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--- |
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tags: |
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- qwen3 |
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- eagle3 |
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- eagle |
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--- |
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<p align="center"> |
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<picture> |
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<source media="(prefers-color-scheme: dark)" srcset="https://github.com/Tencent/AngelSlim/blob/main/docs/source/assets/logos/angelslim_logo_light.png?raw=true"> |
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<img alt="AngelSlim" src="https://github.com/Tencent/AngelSlim/blob/main/docs/source/assets/logos/angelslim_logo.png?raw=true" width=55%> |
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</picture> |
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</p> |
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<h3 align="center"> |
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Dedicated to building a more intuitive, comprehensive, and efficient LLMs compression toolkit. |
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</h3> |
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<p align="center"> |
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π <a href="https://angelslim.readthedocs.io/">Documentation</a>   |   π€ <a href="https://huggingface.co/AngelSlim">Hugging Face</a>   |   π€ <a href="https://modelscope.cn/organization/AngelSlim">ModelScope</a>   |   π¬ <a href="./docs/source/assets/angel_slim_wechat.png">WeChat</a> |
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<br> |
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</p> |
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## Table of Contents |
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- [Latest Updates](#latest-updates) |
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- [Key Features](#key-features) |
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- [Supported Models](#supported-models) |
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- [How to Use](#how-to-use) |
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- [Install AngelSlim](#install-angelslim) |
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- [Quick Start](#quick-start) |
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- [deployment & Evaluation](#deployment) |
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- [Benchmark](#benchmark) |
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- [License](#license) |
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- [Citation](#citation) |
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- [Technical Discussion](#technical-discussion) |
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## π£Latest Updates |
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- [25/07/04] We now support quantization for Hunyuan/Qwen2.5/Qwen3/DeepSeek-R1-Distill-Qwen and other models, including INT8/FP8/INT4 algorithms. |
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We also opensource Qwen3-8B`s Eagle3 model weight. |
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Coming soon: |
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- [ ] Support W4A8 quantization for DeepSeek-R1. |
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- [ ] Support quantization for multimodal models like Qwen-VL. |
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- [ ] Release of new algorithm for speculative sampling. |
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## πKey Features |
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- **Highly Integrated**: This toolkit integrates mainstream compression algorithms into a unified framework, offering developers one-click access with exceptional ease of use. |
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- **Continuous Innovation**: Beyond integrating widely-used industry algorithms, we are continuously researching better compression algorithms, which will be gradually open-sourced in the future. |
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- **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. |
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## πΌSupported Models |
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### Quantization |
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Currently supports the following LLMs, including Hunyuan-Dense, Hunyuan-MoE, Qwen3-Dense, Qwen3-MoE, Qwen2.5, DeepSeek-R1 distilled Qwen models, and QwQ:: |
