Upload folder using huggingface_hub
Browse files- README.md +353 -0
- added_tokens.json +10 -0
- chat_template.jinja +7 -0
- config.json +210 -0
- configuration_minicpm.py +203 -0
- generation_config.json +8 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +747 -0
- modeling_minicpm.py +0 -0
- recipe.yaml +38 -0
- special_tokens_map.json +40 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +119 -0
README.md
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| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
tags:
|
| 4 |
+
- text-generation-inference
|
| 5 |
+
- agent
|
| 6 |
+
base_model: openbmb/AgentCPM-Report
|
| 7 |
+
---
|
| 8 |
+
# AgentCPM-Report: Gemini-2.5-pro-DeepResearch Level Local DeepResearch
|
| 9 |
+
|
| 10 |
+
<p align="center">
|
| 11 |
+
<a href='https://huggingface.co/openbmb/AgentCPM-Report'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AgentCPM--Report-yellow'>
|
| 12 |
+
<a href='https://huggingface.co/openbmb/AgentCPM-Report-GGUF'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AgentCPM--Report--GGUF-yellow'>
|
| 13 |
+
<a href='https://github.com/OpenBMB/AgentCPM'><img src='https://img.shields.io/badge/GitHub-AgentCPM-blue?logo=github'>
|
| 14 |
+
<a href='https://github.com/OpenBMB/UltraRAG'><img src='https://img.shields.io/badge/GitHub-UltraRAG-blue?logo=github'>
|
| 15 |
+
</p>
|
| 16 |
+
|
| 17 |
+
## Links & Resources
|
| 18 |
+
### 📊 AgentCPM-Report Models
|
| 19 |
+
- **[AgentCPM-Report](https://huggingface.co/openbmb/AgentCPM-Report)** The Gemini-2.5-pro-DeepResearch Level Local DeepResearch Model
|
| 20 |
+
- **[AgentCPM-Report-GGUF](https://huggingface.co/openbmb/AgentCPM-Report-GGUF)** The GGUF version of AgentCPM-Report
|
| 21 |
+
|
| 22 |
+
### 🤖 AgentCPM-Explore Models
|
| 23 |
+
- **[AgentCPM-Explore](https://huggingface.co/openbmb/AgentCPM-Explore)** The first open-source agent model with 4B parameters to appear on 8 widely used long-horizon agent benchmarks.
|
| 24 |
+
- **[AgentCPM-Explore-GGUF](https://huggingface.co/openbmb/AgentCPM-Explore-GGUF)** The GGUF version of AgentCPM-Explore
|
| 25 |
+
|
| 26 |
+
### 💻 Code & Framework
|
| 27 |
+
- **[AgentCPM](https://github.com/OpenBMB/AgentCPM)** Our code for AgentCPM Series
|
| 28 |
+
- **[UltraRAG](https://github.com/OpenBMB/UltraRAG)** A RAG Framework, Less Code, Lower Barrier, Faster Deployment
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
## News
|
| 32 |
+
- [2026-01-20] 🚀🚀🚀 We open-sourced AgentCPM-Report built on MiniCPM4.1-8B, capable of matching top closed-source commercial systems like Gemini-2.5-pro-DeepResearch in report generation.
|
| 33 |
+
|
| 34 |
+
## Overview
|
| 35 |
+
AgentCPM-Report is an open-source large language model agent jointly developed by [THUNLP](https://nlp.csai.tsinghua.edu.cn), Renmin University of China [RUCBM](https://github.com/RUCBM), and [ModelBest](https://modelbest.cn/en). It is based on the [MiniCPM4.1](https://github.com/OpenBMB/MiniCPM) 8B-parameter base model. It accepts user instructions as input and autonomously generates long-form reports. Key highlights:
|
| 36 |
+
|
| 37 |
+
- **Extreme Performance, Minimal Footprint**: Through an average of 40 rounds of deep retrieval and nearly 100 rounds of chain-of-thought reasoning, it achieves comprehensive information mining and restructuring, enabling edge-side models to produce logically rigorous, deeply insightful long-form articles with tens of thousands of words. With just 8 billion parameters, it delivers performance on par with top-tier closed-source systems in deep research tasks.
