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AngelSlim/
Qwen3-235B-A22B_nvfp4

Name: Qwen3-235B-A22B_nvfp4
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Dedicated to building a more intuitive, comprehensive, and efficient LLMs compression toolkit.

📖 Documentation | 🤗 Hugging Face | 🤖 ModelScope | 💬 WeChat

Latest Updates
Key Features
Supported Models
How to Use
Benchmark
License
Citation
Technical Discussion

📣Latest Updates

[25/09/24] We now support the PTQ quantification of NVFP4 for the Qwen3 series models. We also opensource Qwen3-32B-NVFP4 and Qwen3-235B-A22B-NVFP4 weights.
[25/09/01] We now support FP8 quantization of the Hunyuan-MT-7B translation model. And enabled Torch inference and Benchmark evaluation for Eagle3. And implemented support for quantization and Cache for FLUX. And support quantization for the Seed-OSS.
[25/08/06] We now support quantization for Hunyuan 0.5B/1.8B/4B/7B and multimodal model Qwen2.5VL 3B/7B/32B/72B, including FP8/INT4 algorithms, and quantization for DeepSeek-R1/V3 and Kimi-K2, including FP8-Static and W4A8-FP8 algorithms. We also opensource Hunyuan 1.8B/4B/7B series Eagle3 model weight.
[25/07/04] We now support quantization for Hunyuan/Qwen2.5/Qwen3/DeepSeek-R1-Distill-Qwen and other models, including INT8/FP8/INT4 algorithms. We also opensource Qwen3 series Eagle3 model weight.

Coming soon:

Diffusion model compression support.
Release of new algorithm for speculative sampling.

🌟Key Features

Highly Integrated: This toolkit integrates mainstream compression algorithms into a unified framework, offering developers one-click access with exceptional ease of use.
Continuous Innovation: Beyond integrating widely-used industry algorithms, we are continuously researching better compression algorithms, which will be gradually open-sourced in the future.
Performance-Driven: We continuously optimize end-to-end performance in model compression workflows and algorithm deployment, such as enabling quantization of models like Qwen3-235B and DeepSeek-R1 on a single GPU.

💼Supported Models

Quantization

Currently supports the following LLMs, including Hunyuan-Dense, Hunyuan-MoE, Qwen3-Dense, Qwen3-MoE, Qwen2.5, DeepSeek-R1 distilled Qwen models, and QwQ::

Model	FP8-Dynamic	FP8-Static	INT8-Dynamic	INT4-GPTQ	INT4-AWQ
Hunyuan-Dense	✅	✅	✅	✅	✅
Hunyuan-MoE	✅	✅	✅	✅	✅
Qwen3-Dense	✅	✅	✅	✅	✅
Qwen3-MoE	✅	✅	✅	✅	✅
Qwen2.5	✅	✅	✅	✅	✅
DeepSeek-R1-Distill-Qwen	✅	✅	✅	✅	✅
QwQ	✅	✅	✅	✅	✅

Speculative Decoding

Eagle3

The Eagle3 weights for the Qwen3 series model are now available.

Qwen3 Models	Hunyuan Models
✅ Qwen3-1.7B	✅ Hunyuan-1.8B-Instruct
✅ Qwen3-4B	✅ Hunyuan-4B-Instruct
✅ Qwen3-8B	✅ Hunyuan-7B-Instruct
✅ Qwen3-14B
✅ Qwen3-32B
✅ Qwen3-30B-A3B

🛎️How to Use

Install AngelSlim

We recommend using pip to install the latest stable version of AngelSlim:

pip install angelslim

Alternatively, you can clone the repository and install from source in editable mode:

cd AngelSlim && python setup.py install

For more detailed installation instructions, please refer to the Installation Documentation.