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| Model | FP8-Dynamic | FP8-Static | INT8-Dynamic | INT4-GPTQ | INT4-AWQ | |
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| --------------------------------------------------------------------------------------------------------------------------- | ----------- | ---------- | ------------ | --------- | -------- | |
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| [Hunyuan-Dense](https://huggingface.co/tencent/Hunyuan-7B-Instruct) | β
| β
| β
| β
| β
| |
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| [Hunyuan-MoE](https://huggingface.co/collections/tencent/hunyuan-a13b-685ec38e5b46321e3ea7c4be) | β
| β
| β
| β
| β
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| [Qwen3-Dense](https://huggingface.co/collections/AngelSlim/qwen3-quant-68652e26da31740739d154f8) | β
| β
| β
| β
| β
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| [Qwen3-MoE](https://huggingface.co/collections/AngelSlim/qwen3-quant-68652e26da31740739d154f8) | β
| β
| β
| β
| β
| |
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| [Qwen2.5](https://huggingface.co/collections/AngelSlim/qwen2-25-quant-68652d6cbdf5c0d4b1c4499a) | β
| β
| β
| β
| β
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| [DeepSeek-R1-Distill-Qwen](https://huggingface.co/collections/AngelSlim/deepseek-r1-distill-quant-68652f16a9c206b030b05f7f) | β
| β
| β
| β
| β
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| [QwQ](https://huggingface.co/collections/AngelSlim/qwen3-quant-68652e26da31740739d154f8) | β
| β
| β
| β
| β
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### Speculative Decoding |
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The Eagle3 weights for the Qwen3 series model are now available. |
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| Qwen3 Models | Hunyuan Models | |
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| ----------|----------| |
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| β
[Qwen3-1.7B](https://huggingface.co/AngelSlim/Qwen3-1.7B_eagle3) |β
[Hunyuan-1.8B-Instruct](https://huggingface.co/AngelSlim/Hunyuan-1.8B-Instruct_eagle3) | |
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| β
[Qwen3-4B](https://huggingface.co/AngelSlim/Qwen3-4B_eagle3) |β
[Hunyuan-4B-Instruct](https://huggingface.co/AngelSlim/Hunyuan-4B-Instruct_eagle3) | |
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| β
[Qwen3-8B](https://huggingface.co/AngelSlim/Qwen3-8B_eagle3) |β
[Hunyuan-7B-Instruct](https://huggingface.co/AngelSlim/Hunyuan-7B-Instruct_eagle3) | |
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| β
[Qwen3-14B](https://huggingface.co/AngelSlim/Qwen3-14B_eagle3) | |
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| β
[Qwen3-32B](https://huggingface.co/AngelSlim/Qwen3-32B_eagle3) | |
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[Qwen3-30B-A3B](https://huggingface.co/AngelSlim/Qwen3-a3B_eagle3) | |
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## ποΈHow to Use |
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### Install AngelSlim |
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We recommend using `pip` to install the latest stable version of `AngelSlim`: |
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```shell |
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pip install angelslim |
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``` |
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Alternatively, you can clone the repository and install from source in editable mode: |
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```shell |
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cd AngelSlim && python setup.py install |
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``` |
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For more detailed installation instructions, please refer to the [Installation Documentation](https://angelslim.readthedocs.io/zh-cn/latest/getting_started/installation.html). |
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### Quick Start |
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After installing `AngelSlim`, you can quickly start by running the following script to perform static `FP8` quantization on the `Qwen3-1.7B` model: |
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* One-click Start |
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```shell |
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python3 tools/run.py -c configs/qwen3/fp8_static/qwen3-1_7b_fp8_static.yaml |
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``` |
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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. |
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* Code-based Start |