|
| 38 |
+
- **Physical Isolation, Local Security**: Specifically designed for high-privacy scenarios, it supports fully offline and agile local deployment, completely eliminating the risk of cloud data leaks. Leveraging our UltraRAG framework, it efficiently mounts and understands your local private knowledge base, securely transforming core confidential data into highly valuable professional decision-making reports without ever leaving its domain.
|
| 39 |
+
|
| 40 |
+
## Demo Cases
|
| 41 |
+
<div align="center">
|
| 42 |
+
<a href="https://www.youtube.com/watch?v=d5XWONt0PWo"><img src="https://img.youtube.com/vi/d5XWONt0PWo/0.jpg", width=70%></a>
|
| 43 |
+
</div>
|
| 44 |
+
|
| 45 |
+
**You can watch our demo video here [Demo](https://www.youtube.com/watch?v=d5XWONt0PWo) 🔗**
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
## Quick Start
|
| 49 |
+
### Docker Deployment
|
| 50 |
+
<div align="center">
|
| 51 |
+
<a href="https://www.youtube.com/watch?v=ze8qJRrass4"><img src="https://img.youtube.com/vi/ze8qJRrass4/0.jpg", width=70%></a>
|
| 52 |
+
</div>
|
| 53 |
+
|
| 54 |
+
**You can watch our demo video here [Tutorial](https://www.youtube.com/watch?v=ze8qJRrass4) 🔗**
|
| 55 |
+
|
| 56 |
+
We provide a minimal one-click `docker-compose` deployment integrated with UltraRAG, including the RAG framework UltraRAG2.0, the model inference framework vllm, and the vector database milvus. If you want CPU inference, we also provide a llama.cpp-based version for gguf models—just switch `docker-compose.yml` to `docker-compose.cpu.yml`.
|
| 57 |
+
|
| 58 |
+
``` bash
|
| 59 |
+
git clone git@github.com:OpenBMB/UltraRAG.git
|
| 60 |
+
cd UltraRAG
|
| 61 |
+
git checkout agentcpm-report-demo
|
| 62 |
+
cd agentcpm-report-demo
|
| 63 |
+
cp env.example .env
|
| 64 |
+
docker-compose -f docker-compose.yml up -d --build
|
| 65 |
+
docker-compose -f docker-compose.yml logs -f ultrarag-ui
|
| 66 |
+
```
|
| 67 |
+
The first startup pulls images, downloads the model, and configures the environment, which takes about 30 minutes.
|
| 68 |
+
Then open `http://localhost:5050`. If you can see the UI, your deployment is successful.
|
| 69 |
+
Follow the UI instructions to upload local files, chunk them, and build indexes; then in the Chat section, select AgentCPM-Report in the pipeline to start your workflow.
|
| 70 |
+
|
| 71 |
+
(Optional) You can import [Wiki2024](https://modelscope.cn/datasets/UltraRAG/UltraRAG_Benchmark/tree/master/corpus/wiki24) as the writing database.
|
| 72 |
+
|
| 73 |
+
You can read more tutorials about AgentCPM-Report in the [documentation](https://ultrarag.openbmb.cn/pages/en/demo/deepresearch).
