File size: 25,729 Bytes
8e9ec0e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
English | [简体中文](README.md)

<p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/logos/angelslim_logo_light.png">
    <img alt="AngelSlim" src="./docs/source/assets/logos/angelslim_logo.png" width=55%>
  </picture>
</p>

<h3 align="center">
Dedicated to building a more intuitive, comprehensive, and efficient LLMs compression toolkit.
</h3>

<p align="center">
          📖 <a href="https://angelslim.readthedocs.io/">Documentation</a>&nbsp&nbsp | &nbsp&nbsp🤗 <a href="https://huggingface.co/AngelSlim">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/AngelSlim">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp💬 <a href="./docs/source/assets/angel_slim_wechat.png">WeChat</a> | &nbsp&nbsp🫨 <a href="https://discord.com/invite/dHVNeuNdFt">Discord</a>
<br>
</p>

## Table of Contents

- [Latest Updates](#latest-updates)
- [Key Features](#key-features)
- [Supported Models](#supported-models)
- [How to Use](#how-to-use)
  - [Install AngelSlim](#install-angelslim)
  - [Quick Start](#quick-start)
  - [deployment & Evaluation](#deployment)
- [Benchmark](#benchmark)
- [License](#license)
- [Citation](#citation)
- [Technical Discussion](#technical-discussion)

## 📣Latest Updates

- [25/08/04] 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. 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:

- [ ] Support W4A8 quantization for DeepSeek-R1.
- [ ] 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](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)                                                         | ✅           | ✅          | ✅            | ✅         | ✅        |
| [Hunyuan-MoE](https://huggingface.co/collections/tencent/hunyuan-a13b-685ec38e5b46321e3ea7c4be)                             | ✅           | ✅          | ✅            | ✅         | ✅        |
| [Qwen3-Dense](https://huggingface.co/collections/AngelSlim/qwen3-quant-68652e26da31740739d154f8)                            | ✅           | ✅          | ✅            | ✅         | ✅        |
| [Qwen3-MoE](https://huggingface.co/collections/AngelSlim/qwen3-quant-68652e26da31740739d154f8)                              | ✅           | ✅          | ✅            | ✅         | ✅        |
| [Qwen2.5](https://huggingface.co/collections/AngelSlim/qwen2-25-quant-68652d6cbdf5c0d4b1c4499a)                             | ✅           | ✅          | ✅            | ✅         | ✅        |
| [DeepSeek-R1-Distill-Qwen](https://huggingface.co/collections/AngelSlim/deepseek-r1-distill-quant-68652f16a9c206b030b05f7f) | ✅           | ✅          | ✅            | ✅         | ✅        |
| [QwQ](https://huggingface.co/collections/AngelSlim/qwen3-quant-68652e26da31740739d154f8)                                    | ✅           | ✅          | ✅            | ✅         | ✅        |

### Speculative Decoding

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

| Qwen3  Models   | Hunyuan Models     |
| ----------|----------|
| ✅ [Qwen3-1.7B](https://huggingface.co/AngelSlim/Qwen3-1.7B_eagle3)    |✅ [Hunyuan-1.8B-Instruct](https://huggingface.co/AngelSlim/Hunyuan-1.8B-Instruct_eagle3)    |
| ✅ [Qwen3-4B](https://huggingface.co/AngelSlim/Qwen3-4B_eagle3)        |✅ [Hunyuan-4B-Instruct](https://huggingface.co/AngelSlim/Hunyuan-4B-Instruct_eagle3)        |
| ✅ [Qwen3-8B](https://huggingface.co/AngelSlim/Qwen3-8B_eagle3)        |✅ [Hunyuan-7B-Instruct](https://huggingface.co/AngelSlim/Hunyuan-7B-Instruct_eagle3)        |
| ✅ [Qwen3-14B](https://huggingface.co/AngelSlim/Qwen3-14B_eagle3)      |
| ✅ [Qwen3-32B](https://huggingface.co/AngelSlim/Qwen3-32B_eagle3)      |
| ✅ [Qwen3-30B-A3B](https://huggingface.co/AngelSlim/Qwen3-a3B_eagle3)  |

## 🛎️How to Use

### Install AngelSlim

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

```shell
pip install angelslim
```

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

```shell
cd AngelSlim && python setup.py install
```

For more detailed installation instructions, please refer to the [Installation Documentation](https://angelslim.readthedocs.io/zh-cn/latest/getting_started/installation.html).