Quick Start

Quantization

After installing AngelSlim, you can quickly start by running the following script to perform static FP8 quantization on the Qwen3-1.7B model:

One-click Start

python3 tools/run.py -c configs/qwen3/fp8_static/qwen3-1_7b_fp8_static.yaml

This example will load the HuggingFace model and perform activation value calibration using the dataset specified in the config file, saving the quantized model weights.
Code-based Start

To perform dynamic FP8 quantization on Qwen3-1.7B:

python
1from angelslim.engine import Engine 2 3slim_engine = Engine() 4# Prepare model 5slim_engine.prepare_model(model_name="Qwen", model_path="Qwen/Qwen3-1.7B",) 6# Initialize compressor 7slim_engine.prepare_compressor("PTQ", default_method="fp8_dynamic") 8# Compress model 9slim_engine.run() 10# Save compressed model 11slim_engine.save("./output")

For more details, please refer to the Quick Start Documentation.

Speculative_Decoding

Eagle3 PyTorch Performance Testing

After installing AngelSlim, you can quickly start Eagle3 PyTorch performance testing with the following script:

bash

python3 tools/spec_benchmark.py \
    --base-model-path /path/to/base/model \
    --eagle-model-path /path/to/eagle/model \
    --model-id your_model_id \
    --mode both

For more details, please refer to the Quick Start Documentation.

Deployment and Testing

1. Offline Inference

If you need to load a quantized model via transformers, please set the deploy_backend: huggingface in the global configuration before quantizing the model, or manually modify the ignored_layers field in the config.json file located in the quantized model output directory to ignore.

To test offline inference with a quantized model loaded via transformers, run the following command:

python deploy/offline.py $MODEL_PATH

Where MODEL_PATH is the path to the quantized model output.

2. API Service Deployment

After specifying the quantized model path MODEL_PATH, you can deploy an OpenAI-compatible API service using the following LLMs inference frameworks:

vLLM

Use the following script to launch a vLLM server, recommended version vllm>=0.8.5.post1. For MOE INT8 quantized models, vllm>=0.9.0 is required.

bash deploy/run_vllm.sh $MODEL_PATH

SGLang

Use the following script to launch a SGLang server, recommended version sglang>=0.4.6.post1.

bash deploy/run_sglang.sh $MODEL_PATH

3. Service Invocation

Invoke requests via OpenAI's API format:

bash deploy/openai.sh $MODEL_PATH

4. Performance Evaluation

Evaluate the performance of quantized model using lm-evaluation-harness, recommended versionlm-eval>=0.4.8:

bash deploy/lm_eval.sh $MODEL_PATH

For more detaileds, please refer to the Deployment Documentation.

📈 Benchmark

(1) Quantization

The performance test results for selected models are shown below. For the complete benchmark, refer to the Benchmark documentation

Hunyuan Series Models

Benchmark results for the Hunyuan-Instruct model with FP8, INT4-AWQ and INT4-GPTQ quantization algorithms on datasets includingOlympiadBench, AIME 2024 and DROP:

Model	Quantization	OlympiadBench	AIME 2024	DROP	GPQA-Diamond
Hunyuan-A13B-Instruct	BF16	82.7	87.30	91.1	71.2
FP8-Static	83.0	86.7	91.1	-
Int4-GPTQ	82.7	86.7	91.1	-
Int4-AWQ	82.6	85.6	91.0	-
Hunyuan-7B-Instruct	BF16	76.5	81.1	85.9	60.1
FP8-Static	76.6	80.9	86.0	60.1
Int4-GPTQ	76.2	81.0	85.7	60.0
Int4-AWQ	76.4	80.9	85.9	60.1
Hunyuan-4B-Instruct	BF16	73.1	78.3	78.2	61.1
FP8-Static	73.1	76.6	78.3	60.2
Int4-GPTQ	72.9	-	78.1	58.1
Int4-AWQ	72.8	-	78.2	-
Hunyuan-1.8B-Instruct	BF16	63.4	56.7	76.7	47.2
FP8-Static	62.5	55.2	75.1	47.7
Int4-GPTQ	60.9	-	73.0	44.4
Int4-AWQ	61.7	-	71.7	43.6
Hunyuan-0.5B-Instruct	BF16	29.6	17.2	52.8	23.3
FP8-Static	29.6	17.2	51.6	22.5
Int4-GPTQ	26.8	-	50.9	23.3
Int4-AWQ	26.3	-	48.9	23.3