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
## Evaluation
|
| 77 |
+
<table align="center">
|
| 78 |
+
<thead>
|
| 79 |
+
<tr>
|
| 80 |
+
<th align="center">DeepResearch Bench</th>
|
| 81 |
+
<th align="center">Overall</th>
|
| 82 |
+
<th align="center">Comprehensiveness</th>
|
| 83 |
+
<th align="center">Insight</th>
|
| 84 |
+
<th align="center">Instruction Following</th>
|
| 85 |
+
<th align="center">Readability</th>
|
| 86 |
+
</tr>
|
| 87 |
+
</thead>
|
| 88 |
+
<tbody>
|
| 89 |
+
<tr>
|
| 90 |
+
<td align="center">Doubao-research</td>
|
| 91 |
+
<td align="center">44.34</td>
|
| 92 |
+
<td align="center">44.84</td>
|
| 93 |
+
<td align="center">40.56</td>
|
| 94 |
+
<td align="center">47.95</td>
|
| 95 |
+
<td align="center">44.69</td>
|
| 96 |
+
</tr>
|
| 97 |
+
<tr>
|
| 98 |
+
<td align="center">Claude-research</td>
|
| 99 |
+
<td align="center">45.00</td>
|
| 100 |
+
<td align="center">45.34</td>
|
| 101 |
+
<td align="center">42.79</td>
|
| 102 |
+
<td align="center">47.58</td>
|
| 103 |
+
<td align="center">44.66</td>
|
| 104 |
+
</tr>
|
| 105 |
+
<tr>
|
| 106 |
+
<td align="center">OpenAI-deepresearch</td>
|
| 107 |
+
<td align="center">46.45</td>
|
| 108 |
+
<td align="center">46.46</td>
|
| 109 |
+
<td align="center">43.73</td>
|
| 110 |
+
<td align="center">49.39</td>
|
| 111 |
+
<td align="center">47.22</td>
|
| 112 |
+
</tr>
|
| 113 |
+
<tr>
|
| 114 |
+
<td align="center">Gemini-2.5-Pro-deepresearch</td>
|
| 115 |
+
<td align="center">49.71</td>
|
| 116 |
+
<td align="center">49.51</td>
|
| 117 |
+
<td align="center">49.45</td>
|
| 118 |
+
<td align="center">50.12</td>
|
| 119 |
+
<td align="center">50.00</td>
|
| 120 |
+
</tr>
|
| 121 |
+
<tr>
|
| 122 |
+
<td align="center">WebWeaver(Qwen3-30B-A3B)</td>
|
| 123 |
+
<td align="center">46.77</td>
|
| 124 |
+
<td align="center">45.15</td>
|
| 125 |
+
<td align="center">45.78</td>
|
| 126 |
+
<td align="center">49.21</td>
|
| 127 |
+
<td align="center">47.34</td>
|
| 128 |
+
</tr>
|
| 129 |
+
<tr>
|
| 130 |
+
<td align="center">WebWeaver(Claude-Sonnet-4)</td>
|
| 131 |
+
<td align="center">50.58</td>
|
| 132 |
+
<td align="center">51.45</td>
|
| 133 |
+
<td align="center">50.02</td>
|
| 134 |
+
<td align="center">50.81</td>
|
| 135 |
+
<td align="center">49.79</td>
|
| 136 |
+
</tr>
|
| 137 |
+
<tr>
|
| 138 |
+
<td align="center">Enterprise-DR(Gemini-2.5-Pro)</td>
|
| 139 |
+
<td align="center">49.86</td>
|
| 140 |
+
<td align="center">49.01</td>
|
| 141 |
+
<td align="center">50.28</td>
|
| 142 |
+
<td align="center">50.03</td>
|
| 143 |
+
<td align="center">49.98</td>
|
| 144 |
+
</tr>
|
| 145 |
+
<tr>
|
| 146 |
+
<td align="center">RhinoInsigh(Gemini-2.5-Pro)</td>
|
| 147 |
+
<td align="center">50.92</td>
|
| 148 |
+
<td align="center">50.51</td>
|
| 149 |
+
<td align="center">51.45</td>
|
| 150 |
+
<td align="center">51.72</td>
|
| 151 |
+
<td align="center">50.00</td>
|
| 152 |
+
</tr>
|
| 153 |
+
<tr>
|
| 154 |
+
<td align="center">AgentCPM-Report</td>
|
| 155 |
+
<td align="center">50.11</td>
|
| 156 |
+
<td align="center">50.54</td>
|
| 157 |
+
<td align="center">52.64</td>
|
| 158 |
+
<td align="center">48.87</td>
|
| 159 |
+
<td align="center">44.17</td>