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To perform dynamic `FP8` quantization on `Qwen3-1.7B`: |
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```python |
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from angelslim.engine import Engine |
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slim_engine = Engine() |
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# Prepare model |
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slim_engine.prepare_model(model_name="Qwen", model_path="Qwen/Qwen3-1.7B",) |
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# Initialize compressor |
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slim_engine.prepare_compressor("PTQ", default_method="fp8_dynamic") |
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# Compress model |
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slim_engine.run() |
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# Save compressed model |
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slim_engine.save("./output") |
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``` |
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For more details, please refer to the [Quick Start Documentation](https://angelslim.readthedocs.io/zh-cn/latest/getting_started/quickstrat.html). |
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### π₯οΈ Deployment and Testing |
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#### 1. API Service Deployment |
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After specifying the quantized model path `MODEL_PATH`, you can deploy an OpenAI-compatible API service using the following LLMs inference frameworks: |
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**vLLM** |
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Use the following script to launch a [vLLM](https://github.com/vllm-project/vllm) server, recommended version `vllm>=0.8.5.post1`. For MOE INT8 quantized models, vllm>=0.9.0 is required. |
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```shell |
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bash deploy/run_vllm.sh $MODEL_PATH |
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``` |
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**SGLang** |
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Use the following script to launch a [SGLang](https://github.com/sgl-project/sglang) server, recommended version `sglang>=0.4.6.post1`. |
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```shell |
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bash deploy/run_sglang.sh $MODEL_PATH |
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``` |
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#### 2. Service Invocation |
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Invoke requests via [OpenAI's API format](https://platform.openai.com/docs/api-reference/introduction): |
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```shell |
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bash deploy/openai.sh $MODEL_PATH |
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``` |
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#### 3. Performance Evaluation |
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Evaluate the performance of quantized model using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness), recommended version`lm-eval>=0.4.8`: |
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```shell |
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bash deploy/lm_eval.sh $MODEL_PATH |
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``` |
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For more detaileds, please refer to the [Deployment Documentation](https://angelslim.readthedocs.io/zh-cn/latest/deployment/deploy.html). |
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## π Benchmark |
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### (1) Quantization |
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The performance test results for selected models are shown below. For the complete benchmark, refer to the [Benchmark documentation](https://angelslim.readthedocs.io/zh-cn/latest/performance/quantization/benchmarks.html) |
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#### Hunyuan Series Models |
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Benchmark results for the `Hunyuan-A13B-Instruct` model with `FP8` and `INT4-GPTQ` quantization algorithms on datasets including `AIME 2024`, `GSM8K`, `BBH`, and `DROP`: |
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| Bench | Hunyuan-A13B-Instruct | Hunyuan-A13B-Instruct-FP8 | Hunyuan-A13B-Instruct-Int4-GPTQ | |