### Quick Start

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

  ```shell
  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
  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](https://angelslim.readthedocs.io/zh-cn/latest/getting_started/quickstrat.html).

### 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:

```shell
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](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.


```shell
bash deploy/run_vllm.sh $MODEL_PATH
```

**SGLang**


Use the following script to launch a [SGLang](https://github.com/sgl-project/sglang) server, recommended version `sglang>=0.4.6.post1`.

```shell
bash deploy/run_sglang.sh $MODEL_PATH
```

#### 3. Service Invocation

Invoke requests via [OpenAI's API format](https://platform.openai.com/docs/api-reference/introduction):

```shell
bash deploy/openai.sh $MODEL_PATH
```

#### 4. Performance Evaluation

Evaluate the performance of quantized model using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness), recommended version`lm-eval>=0.4.8`:

```shell
bash deploy/lm_eval.sh $MODEL_PATH
```

For more detaileds, please refer to the [Deployment Documentation](https://angelslim.readthedocs.io/zh-cn/latest/deployment/deploy.html).


## 📈 Benchmark

### (1) Quantization

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)

#### Hunyuan Series Models

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

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

#### 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`:

<table>
  <thead>
    <tr><th>Model</th><th>Quantization</th><th>CEVAL</th><th>MMLU</th><th>GSM8K</th><th>HUMANEVAL</th></tr>
  </thead>
  <tbody>
    <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>
    <tr><td>FP8-Static</td><td>45.99</td><td>46.87</td><td>38.06</td><td>18.90</td></tr>
    <tr><td>FP8-Dynamic</td><td>45.99</td><td>46.93</td><td>38.29</td><td>20.73</td></tr>
    <tr><td>INT8-Dynamic</td><td>45.17</td><td>46.95</td><td>41.17</td><td>21.34</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>78.23</td><td>74.79</td><td>86.96</td><td>62.20</td></tr>
    <tr><td>FP8-Dynamic</td><td>78.45</td><td>74.75</td><td>87.64</td><td>62.80</td></tr>
    <tr><td>INT8-Dynamic</td><td>78.01</td><td>74.84</td><td>86.96</td><td>67.07</td></tr>
    <tr><td>INT4-GPTQ</td><td>77.19</td><td>73.26</td><td>86.43</td><td>62.20</td></tr>
    <tr><td>INT4-AWQ</td><td>76.15</td><td>73.59</td><td>86.96</td><td>63.41</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>82.62</td><td>78.57</td><td>89.46</td><td>57.32</td></tr>
    <tr><td>FP8-Dynamic</td><td>82.24</td><td>78.92</td><td>88.32</td><td>52.44</td></tr>
    <tr><td>INT8-Dynamic</td><td>81.87</td><td>78.13</td><td>86.28</td><td>56.10</td></tr>
    <tr><td>INT4-GPTQ</td><td>81.05</td><td>78.02</td><td>87.34</td><td>57.93</td></tr>
    <tr><td>INT4-AWQ</td><td>82.02</td><td>77.68</td><td>84.23</td><td>61.59</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>86.92</td><td>81.78</td><td>70.20</td><td>39.63</td></tr>
    <tr><td>FP8-Dynamic</td><td>86.55</td><td>81.89</td><td>70.43</td><td>38.41</td></tr>
    <tr><td>INT4-GPTQ</td><td>86.18</td><td>81.01</td><td>-</td><td>43.29</td></tr>
    <tr><td>INT4-AWQ</td><td>86.18</td><td>81.54</td><td>-</td><td>36.59</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>83.95</td><td>79.47</td><td>89.01</td><td>31.10</td></tr>
    <tr><td>FP8-Dynamic</td><td>84.10</td><td>79.40</td><td>89.16</td><td>32.93</td></tr>
    <tr><td>INT8-Dynamic</td><td>83.36</td><td>79.48</td><td>89.16</td><td>34.15</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>89.67</td><td>86.19</td><td>86.96</td><td>27.44</td></tr>
    <tr><td>FP8-Dynamic</td><td>89.67</td><td>86.18</td><td>85.22</td><td>28.05</td></tr>
    <tr><td>INT8-Dynamic</td><td>88.93</td><td>86.20</td><td>86.20</td><td>23.78</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>85.44</td><td>81.91</td><td>75.36</td><td>42.68</td></tr>
    <tr><td>FP8-Dynamic</td><td>85.07</td><td>81.93</td><td>75.66</td><td>42.07</td></tr>
    <tr><td>INT4-GPTQ</td><td>84.03</td><td>81.26</td><td>68.23</td><td>45.73</td></tr>
    <tr><td>INT4-AWQ</td><td>83.58</td><td>81.01</td><td>68.69</td><td>43.29</td></tr>
  </tbody>
</table>