|
| 160 |
+
</tr>
|
| 161 |
+
</tbody>
|
| 162 |
+
</table>
|
| 163 |
+
|
| 164 |
+
|
| 165 |
+
|
| 166 |
+
<table align="center">
|
| 167 |
+
<thead>
|
| 168 |
+
<tr>
|
| 169 |
+
<th align="center">DeepResearch Gym</th>
|
| 170 |
+
<th align="center">Avg.</th>
|
| 171 |
+
<th align="center">Clarity</th>
|
| 172 |
+
<th align="center">Depth</th>
|
| 173 |
+
<th align="center">Balance</th>
|
| 174 |
+
<th align="center">Breadth</th>
|
| 175 |
+
<th align="center">Support</th>
|
| 176 |
+
<th align="center">Insightfulness</th>
|
| 177 |
+
</tr>
|
| 178 |
+
</thead>
|
| 179 |
+
<tbody>
|
| 180 |
+
<tr>
|
| 181 |
+
<td align="center">Doubao-research</td>
|
| 182 |
+
<td align="center">84.46</td>
|
| 183 |
+
<td align="center">68.85</td>
|
| 184 |
+
<td align="center">93.12</td>
|
| 185 |
+
<td align="center">83.96</td>
|
| 186 |
+
<td align="center">93.33</td>
|
| 187 |
+
<td align="center">84.38</td>
|
| 188 |
+
<td align="center">83.12</td>
|
| 189 |
+
</tr>
|
| 190 |
+
<tr>
|
| 191 |
+
<td align="center">Claude-research</td>
|
| 192 |
+
<td align="center">80.25</td>
|
| 193 |
+
<td align="center">86.67</td>
|
| 194 |
+
<td align="center">96.88</td>
|
| 195 |
+
<td align="center">84.41</td>
|
| 196 |
+
<td align="center">96.56</td>
|
| 197 |
+
<td align="center">26.77</td>
|
| 198 |
+
<td align="center">90.22</td>
|
| 199 |
+
</tr>
|
| 200 |
+
<tr>
|
| 201 |
+
<td align="center">OpenAI-deepresearch</td>
|
| 202 |
+
<td align="center">91.27</td>
|
| 203 |
+
<td align="center">84.90</td>
|
| 204 |
+
<td align="center">98.10</td>
|
| 205 |
+
<td align="center">89.80</td>
|
| 206 |
+
<td align="center">97.40</td>
|
| 207 |
+
<td align="center">88.40</td>
|
| 208 |
+
<td align="center">89.00</td>
|
| 209 |
+
</tr>
|
| 210 |
+
<tr>
|
| 211 |
+
<td align="center">Gemini-2.5-pro-deepresearch</td>
|
| 212 |
+
<td align="center">96.02</td>
|
| 213 |
+
<td align="center">90.71</td>
|
| 214 |
+
<td align="center">99.90</td>
|
| 215 |
+
<td align="center">93.37</td>
|
| 216 |
+
<td align="center">99.69</td>
|
| 217 |
+
<td align="center">95.00</td>
|
| 218 |
+
<td align="center">97.45</td>
|
| 219 |
+
</tr>
|
| 220 |
+
<tr>
|
| 221 |
+
<td align="center">WebWeaver (Qwen3-30b-a3b)</td>
|
| 222 |
+
<td align="center">77.27</td>
|
| 223 |
+
<td align="center">71.88</td>
|
| 224 |
+
<td align="center">85.51</td>
|
| 225 |
+
<td align="center">75.80</td>
|
| 226 |
+
<td align="center">84.78</td>
|
| 227 |
+
<td align="center">63.77</td>
|
| 228 |
+
<td align="center">81.88</td>
|
| 229 |
+
</tr>
|
| 230 |
+
<tr>
|
| 231 |
+
<td align="center">WebWeaver (Claude-sonnet-4)</td>
|
| 232 |
+
<td align="center">96.77</td>
|
| 233 |
+
<td align="center">90.50</td>
|
| 234 |
+
<td align="center">99.87</td>
|
| 235 |
+
<td align="center">94.30</td>
|
| 236 |
+
<td align="center">100.00</td>
|
| 237 |
+
<td align="center">98.73</td>
|
| 238 |
+
<td align="center">97.22</td>
|
| 239 |
+
</tr>
|
| 240 |
+
<tr>
|
| 241 |
+
<td align="center">AgentCPM-Report</td>
|
| 242 |
+
<td align="center">98.48</td>
|
| 243 |
+
<td align="center">95.10</td>
|
| 244 |
+
<td align="center">100.00</td>
|
| 245 |
+
<td align="center">98.50</td>
|
| 246 |
+
<td align="center">100.00</td>
