README for model-qwen3-32b-fp4
| .gitattributes | ||
| added_tokens.json | ||
| config.json | ||
| configuration.json | ||
| generation_config.json | ||
| merges.txt | ||
| model-00001-of-00005.safetensors | ||
| model-00002-of-00005.safetensors | ||
| model-00003-of-00005.safetensors | ||
| model-00004-of-00005.safetensors | ||
| model-00005-of-00005.safetensors | ||
| model.safetensors.index.json | ||
| quant_config.yaml | ||
| README.md | ||
| special_tokens_map.json | ||
| tokenizer_config.json | ||
| vocab.json | ||
| license | base_model | base_model_relation | library_name | tags | |||
|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
quantized | transformers |
|
Evaluation
The test results in the following table are based on the MMLU benchmark.
In order to speed up the test, we prevent the model from generating too long thought chains, so the score may be different from that with longer thought chain.
In our experiment, the accuracy of the FP4 quantized version is almost the same as the BF16 version, and it can be used for faster inference.
| Data Format | MMLU Score |
|---|---|
| BF16 Official | 88.21 |
| FP4 Quantized | 87.43 |
Quickstart
We recommend using the Chitu inference framework(https://github.com/thu-pacman/chitu) to run this model. Here provides a simple command to show you how to run Qwen3-32B-fp4.
torchrun --nproc_per_node 1 \
--master_port=22525 \
-m chitu \
serve.port=21002 \
infer.cache_type=paged \
infer.pp_size=1 \
infer.tp_size=1 \
models=Qwen3-32B-fp4 \
models.ckpt_dir="your model path" \
models.tokenizer_path="your model path" \
dtype=float16 \
infer.do_load=True \
infer.max_reqs=1 \
scheduler.prefill_first.num_tasks=100 \
infer.max_seq_len=4096 \
request.max_new_tokens=100 \
infer.use_cuda_graph=True