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|:---------:|:---------------------:|:-------------------------:|:-------------------------------:| |
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| AIME 2024 | 87.3 | 86.7 | 86.7 | |
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| GSM8K | 94.39 | 94.01 | 94.24 | |
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| BBH | 89.1 | 88.34 | 87.91 | |
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| DROP | 91.1 | 91.1 | 91.05 | |
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#### Qwen3 Series Models |
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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`: |
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<table> |
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<thead> |
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<tr><th>Model</th><th>Quantization</th><th>CEVAL</th><th>MMLU</th><th>GSM8K</th><th>HUMANEVAL</th></tr> |
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</thead> |
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<tbody> |
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<tr><td rowspan="4">Qwen3-0.6B</td><td>BF16</td><td>45.84</td><td>47.21</td><td>42.99</td><td>19.51</td></tr> |
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<tr><td>FP8-Static</td><td>45.99</td><td>46.87</td><td>38.06</td><td>18.90</td></tr> |
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<tr><td>FP8-Dynamic</td><td>45.99</td><td>46.93</td><td>38.29</td><td>20.73</td></tr> |
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<tr><td>INT8-Dynamic</td><td>45.17</td><td>46.95</td><td>41.17</td><td>21.34</td></tr> |
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<tr><td rowspan="6">Qwen3-8B</td><td>BF16</td><td>79.27</td><td>74.78</td><td>87.79</td><td>63.41</td></tr> |
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<tr><td>FP8-Static</td><td>78.23</td><td>74.79</td><td>86.96</td><td>62.20</td></tr> |
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<tr><td>FP8-Dynamic</td><td>78.45</td><td>74.75</td><td>87.64</td><td>62.80</td></tr> |
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<tr><td>INT8-Dynamic</td><td>78.01</td><td>74.84</td><td>86.96</td><td>67.07</td></tr> |
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<tr><td>INT4-GPTQ</td><td>77.19</td><td>73.26</td><td>86.43</td><td>62.20</td></tr> |
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<tr><td>INT4-AWQ</td><td>76.15</td><td>73.59</td><td>86.96</td><td>63.41</td></tr> |
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<tr><td rowspan="6">Qwen3-14B</td><td>BF16</td><td>83.06</td><td>78.90</td><td>88.40</td><td>55.49</td></tr> |
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<tr><td>FP8-Static</td><td>82.62</td><td>78.57</td><td>89.46</td><td>57.32</td></tr> |
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<tr><td>FP8-Dynamic</td><td>82.24</td><td>78.92</td><td>88.32</td><td>52.44</td></tr> |
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<tr><td>INT8-Dynamic</td><td>81.87</td><td>78.13</td><td>86.28</td><td>56.10</td></tr> |
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<tr><td>INT4-GPTQ</td><td>81.05</td><td>78.02</td><td>87.34</td><td>57.93</td></tr> |
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<tr><td>INT4-AWQ</td><td>82.02</td><td>77.68</td><td>84.23</td><td>61.59</td></tr> |
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<tr><td rowspan="5">Qwen3-32B</td><td>BF16</td><td>86.55</td><td>82.00</td><td>74.53</td><td>37.80</td></tr> |
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<tr><td>FP8-Static</td><td>86.92</td><td>81.78</td><td>70.20</td><td>39.63</td></tr> |
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<tr><td>FP8-Dynamic</td><td>86.55</td><td>81.89</td><td>70.43</td><td>38.41</td></tr> |
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<tr><td>INT4-GPTQ</td><td>86.18</td><td>81.01</td><td>-</td><td>43.29</td></tr> |
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<tr><td>INT4-AWQ</td><td>86.18</td><td>81.54</td><td>-</td><td>36.59</td></tr> |
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<tr><td rowspan="4">Qwen3-30B-A3B</td><td>BF16</td><td>83.66</td><td>79.36</td><td>89.99</td><td>31.71</td></tr> |
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<tr><td>FP8-Static</td><td>83.95</td><td>79.47</td><td>89.01</td><td>31.10</td></tr> |
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<tr><td>FP8-Dynamic</td><td>84.10</td><td>79.40</td><td>89.16</td><td>32.93</td></tr> |
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<tr><td>INT8-Dynamic</td><td>83.36</td><td>79.48</td><td>89.16</td><td>34.15</td></tr> |