|
| 247 |
+
<td align="center">97.30</td>
|
| 248 |
+
<td align="center">100.00</td>
|
| 249 |
+
</tr>
|
| 250 |
+
</tbody>
|
| 251 |
+
</table>
|
| 252 |
+
|
| 253 |
+
<table align="center">
|
| 254 |
+
<thead>
|
| 255 |
+
<tr>
|
| 256 |
+
<th align="center">DeepConsult</th>
|
| 257 |
+
<th align="center">Avg.</th>
|
| 258 |
+
<th align="center">Win</th>
|
| 259 |
+
<th align="center">Tie</th>
|
| 260 |
+
<th align="center">Lose</th>
|
| 261 |
+
</tr>
|
| 262 |
+
</thead>
|
| 263 |
+
<tbody>
|
| 264 |
+
<tr>
|
| 265 |
+
<td align="center">Doubao-research</td>
|
| 266 |
+
<td align="center">5.42</td>
|
| 267 |
+
<td align="center">29.95</td>
|
| 268 |
+
<td align="center">40.35</td>
|
| 269 |
+
<td align="center">29.70</td>
|
| 270 |
+
</tr>
|
| 271 |
+
<tr>
|
| 272 |
+
<td align="center">Claude-research</td>
|
| 273 |
+
<td align="center">4.60</td>
|
| 274 |
+
<td align="center">25.00</td>
|
| 275 |
+
<td align="center">38.89</td>
|
| 276 |
+
<td align="center">36.11</td>
|
| 277 |
+
</tr>
|
| 278 |
+
<tr>
|
| 279 |
+
<td align="center">OpenAI-deepresearch</td>
|
| 280 |
+
<td align="center">5.00</td>
|
| 281 |
+
<td align="center">0.00</td>
|
| 282 |
+
<td align="center">100.00</td>
|
| 283 |
+
<td align="center">0.00</td>
|
| 284 |
+
</tr>
|
| 285 |
+
<tr>
|
| 286 |
+
<td align="center">Gemini-2.5-Pro-deepresearch</td>
|
| 287 |
+
<td align="center">6.70</td>
|
| 288 |
+
<td align="center">61.27</td>
|
| 289 |
+
<td align="center">31.13</td>
|
| 290 |
+
<td align="center">7.60</td>
|
| 291 |
+
</tr>
|
| 292 |
+
<tr>
|
| 293 |
+
<td align="center">WebWeaver(Qwen3-30B-A3B)</td>
|
| 294 |
+
<td align="center">4.57</td>
|
| 295 |
+
<td align="center">28.65</td>
|
| 296 |
+
<td align="center">34.90</td>
|
| 297 |
+
<td align="center">36.46</td>
|
| 298 |
+
</tr>
|
| 299 |
+
<tr>
|
| 300 |
+
<td align="center">WebWeaver(Claude-Sonnet-4)</td>
|
| 301 |
+
<td align="center">6.96</td>
|
| 302 |
+
<td align="center">66.86</td>
|
| 303 |
+
<td align="center">10.47</td>
|
| 304 |
+
<td align="center">22.67</td>
|
| 305 |
+
</tr>
|
| 306 |
+
<tr>
|
| 307 |
+
<td align="center">Enterprise-DR(Gemini-2.5-Pro)</td>
|
| 308 |
+
<td align="center">6.82</td>
|
| 309 |
+
<td align="center">71.57</td>
|
| 310 |
+
<td align="center">19.12</td>
|
| 311 |
+
<td align="center">9.31</td>
|
| 312 |
+
</tr>
|
| 313 |
+
<tr>
|
| 314 |
+
<td align="center">RhinoInsigh(Gemini-2.5-Pro)</td>
|
| 315 |
+
<td align="center">6.82</td>
|
| 316 |
+
<td align="center">68.51</td>
|
| 317 |
+
<td align="center">11.02</td>
|
| 318 |
+
<td align="center">20.47</td>
|
| 319 |
+
</tr>
|
| 320 |
+
<tr>
|
| 321 |
+
<td align="center">AgentCPM-Report</td>
|
| 322 |
+
<td align="center">6.60</td>
|
| 323 |
+
<td align="center">57.60</td>
|
| 324 |
+
<td align="center">13.73</td>
|
| 325 |
+
<td align="center">28.68</td>
|
| 326 |
+
</tr>
|
| 327 |
+
</tbody>
|
| 328 |
+
</table>
|
| 329 |
+
|
| 330 |
+
Our evaluation datasets include DeepResearch Bench, DeepConsult, and DeepResearch Gym. The writing-time knowledge base includes about 2.7 million [Arxiv papers](https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv) and about 200,000 internal webpage summaries.