Qwen3 Series Models

Benchmark results for Qwen3 series models with FP8-Static, FP8-Dynamic, INT4-GPTQ, and INT4-AWQ quantization algorithms on datasets including CEVAL, MMLU, GSM8K, and HUMANEVAL:

Model	Quantization	CEVAL	MMLU	GSM8K	HUMANEVAL
Qwen3-0.6B	BF16	45.84	47.21	42.99	19.51
FP8-Static	45.99	46.87	38.06	18.90
FP8-Dynamic	45.99	46.93	38.29	20.73
INT8-Dynamic	45.17	46.95	41.17	21.34
Qwen3-8B	BF16	79.27	74.78	87.79	63.41
FP8-Static	78.23	74.79	86.96	62.20
FP8-Dynamic	78.45	74.75	87.64	62.80
INT8-Dynamic	78.01	74.84	86.96	67.07
INT4-GPTQ	77.19	73.26	86.43	62.20
INT4-AWQ	76.15	73.59	86.96	63.41
Qwen3-14B	BF16	83.06	78.90	88.40	55.49
FP8-Static	82.62	78.57	89.46	57.32
FP8-Dynamic	82.24	78.92	88.32	52.44
INT8-Dynamic	81.87	78.13	86.28	56.10
INT4-GPTQ	81.05	78.02	87.34	57.93
INT4-AWQ	82.02	77.68	84.23	61.59
Qwen3-32B	BF16	86.55	82.00	74.53	37.80
FP8-Static	86.92	81.78	70.20	39.63
FP8-Dynamic	86.55	81.89	70.43	38.41
INT4-GPTQ	86.18	81.01	-	43.29
INT4-AWQ	86.18	81.54	-	36.59
Qwen3-30B-A3B	BF16	83.66	79.36	89.99	31.71
FP8-Static	83.95	79.47	89.01	31.10
FP8-Dynamic	84.10	79.40	89.16	32.93
INT8-Dynamic	83.36	79.48	89.16	34.15
Qwen3-235B-A22B	BF16	89.60	86.28	85.29	27.44
FP8-Static	89.67	86.19	86.96	27.44
FP8-Dynamic	89.67	86.18	85.22	28.05
INT8-Dynamic	88.93	86.20	86.20	23.78
QwQ-32B	BF16	85.74	82.03	73.31	42.68
FP8-Static	85.44	81.91	75.36	42.68
FP8-Dynamic	85.07	81.93	75.66	42.07
INT4-GPTQ	84.03	81.26	68.23	45.73
INT4-AWQ	83.58	81.01	68.69	43.29

Qwen2.5VL Series Models

Benchmark results for Qwen2.5VL series models with BF16、FP8-Static、FP8-Dynamic、INT4-GPTQ、INT4-AWQ quantization algorithms on datasets including MMMU_VAL、DocVQA_VAL and ChartQA_TEST：

Model	Quantization	MMMU_VAL	MMLDocVQA_VALU	ChartQA_TEST
Qwen2.5VL-3B	BF16	47.11	78.57	80.32
FP8-Static	47.33	79.34	79.68
FP8-Dynamic	45.99	46.93	38.29
INT4-GPTQ	46.56	77.20	78.96
INT4-AWQ	45.78	-	79.60
Qwen2.5VL-7B	BF16	45.44	89.71	84.64
FP8-Static	47.00	89.83	85.92
FP8-Dynamic	47.22	89.80	88.64
INT4-GPTQ	46.67	90.45	-
INT4-AWQ	45.67	89.28	-
Qwen2.5VL-32B	BF16	57.00	90.03	-
FP8-Static	57.00	89.88	-
FP8-Dynamic	56.44	89.88	-
INT4-GPTQ	55.22	89.80	-
INT4-AWQ	55.22	90.30	-
Qwen2.5VL-72B	BF16	58.78	94.39	85.60
FP8-Static	57.89	94.41	85.84
FP8-Dynamic	58.67	94.38	85.60
INT4-GPTQ	57.56	94.46	86.48
INT4-AWQ	58.78	94.19	87.28

