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

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Table of Contents

• Latest Updates
• Key Features
• Supported Models
• How to Use
  • Install AngelSlim
  • Quick Start
  • deployment & Evaluation
• 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::

Speculative Decoding

Eagle3

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

🛎️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:

  from angelslim.engine import Engine
  
  slim_engine = Engine()
  # Prepare model
  slim_engine.prepare_model(model_name="Qwen", model_path="Qwen/Qwen3-1.7B",)
  # Initialize compressor
  slim_engine.prepare_compressor("PTQ", default_method="fp8_dynamic")
  # Compress model
  slim_engine.run()
  # Save compressed model
  slim_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:

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:

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:

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：

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：

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:

{
 "top_k": 20,
 "top_p": 0.6,
 "temperature": 0.7,
 "output_seq_len": 32768,
 "max_input_seq_len": 16384
}

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:

(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:

Hunyuan Series Models

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

📝 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.