|
| 331 |
+
|
| 332 |
+
## Acknowledgements
|
| 333 |
+
This project would not be possible without the support and contributions of the open-source community. During development, we referred to and used multiple excellent open-source frameworks, models, and data resources, including [verl](https://github.com/volcengine/verl), [UltraRAG](https://github.com/OpenBMB/UltraRAG), [MiniCPM4.1](https://github.com/OpenBMB/MiniCPM), and [SurveyGo](https://surveygo.modelbest.cn/).
|
| 334 |
+
|
| 335 |
+
## Contributions
|
| 336 |
+
Project leads: Yishan Li, Wentong Chen
|
| 337 |
+
|
| 338 |
+
Contributors: Yishan Li, Wentong Chen, Yukun Yan, Mingwei Li, Sen Mei, Xiaorong Wang, Kunpeng Liu, Xin Cong, Shuo Wang, Zhong Zhang, Yaxi Lu, Zhenghao Liu, Yankai Lin, Zhiyuan Liu, Maosong Sun
|
| 339 |
+
|
| 340 |
+
Advisors: Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
|
| 341 |
+
|
| 342 |
+
## Citation
|
| 343 |
+
|
| 344 |
+
If **AgentCPM-Report** is helpful for your research, please cite it as follows:
|
| 345 |
+
|
| 346 |
+
```bibtex
|
| 347 |
+
@software{AgentCPMReport2026,
|
| 348 |
+
title = {AgentCPM-Report: Gemini-2.5-pro-DeepResearch Level Local DeepResearch},
|
| 349 |
+
author = {Yishan Li, Wentong Chen, Yukun Yan, Mingwei Li, Sen Mei, Xiaorong Wang, Kunpeng Liu, Xin Cong, Shuo Wang, Zhong Zhang, Yaxi Lu, Zhenghao Liu, Yankai Lin, Zhiyuan Liu, Maosong Sun},
|
| 350 |
+
year = {2026},
|
| 351 |
+
url = {https://github.com/OpenBMB/AgentCPM}
|
| 352 |
+
}
|
| 353 |
+
```
|
added_tokens.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"<|execute_end|>": 73444,
|
| 3 |
+
"<|execute_start|>": 73443,
|
| 4 |
+
"<|fim_middle|>": 73446,
|
| 5 |
+
"<|fim_prefix|>": 73445,
|
| 6 |
+
"<|fim_suffix|>": 73447,
|
| 7 |
+
"<|im_end|>": 73440,
|
| 8 |
+
"<|im_start|>": 73441,
|
| 9 |
+
"<|tool_call|>": 73442
|
| 10 |
+
}
|
chat_template.jinja
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{% for message in messages %}{{'<|im_start|>' + message['role'] + '
|
| 2 |
+
' + message['content'] + '<|im_end|>' + '
|
| 3 |
+
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
|
| 4 |
+
' }}{% if enable_thinking is defined and enable_thinking is false %}{{ '<think>
|
| 5 |
+
|
| 6 |
+
</think>
|
| 7 |
+
' }}{% endif %}{% endif %}
|
config.json
ADDED
|
@@ -0,0 +1,210 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"MiniCPMForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"auto_map": {
|
| 8 |
+
"AutoConfig": "configuration_minicpm.MiniCPMConfig",
|
| 9 |
+
"AutoModel": "modeling_minicpm.MiniCPMForCausalLM",
|
| 10 |
+
"AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
|
| 11 |
+
"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
|
| 12 |
+
"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
|
| 13 |
+
},
|
| 14 |
+
"bos_token_id": 1,
|
| 15 |
+
"dim_model_base": 256,
|
| 16 |
+
"dtype": "bfloat16",
|
| 17 |
+
"eos_token_id": 73440,
|
| 18 |
+
"hidden_act": "silu",
|
| 19 |
+
"hidden_size": 4096,
|
| 20 |
+
"initializer_range": 0.1,
|
| 21 |
+
"intermediate_size": 16384,
|
| 22 |
+
"max_position_embeddings": 65536,
|
| 23 |
+
"model_type": "minicpm",
|
| 24 |
+