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<tr><td rowspan="4">Qwen3-235B-A22B</td><td>BF16</td><td>89.60</td><td>86.28</td><td>85.29</td><td>27.44</td></tr> |
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<tr><td>FP8-Static</td><td>89.67</td><td>86.19</td><td>86.96</td><td>27.44</td></tr> |
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<tr><td>FP8-Dynamic</td><td>89.67</td><td>86.18</td><td>85.22</td><td>28.05</td></tr> |
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<tr><td>INT8-Dynamic</td><td>88.93</td><td>86.20</td><td>86.20</td><td>23.78</td></tr> |
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<tr><td rowspan="5">QwQ-32B</td><td>BF16</td><td>85.74</td><td>82.03</td><td>73.31</td><td>42.68</td></tr> |
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<tr><td>FP8-Static</td><td>85.44</td><td>81.91</td><td>75.36</td><td>42.68</td></tr> |
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<tr><td>FP8-Dynamic</td><td>85.07</td><td>81.93</td><td>75.66</td><td>42.07</td></tr> |
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<tr><td>INT4-GPTQ</td><td>84.03</td><td>81.26</td><td>68.23</td><td>45.73</td></tr> |
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<tr><td>INT4-AWQ</td><td>83.58</td><td>81.01</td><td>68.69</td><td>43.29</td></tr> |
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</tbody> |
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</table> |
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#### Other Models |
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Benchmark results for other models with `FP8-Static`, `FP8-Dynamic`, `INT4-GPTQ`, and `INT4-AWQ` quantization algorithms on datasets including `CEVAL`, `MMLU` and `GSM8K`: |
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<table> |
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<thead> |
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<tr><th>Model</th><th>Quantization</th><th>CEVAL</th><th>MMLU</th><th>GSM8K</th></tr> |
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</thead> |
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<tbody> |
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<tr><td rowspan="3">Qwen2.5-1.5B-Instruct</td><td>BF16</td><td>67.01</td><td>60.05</td><td>54.28</td></tr> |
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<tr><td>FP8-Static</td><td>66.27</td><td>60.23</td><td>-</td></tr> |
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<tr><td>FP8-Dynamic</td><td>66.79</td><td>60.08</td><td>51.71</td></tr> |
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<tr><td rowspan="5">Qwen2.5-7B-Instruct</td><td>BF16</td><td>81.20</td><td>74.55</td><td>79.98</td></tr> |
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<tr><td>FP8-Static</td><td>81.13</td><td>74.03</td><td>79.30</td></tr> |
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<tr><td>FP8-Dynamic</td><td>80.31</td><td>74.07</td><td>79.00</td></tr> |
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<tr><td>INT4-GPTQ</td><td>79.05</td><td>73.05</td><td>74.75</td></tr> |
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<tr><td>INT4-AWQ</td><td>79.35</td><td>73.22</td><td>79.38</td></tr> |
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<tr><td rowspan="5">Qwen2.5-32B-Instruct</td><td>BF16</td><td>87.30</td><td>83.21</td><td>81.73</td></tr> |
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<tr><td>FP8-Static</td><td>87.59</td><td>83.08</td><td>81.58</td></tr> |
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<tr><td>FP8-Dynamic</td><td>87.30</td><td>83.04</td><td>81.58</td></tr> |
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<tr><td>INT4-GPTQ</td><td>86.70</td><td>82.45</td><td>82.03</td></tr> |
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<tr><td>INT4-AWQ</td><td>87.00</td><td>82.64</td><td>-</td></tr> |
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<tr><td rowspan="5">DeepSeek-R1-Distill-Qwen-7B</td><td>BF16</td><td>53.49</td><td>53.80</td><td>75.74</td></tr> |
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<tr><td>FP8-Static</td><td>53.57</td><td>54.17</td><td>76.19</td></tr> |
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<tr><td>FP8-Dynamic</td><td>52.97</td><td>54.13</td><td>74.15</td></tr> |
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<tr><td>INT4-GPTQ</td><td>51.86</td><td>52.44</td><td>75.89</td></tr> |
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<tr><td>INT4-AWQ</td><td>53.49</td><td>53.70</td><td>-</td></tr> |
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<tr><td rowspan="5">DeepSeek-R1-Distill-Qwen-14B</td><td>BF16</td><td>77.71</td><td>74.28</td><td>85.67</td></tr> |
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<tr><td>FP8-Static</td><td>77.56</td><td>74.66</td><td>86.73</td></tr> |