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

#### 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`:

<table>
  <thead>
    <tr><th>Model</th><th>Quantization</th><th>CEVAL</th><th>MMLU</th><th>GSM8K</th></tr>
  </thead>
  <tbody>
    <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>
    <tr><td>FP8-Static</td><td>66.27</td><td>60.23</td><td>-</td></tr>
    <tr><td>FP8-Dynamic</td><td>66.79</td><td>60.08</td><td>51.71</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>81.13</td><td>74.03</td><td>79.30</td></tr>
    <tr><td>FP8-Dynamic</td><td>80.31</td><td>74.07</td><td>79.00</td></tr>
    <tr><td>INT4-GPTQ</td><td>79.05</td><td>73.05</td><td>74.75</td></tr>
    <tr><td>INT4-AWQ</td><td>79.35</td><td>73.22</td><td>79.38</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>87.59</td><td>83.08</td><td>81.58</td></tr>
    <tr><td>FP8-Dynamic</td><td>87.30</td><td>83.04</td><td>81.58</td></tr>
    <tr><td>INT4-GPTQ</td><td>86.70</td><td>82.45</td><td>82.03</td></tr>
    <tr><td>INT4-AWQ</td><td>87.00</td><td>82.64</td><td>-</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>53.57</td><td>54.17</td><td>76.19</td></tr>
    <tr><td>FP8-Dynamic</td><td>52.97</td><td>54.13</td><td>74.15</td></tr>
    <tr><td>INT4-GPTQ</td><td>51.86</td><td>52.44</td><td>75.89</td></tr>
    <tr><td>INT4-AWQ</td><td>53.49</td><td>53.70</td><td>-</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>77.56</td><td>74.66</td><td>86.73</td></tr>
    <tr><td>FP8-Dynamic</td><td>76.82</td><td>74.63</td><td>87.11</td></tr>
    <tr><td>INT4-GPTQ</td><td>74.29</td><td>72.37</td><td>84.61</td></tr>
    <tr><td>INT4-AWQ</td><td>74.81</td><td>73.00</td><td>86.05</td></tr>
    <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>
    <tr><td>FP8-Static</td><td>83.43</td><td>80.90</td><td>87.57</td></tr>
    <tr><td>FP8-Dynamic</td><td>83.73</td><td>81.10</td><td>86.43</td></tr>
    <tr><td>INT4-GPTQ</td><td>84.10</td><td>79.80</td><td>86.73</td></tr>
    <tr><td>INT4-AWQ</td><td>82.84</td><td>80.15</td><td>87.19</td></tr>
  </tbody>
</table>

### (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`:

<table>
  <thead>
    <tr>
        <th>&nbsp</th><th>&nbsp</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>τ</th><th>Speedup</th><th>τ</th><th>Speedup</th><th>τ</th><th>Speedup</th><th>τ</th><th>Speedup</th><th>τ</th></tr>
  </thead>
  <tbody>
    <!-- <tr><td colspan="12" style="text-align: center; vertical-align: middle;"><strong>Temperature=0</strong></td></tr> -->
    <tr><td rowspan="6"><strong>T=0</strong></td>
    <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>
    <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>
    <tr><td>Qwen3-8B</td><td>2.63x</td><td>3.65</td><td>2.76x</td><td>3.85</td><td>2.82x</td><td>3.90</td><td>2.62x</td><td>3.48</td><td>2.70x</td><td>3.72</td></tr>
    <tr><td>Qwen3-14B</td><td>2.23x</td><td>3.30</td><td>2.53x</td><td>3.74</td><td>2.56x</td><td>3.79</td><td>2.16x</td><td>3.13</td><td>2.37x</td><td>3.49</td></tr>
    <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>
    <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>
    <!-- <tr><td colspan="12" style="text-align: center; vertical-align: middle;"><strong>Temperature=1</strong></td></tr> -->
    <tr><td rowspan="6"><strong>T=1</strong></td>
    <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>
    <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>
    <tr><td>Qwen3-8B</td><td>1.98x</td><td>2.75</td><td>2.25x</td><td>3.11</td><td>2.31x</td><td>3.15</td><td>2.10x</td><td>2.76</td><td>2.90x</td><td>2.94</td></tr>
    <tr><td>Qwen3-14B</td><td>1.71x</td><td>2.61</td><td>1.95x</td><td>2.87</td><td>2.04x</td><td>3.08</td><td>1.68x</td><td>2.55</td><td>2.90x</td><td>2.78</td></tr>
    <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>
    <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>
  </tbody>
</table>

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

<table>
  <thead>
    <tr>
        <th>&nbsp</th><th>&nbsp</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>τ</th><th>Speedup</th><th>τ</th><th>Speedup</th><th>τ</th><th>Speedup</th><th>τ</th><th>Speedup</th><th>τ</th></tr>
  </thead>
  <tbody>
    <!-- <tr><td colspan="12" style="text-align: center; vertical-align: middle;"><strong>Temperature=0</strong></td></tr> -->
    <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>

## 📝 License

The code for this project is open-sourced under the [License for AngelSlim](LICENSE).

## 🔗 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](https://github.com/Tencent/AngelSlim/issues) or join our [WeChat technical discussion group](./docs/source/assets/angel_slim_wechat.png).