DeepSeek Series Models

Benchmark results for DeepSeek-R1-0528 series models with FP8-Block-Wise and W4A8-FP8 quantization algorithms on datasets including GPQA Diamond、AIME 2024、SimpleQA and LiveCodeBench：

Model	Quantization	GPQA Diamond	AIME 2024	SimpleQA	LiveCodeBench
DeepSeek-R1-0528	FP8-Block-Wise	78.28	88.67	27.8	77.1
W4A8-FP8	77.37	88.67	26.83	78.86

Note：

The above results are based on the average of 5 test runs deployed with TRT-LLM

The hyperparameters used during evaluation are as follows:

json
1{ 2 "top_k": 20, 3 "top_p": 0.6, 4 "temperature": 0.7, 5 "output_seq_len": 32768, 6 "max_input_seq_len": 16384 7}

Other Models

Benchmark results for other models with FP8-Static, FP8-Dynamic, INT4-GPTQ, and INT4-AWQ quantization algorithms on datasets including CEVAL, MMLU and GSM8K:

Model	Quantization	CEVAL	MMLU	GSM8K
Qwen2.5-1.5B-Instruct	BF16	67.01	60.05	54.28
FP8-Static	66.27	60.23	-
FP8-Dynamic	66.79	60.08	51.71
Qwen2.5-7B-Instruct	BF16	81.20	74.55	79.98
FP8-Static	81.13	74.03	79.30
FP8-Dynamic	80.31	74.07	79.00
INT4-GPTQ	79.05	73.05	74.75
INT4-AWQ	79.35	73.22	79.38
Qwen2.5-32B-Instruct	BF16	87.30	83.21	81.73
FP8-Static	87.59	83.08	81.58
FP8-Dynamic	87.30	83.04	81.58
INT4-GPTQ	86.70	82.45	82.03
INT4-AWQ	87.00	82.64	-
DeepSeek-R1-Distill-Qwen-7B	BF16	53.49	53.80	75.74
FP8-Static	53.57	54.17	76.19
FP8-Dynamic	52.97	54.13	74.15
INT4-GPTQ	51.86	52.44	75.89
INT4-AWQ	53.49	53.70	-
DeepSeek-R1-Distill-Qwen-14B	BF16	77.71	74.28	85.67
FP8-Static	77.56	74.66	86.73
FP8-Dynamic	76.82	74.63	87.11
INT4-GPTQ	74.29	72.37	84.61
INT4-AWQ	74.81	73.00	86.05
DeepSeek-R1-Distill-Qwen-32B	BF16	84.18	80.89	87.41
FP8-Static	83.43	80.90	87.57
FP8-Dynamic	83.73	81.10	86.43
INT4-GPTQ	84.10	79.80	86.73
INT4-AWQ	82.84	80.15	87.19

(2) Speculative Decoding

Qwen3 Series Models

Benchmark results for Qwen3 series models with Eagle3 speculative decoding algorithm on datasets including MT-bench, HunmanEval, GSM8K, and Alpaca:

		MT-bench	HumanEval	GSM8K	Alpaca	Mean
Temperature	Model	Speedup	τ	Speedup	τ	Speedup	τ	Speedup	τ	Speedup	τ
T=0	Qwen3-1.7B	2.05x	2.81	2.07x	2.93	2.11x	2.98	1.93x	2.69	2.04x	2.85
Qwen3-4B	2.21x	3.01	2.36x	3.24	2.42x	3.13	2.32x	2.75	2.33x	3.03
Qwen3-8B	2.63x	3.65	2.76x	3.85	2.82x	3.90	2.62x	3.48	2.70x	3.72
Qwen3-14B	2.23x	3.30	2.53x	3.74	2.56x	3.79	2.16x	3.13	2.37x	3.49
Qwen3-32B	2.39x	2.78	2.37x	2.81	2.47x	2.92	2.42x	2.53	2.41x	2.76
Qwen3-30B-A3B	2.84x	3.63	2.27x	3.09	2.64x	3.42	2.83x	3.56	2.64x	3.42
T=1	Qwen3-1.7B	1.74x	2.53	1.86x	2.70	1.82x	2.69	1.72x	2.46	1.93x	2.60
Qwen3-4B	1.93x	2.60	2.00x	2.84	2.11x	2.82	2.34x	2.50	1.75x	2.69
Qwen3-8B	1.98x	2.75	2.25x	3.11	2.31x	3.15	2.10x	2.76	2.90x	2.94
Qwen3-14B	1.71x	2.61	1.95x	2.87	2.04x	3.08	1.68x	2.55	2.90x	2.78
Qwen3-32B	1.62x	1.91	1.71x	2.05	1.78x	2.10	1.80x	1.95	1.62x	2.00
Qwen3-30B-A3B	1.91x	2.46	2.00x	2.64	1.90x	2.53	1.80x	2.32	1.90x	2.48

Hunyuan Series Models

Benchmark results for Hunyuan series models with Eagle3 speculative decoding algorithm on datasets including MT-bench, HunmanEval, GSM8K, and Alpaca:

		MT-bench	HumanEval	GSM8K	Alpaca	Mean
Temperature	Model	Speedup	τ	Speedup	τ	Speedup	τ	Speedup	τ	Speedup	τ
T=0	Hunyuan-1.8B-Instruct	1.97x	2.90	2.58x	3.73	2.61x	3.71	1.71x	2.43	2.22x	3.19
Hunyuan-4B-Instruct	1.77x	2.60	2.64x	3.35	2.14x	3.17	1.72x	2.57	2.07x	2.92
Hunyuan-7B-Instruct	2.22x	3.58	3.59x	5.47	2.96x	4.68	1.64x	2.56	2.60x	4.07
T=1	Hunyuan-1.8B-Instruct	1.58x	2.36	2.35x	3.56	2.23x	3.38	1.26x	1.87	1.86x	2.79
Hunyuan-4B-Instruct	1.36x	2.05	1.97x	2.86	1.72x	2.68	1.14x	1.76	1.55x	2.34
Hunyuan-7B-Instruct	1.90x	3.11	3.12x	5.09	2.74x	4.34	1.47x	2.39	2.31x	3.73

📝 License

The code for this project is open-sourced under the License for AngelSlim.

🔗 Citation

@software{AngelSlim2025,
    title={{AngelSlim}},
    author={Tencent AngelSlim Project Contributors},
    year={2025},
    month={6},
    url={https://github.com/Tencent/AngelSlim},
}

💬 Technical Discussion

AngelSlim is continuously iterating and new features will be released soon. If you have any questions or suggestions, please open an issue on GitHub Issues or join our WeChat technical discussion group.

AngelSlim/Qwen3-235B-A22B_nvfp4

Dedicated to building a more intuitive, comprehensive, and efficient LLMs compression toolkit.

Table of Contents

📣Latest Updates

🌟Key Features

💼Supported Models

Quantization

Speculative Decoding

Eagle3

🛎️How to Use

Install AngelSlim

Quick Start

Quantization

Speculative_Decoding

Eagle3 PyTorch Performance Testing

Deployment and Testing

1. Offline Inference

2. API Service Deployment

3. Service Invocation

4. Performance Evaluation

📈 Benchmark

(1) Quantization

Hunyuan Series Models

Qwen3 Series Models

Qwen2.5VL Series Models

DeepSeek Series Models

Other Models

(2) Speculative Decoding

Qwen3 Series Models

Hunyuan Series Models

📝 License

🔗 Citation

💬 Technical Discussion

AngelSlim/
Qwen3-235B-A22B_nvfp4