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<tr><td>FP8-Dynamic</td><td>76.82</td><td>74.63</td><td>87.11</td></tr> |
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<tr><td>INT4-GPTQ</td><td>74.29</td><td>72.37</td><td>84.61</td></tr> |
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<tr><td>INT4-AWQ</td><td>74.81</td><td>73.00</td><td>86.05</td></tr> |
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<tr><td rowspan="5">DeepSeek-R1-Distill-Qwen-32B</td><td>BF16</td><td>84.18</td><td>80.89</td><td>87.41</td></tr> |
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<tr><td>FP8-Static</td><td>83.43</td><td>80.90</td><td>87.57</td></tr> |
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<tr><td>FP8-Dynamic</td><td>83.73</td><td>81.10</td><td>86.43</td></tr> |
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<tr><td>INT4-GPTQ</td><td>84.10</td><td>79.80</td><td>86.73</td></tr> |
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<tr><td>INT4-AWQ</td><td>82.84</td><td>80.15</td><td>87.19</td></tr> |
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</tbody> |
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</table> |
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### (2) Speculative Decoding |
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#### Qwen3 Series Models |
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Benchmark results for Qwen3 series models with `Eagle3` speculative decoding algorithm on datasets including `MT-bench`, `HunmanEval`, `GSM8K`, and `Alpaca`: |
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|
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<table> |
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<thead> |
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<tr> |
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<th> </th><th> </th> |
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<th colspan="2" style="text-align: center; vertical-align: middle;">MT-bench</th> |
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<th colspan="2" style="text-align: center; vertical-align: middle;">HumanEval</th> |
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<th colspan="2" style="text-align: center; vertical-align: middle;">GSM8K</th> |
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<th colspan="2" style="text-align: center; vertical-align: middle;">Alpaca</th> |
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<th colspan="2" style="text-align: center; vertical-align: middle;">Mean</th></tr> |
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<tr><th>Temperature</th><th>Model</th><th>Speedup</th><th>Ο</th><th>Speedup</th><th>Ο</th><th>Speedup</th><th>Ο</th><th>Speedup</th><th>Ο</th><th>Speedup</th><th>Ο</th></tr> |
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</thead> |
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<tbody> |
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<!-- <tr><td colspan="12" style="text-align: center; vertical-align: middle;"><strong>Temperature=0</strong></td></tr> --> |
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<tr><td rowspan="6"><strong>T=0</strong></td> |
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<td>Qwen3-1.7B</td><td>2.05x</td><td>2.81</td><td>2.07x</td><td>2.93</td><td>2.11x</td><td>2.98</td><td>1.93x</td><td>2.69</td><td>2.04x</td><td>2.85</td></tr> |
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<tr> <td>Qwen3-4B</td><td>2.21x</td><td>3.01</td><td>2.36x</td><td>3.24</td><td>2.42x</td><td>3.13</td><td>2.32x</td><td>2.75</td><td>2.33x</td><td>3.03</td></tr> |
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<tr><td>Qwen3-8B</td><td>2.65x</td><td>3.87</td><td>2.64x</td><td>3.82</td><td>2.86x</td><td>4.10</td><td>2.58x</td><td>3.55</td><td>2.68x</td><td>3.83</td></tr> |
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<tr><td>Qwen3-14B</td><td>2.42x</td><td>3.38</td><td>2.57x</td><td>3.58</td><td>2.75x</td><td>3.77</td><td>2.27x</td><td>3.11</td><td>2.50x</td><td>3.46</td></tr> |
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<tr><td>Qwen3-32B</td><td>2.39x</td><td>2.78</td><td>2.37x</td><td>2.81</td><td>2.47x</td><td>2.92</td><td>2.42x</td><td>2.53</td><td>2.41x</td><td>2.76</td></tr> |
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<tr><td>Qwen3-30B-A3B</td><td>2.84x</td><td>3.63</td><td>2.27x</td><td>3.09</td><td>2.64x</td><td>3.42</td><td>2.83x</td><td>3.56</td><td>2.64x</td><td>3.42</td></tr> |
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<!-- <tr><td colspan="12" style="text-align: center; vertical-align: middle;"><strong>Temperature=1</strong></td></tr> --> |
