6** | 81.1 | 75.2 | 83.0 | |
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| MMLU-Redux | 90.4 | 91.3 | **94.2** | 92.7 | 89.2 | 93.1 | |
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| GPQA | 68.4 | 66.9 | 74.9 | 75.1 | 62.9 | **77.5** | |
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| SuperGPQA | 57.3 | 51.0 | 56.5 | 57.2 | 48.2 | **62.6** | |
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| SimpleQA | 27.2 | 40.3 | 22.8 | 31.0 | 12.2 | **54.3** | |
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| CSimpleQA | 71.1 | 60.2 | 68.0 | 74.5 | 60.8 | **84.3** | |
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| **Reasoning** | | | | | | | |
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| AIME25 | 46.6 | 26.7 | 33.9 | 49.5 | 24.7 | **70.3** | |
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| HMMT25 | 27.5 | 7.9 | 15.9 | 38.8 | 10.0 | **55.4** | |
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| ARC-AGI | 9.0 | 8.8 | 30.3 | 13.3 | 4.3 | **41.8** | |
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| ZebraLogic | 83.4 | 52.6 | - | 89.0 | 37.7 | **95.0** | |
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| LiveBench 20241125 | 66.9 | 63.7 | 74.6 | **76.4** | 62.5 | 75.4 | |
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| **Coding** | | | | | | | |
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| LiveCodeBench v6 (25.02-25.05) | 45.2 | 35.8 | 44.6 | 48.9 | 32.9 | **51.8** | |
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| MultiPL-E | 82.2 | 82.7 | **88.5** | 85.7 | 79.3 | 87.9 | |
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| Aider-Polyglot | 55.1 | 45.3 | **70.7** | 59.0 | 59.6 | 57.3 | |
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| **Alignment** | | | | | | | |
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| IFEval | 82.3 | 83.9 | 87.4 | **89.8** | 83.2 | 88.7 | |
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| Arena-Hard v2* | 45.6 | 61.9 | 51.5 | 66.1 | 52.0 | **79.2** | |
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| Creative Writing v3 | 81.6 | 84.9 | 83.8 | **88.1** | 80.4 | 87.5 | |
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| WritingBench | 74.5 | 75.5 | 79.2 | **86.2** | 77.0 | 85.2 | |
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| **Agent** | | | | | | | |
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| BFCL-v3 | 64.7 | 66.5 | 60.1 | 65.2 | 68.0 | **70.9** | |
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| TAU-Retail | 49.6 | 60.3# | **81.4** | 70.7 | 65.2 | 71.3 | |
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| TAU-Airline | 32.0 | 42.8# | **59.6** | 53.5 | 32.0 | 44.0 | |
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| **Multilingualism** | | | | | | | |
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| MultiIF | 66.5 | 70.4 | - | 76.2 | 70.2 | **77.5** | |
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| MMLU-ProX | 75.8 | 76.2 | - | 74.5 | 73.2 | **79.4** | |
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| INCLUDE | 80.1 | **82.1** | - | 76.9 | 75.6 | 79.5 | |
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| PolyMATH | 32.2 | 25.5 | 30.0 | 44.8 | 27.0 | **50.2** | |
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*: For reproducibility, we report the win rates evaluated by GPT-4.1. |
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\#: Results were generated using GPT-4o-20241120, as access to the native function calling API of GPT-4o-0327 was unavailable. |
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## Quickstart |
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The code of Qwen3-MoE has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. |
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With `transformers<4.51.0`, you will encounter the following error: |
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``` |
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KeyError: 'qwen3_moe' |
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``` |
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The following contains a code snippet illustrating how to use the model generate content based on given inputs. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8" |
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# load the tokenizer and the model |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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# prepare the model input |
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prompt = "Give me a short introduction to large language model." |
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messages = [ |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True, |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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# conduct text completion |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=16384 |
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) |
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
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content = tokenizer.decode(output_ids, skip_special_tokens=True) |
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print("content:", content) |
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``` |
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For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: |
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- SGLang: |
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```shell |
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python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 --tp 4 --context-length 262144 |
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``` |
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- vLLM: |
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```shell |
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vllm serve Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 --tensor-parallel-size 4 --max-model-len 262144 |
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``` |
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**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.** |
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For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. |
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## Note on FP8 |
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For convenience and performance, we have provided `fp8`-quantized model checkpoint for Qwen3, whose name ends with `-FP8`. The quantization method is fine-grained `fp8` quantization with block size of 128. You can find more details in the `quantization_config` field in `config.json`. |
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You can use the Qwen3-235B-A22B-Instruct-2507-FP8 model with serveral inference frameworks, including `transformers`, `sglang`, and `vllm`, as the original bfloat16 model. |
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However, please pay attention to the following known issues: |
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- `transformers`: |
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- there are currently issues with the "fine-grained fp8" method in `transformers` for distributed inference. You may need to set the environment variable `CUDA_LAUNCH_BLOCKING=1` if multiple devices are used in inference. |
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## Agentic Use |
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Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. |
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To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. |
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```python |
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from qwen_agent.agents import Assistant |
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# Define LLM |
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llm_cfg = { |
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'model': 'Qwen3-235B-A22B-Instruct-2507-FP8', |
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# Use a custom endpoint compatible with OpenAI API: |
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'model_server': 'http://localhost:8000/v1', # api_base |
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'api_key': 'EMPTY', |
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} |
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# Define Tools |
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tools = [ |
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{'mcpServers': { # You can specify the MCP configuration file |
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'time': { |
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'command': 'uvx', |
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'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] |
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}, |
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"fetch": { |
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"command": "uvx", |
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"args": ["mcp-server-fetch"] |
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} |
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} |
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}, |
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'code_interpreter', # Built-in tools |
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] |
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# Define Agent |
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bot = Assistant(llm=llm_cfg, function_list=tools) |
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# Streaming generation |
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messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] |
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for responses in bot.run(messages=messages): |
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pass |
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print(responses) |
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``` |
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## Best Practices |
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To achieve optimal performance, we recommend the following settings: |
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1. **Sampling Parameters**: |
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- We suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. |
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- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. |
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2. **Adequate Output Length**: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models. |
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3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking. |
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- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. |
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- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." |
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### Citation |
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If you find our work helpful, feel free to give us a cite. |
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``` |
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@misc{qwen3technicalreport, |
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title={Qwen3 Technical Report}, |
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author={Qwen Team}, |
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year={2025}, |
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eprint={2505.09388}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2505.09388}, |
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} |
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``` |
