Includes Unsloth chat template fixes!
For llama.cpp, use --jinja

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

Qwen3-235B-A22B-Instruct-2507-FP8

Chat

Highlights

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:

  • Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
  • Substantial gains in long-tail knowledge coverage across multiple languages.
  • Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
  • Enhanced capabilities in 256K long-context understanding.

image/jpeg

Model Overview

This repo contains the FP8 version of Qwen3-235B-A22B-Instruct-2507, which has the following features:

  • Type: Causal Language Models
  • Training Stage: Pretraining & Post-training
  • Number of Parameters: 235B in total and 22B activated
  • Number of Paramaters (Non-Embedding): 234B
  • Number of Layers: 94
  • Number of Attention Heads (GQA): 64 for Q and 4 for KV
  • Number of Experts: 128
  • Number of Activated Experts: 8
  • Context Length: 262,144 natively.

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.

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Performance

Deepseek-V3-0324 GPT-4o-0327 Claude Opus 4 Non-thinking Kimi K2 Qwen3-235B-A22B Non-thinking Qwen3-235B-A22B-Instruct-2507
Knowledge
MMLU-Pro 81.2 79.8 86.6 81.1 75.2 83.0
MMLU-Redux 90.4 91.3 94.2 92.7 89.2 93.1
GPQA 68.4 66.9 74.9 75.1 62.9 77.5
SuperGPQA 57.3 51.0 56.5 57.2 48.2 62.6
SimpleQA 27.2 40.3 22.8 31.0 12.2 54.3
CSimpleQA 71.1 60.2 68.0 74.5 60.8 84.3
Reasoning
AIME25 46.6 26.7 33.9 49.5 24.7 70.3
HMMT25 27.5 7.9 15.9 38.8 10.0 55.4
ARC-AGI 9.0 8.8 30.3 13.3 4.3 41.8
ZebraLogic 83.4 52.6 - 89.0 37.7 95.0
LiveBench 20241125 66.9 63.7 74.6 76.4 62.5 75.4
Coding
LiveCodeBench v6 (25.02-25.05) 45.2 35.8 44.6 48.9 32.9 51.8
MultiPL-E 82.2 82.7 88.5 85.7 79.3 87.9
Aider-Polyglot 55.1 45.3 70.7 59.0 59.6 57.3
Alignment
IFEval 82.3 83.9 87.4 89.8 83.2 88.7
Arena-Hard v2* 45.6 61.9 51.5 66.1 52.0 79.2
Creative Writing v3 81.6 84.9 83.8 88.1 80.4 87.5
WritingBench 74.5 75.5 79.2 86.2 77.0 85.2
Agent
BFCL-v3 64.7 66.5 60.1 65.2 68.0 70.9
TAU-Retail 49.6 60.3# 81.4 70.7 65.2 71.3
TAU-Airline 32.0 42.8# 59.6 53.5 32.0 44.0
Multilingualism
MultiIF 66.5 70.4 - 76.2 70.2 77.5
MMLU-ProX 75.8 76.2 - 74.5 73.2 79.4
INCLUDE 80.1 82.1 - 76.9 75.6 79.5
PolyMATH 32.2 25.5 30.0 44.8 27.0 50.2

*: For reproducibility, we report the win rates evaluated by GPT-4.1.

#: Results were generated using GPT-4o-20241120, as access to the native function calling API of GPT-4o-0327 was unavailable.

Quickstart

The code of Qwen3-MoE has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers.

With transformers<4.51.0, you will encounter the following error:

KeyError: 'qwen3_moe'

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=16384
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True)

print("content:", content)

For deployment, you can use sglang>=0.4.6.post1 or vllm>=0.8.5 or to create an OpenAI-compatible API endpoint:

  • SGLang:
    python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 --tp 4 --context-length 262144
    
  • vLLM:
    vllm serve Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 --tensor-parallel-size 4 --max-model-len 262144
    

Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as 32,768.

For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.

Note on FP8

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.

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. However, please pay attention to the following known issues:

  • transformers:
    • 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.

Agentic Use

Qwen3 excels in tool calling capabilities. We recommend using 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.

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.

from qwen_agent.agents import Assistant

# Define LLM
llm_cfg = {
    'model': 'Qwen3-235B-A22B-Instruct-2507-FP8',

    # Use a custom endpoint compatible with OpenAI API:
    'model_server': 'http://localhost:8000/v1',  # api_base
    'api_key': 'EMPTY',
}

# Define Tools
tools = [
    {'mcpServers': {  # You can specify the MCP configuration file
            'time': {
                'command': 'uvx',
                'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
            },
            "fetch": {
                "command": "uvx",
                "args": ["mcp-server-fetch"]
            }
        }
    },
  'code_interpreter',  # Built-in tools
]

# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

Best Practices

To achieve optimal performance, we recommend the following settings:

  1. Sampling Parameters:

    • We suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0.
    • 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.
  2. Adequate Output Length: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.

  3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.

    • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
    • 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"."

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}
Downloads last month
124
Safetensors
Model size
235B params
Tensor type
BF16
·
F8_E4M3
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for unsloth/Qwen3-235B-A22B-Instruct-2507-FP8

Collection including unsloth/Qwen3-235B-A22B-Instruct-2507-FP8