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<tr><td rowspan="6"><strong>T=1</strong></td> |
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<td>Qwen3-1.7B</td><td>1.74x</td><td>2.53</td><td>1.86x</td><td>2.70</td><td>1.82x</td><td>2.69</td><td>1.72x</td><td>2.46</td><td>1.93x</td><td>2.60</td></tr> |
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<tr><td>Qwen3-4B</td><td>1.93x</td><td>2.60</td><td>2.00x</td><td>2.84</td><td>2.11x</td><td>2.82</td><td>2.34x</td><td>2.50</td><td>1.75x</td><td>2.69</td></tr> |
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<tr><td>Qwen3-8B</td><td>1.91x</td><td>2.84</td><td>2.07x</td><td>3.05</td><td>2.34x</td><td>3.26</td><td>2.09x</td><td>2.92</td><td>2.10x</td><td>3.02</td></tr> |
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<tr><td>Qwen3-14B</td><td>1.81x</td><td>2.58</td><td>1.96x</td><td>2.81</td><td>2.16x</td><td>3.09</td><td>1.76x</td><td>2.49</td><td>1.92x</td><td>2.74</td></tr> |
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<tr><td>Qwen3-32B</td><td>1.62x</td><td>1.91</td><td>1.71x</td><td>2.05</td><td>1.78x</td><td>2.10</td><td>1.80x</td><td>1.95</td><td>1.62x</td><td>2.00</td></tr> |
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<tr><td>Qwen3-30B-A3B</td><td>1.91x</td><td>2.46</td><td>2.00x</td><td>2.64</td><td>1.90x</td><td>2.53</td><td>1.80x</td><td>2.32</td><td>1.90x</td><td>2.48</td></tr> |
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</tbody> |
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</table> |
|
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|
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|
#### Hunyuan Series Models |
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Benchmark results for Hunyuan series models with `Eagle3` speculative decoding algorithm on datasets including `MT-bench`, `HunmanEval`, `GSM8K`, and `Alpaca`: |
|
|
|
|
|
<table> |
|
|
<thead> |
|
|
<tr> |
|
|
<th> </th><th> </th> |
|
|
<th colspan="2" style="text-align: center; vertical-align: middle;">MT-bench</th> |
|
|
<th colspan="2" style="text-align: center; vertical-align: middle;">HumanEval</th> |
|
|
<th colspan="2" style="text-align: center; vertical-align: middle;">GSM8K</th> |
|
|
<th colspan="2" style="text-align: center; vertical-align: middle;">Alpaca</th> |
|
|
<th colspan="2" style="text-align: center; vertical-align: middle;">Mean</th></tr> |
|
|
<tr><th>Temperature</th><th>Model</th><th>Speedup</th><th>Ο</th><th>Speedup</th><th>Ο</th><th>Speedup</th><th>Ο</th><th>Speedup</th><th>Ο</th><th>Speedup</th><th>Ο</th></tr> |
|
|
</thead> |
|
|
<tbody> |
|
|
<!-- <tr><td colspan="12" style="text-align: center; vertical-align: middle;"><strong>Temperature=0</strong></td></tr> --> |
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|
<tr><td rowspan="3"><strong>T=0</strong></td> |
|
|
<td>Hunyuan-1.8B-Instruct</td><td>1.97x</td><td>2.90</td><td>2.58x</td><td>3.73</td><td>2.61x</td><td>3.71</td><td>1.71x</td><td>2.43</td><td>2.22x</td><td>3.19</td></tr> |
|
|
<tr> <td>Hunyuan-4B-Instruct</td><td>1.77x</td><td>2.60</td><td>2.64x</td><td>3.35</td><td>2.14x</td><td>3.17</td><td>1.72x</td><td>2.57</td><td>2.07x</td><td>2.92</td></tr> |
|
|
<tr><td>Hunyuan-7B-Instruct</td><td>2.22x</td><td>3.58</td><td>3.59x</td><td>5.47</td><td>2.96x</td><td>4.68</td><td>1.64x</td><td>2.56</td><td>2.60x</td><td>4.07</td></tr> |
|
|
<!-- <tr><td colspan="12" style="text-align: center; vertical-align: middle;"><strong>Temperature=1</strong></td></tr> --> |
|
|
<tr><td rowspan="3"><strong>T=1</strong></td> |
|
|
<td>Hunyuan-1.8B-Instruct</td><td>1.58x</td><td>2.36</td><td>2.35x</td><td>3.56</td><td>2.23x</td><td>3.38</td><td>1.26x</td><td>1.87</td><td>1.86x</td><td>2.79</td></tr> |
|
|
<tr><td>Hunyuan-4B-Instruct</td><td>1.36x</td><td>2.05</td><td>1.97x</td><td>2.86</td><td>1.72x</td><td>2.68</td><td>1.14x</td><td>1.76</td><td>1.55x</td><td>2.34</td></tr> |
|
|
<tr><td>Hunyuan-7B-Instruct</td><td>1.90x</td><td>3.11</td><td>3.12x</td><td>5.09</td><td>2.74x</td><td>4.34</td><td>1.47x</td><td>2.39</td><td>2.31x</td><td>3.73</td></tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
## π License |
|
|
|
|
|
The code for this project is open-sourced under the [License for AngelSlim](LICENSE). |
|
|
|
|
|
## π Citation |
|
|
|
|
|
``` |
|
|
@software{AngelSlim2025, |
|
|
title={{AngelSlim}}, |
|
|
author={Tencent AngelSlim Project Contributors}, |
|
|
year={2025}, |
|
|
month={6}, |
|
|
url={https://github.com/Tencent/AngelSlim}, |
|
|
} |
|
|
``` |
|
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|
|
|
## π¬ 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 or join our [WeChat technical discussion group](https://github.com/Tencent/AngelSlim/blob/main/docs/source/assets/angel_slim_wechat.png?raw=true). |