"mup_denominator": 32,
|
| 25 |
+
"num_attention_heads": 32,
|
| 26 |
+
"num_hidden_layers": 32,
|
| 27 |
+
"num_key_value_heads": 2,
|
| 28 |
+
"pad_token_id": 73440,
|
| 29 |
+
"pretraining_tp": 1,
|
| 30 |
+
"quantization_config": {
|
| 31 |
+
"config_groups": {
|
| 32 |
+
"group_0": {
|
| 33 |
+
"format": "pack-quantized",
|
| 34 |
+
"input_activations": null,
|
| 35 |
+
"output_activations": null,
|
| 36 |
+
"targets": [
|
| 37 |
+
"Linear"
|
| 38 |
+
],
|
| 39 |
+
"weights": {
|
| 40 |
+
"actorder": null,
|
| 41 |
+
"block_structure": null,
|
| 42 |
+
"dynamic": false,
|
| 43 |
+
"group_size": 32,
|
| 44 |
+
"num_bits": 4,
|
| 45 |
+
"observer": "mse",
|
| 46 |
+
"observer_kwargs": {},
|
| 47 |
+
"strategy": "group",
|
| 48 |
+
"symmetric": true,
|
| 49 |
+
"type": "int"
|
| 50 |
+
}
|
| 51 |
+
}
|
| 52 |
+
},
|
| 53 |
+
"format": "pack-quantized",
|
| 54 |
+
"global_compression_ratio": null,
|
| 55 |
+
"ignore": [
|
| 56 |
+
"lm_head"
|
| 57 |
+
],
|
| 58 |
+
"kv_cache_scheme": null,
|
| 59 |
+
"quant_method": "compressed-tensors",
|
| 60 |
+
"quantization_status": "compressed",
|
| 61 |
+
"sparsity_config": {},
|
| 62 |
+
"transform_config": {},
|
| 63 |
+
"version": "0.12.3.a20251110"
|
| 64 |
+
},
|
| 65 |
+
"rms_norm_eps": 1e-06,
|
| 66 |
+
"rope_scaling": {
|
| 67 |
+
"long_factor": [
|
| 68 |
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0.9982316082870437,
|
| 69 |
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1.033048153422584,
|
| 70 |
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1.0749920956484724,
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|
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1.4535223827776373,
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1.5807816745852985,
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|
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| 84 |
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| 90 |
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| 91 |
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| 93 |
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|
| 99 |
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| 100 |
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36.358112337548896,
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44.425355159339965,
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| 107 |
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50.90428855401433,
|
| 108 |
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52.648136512661125,
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| 109 |
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|
| 110 |
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55.537098635632745,
|
| 111 |
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56.7077647874992,
|
| 112 |
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|
| 113 |
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58.58171910802236,
|
| 114 |
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59.31882031581807,
|
| 115 |
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59.94433101822328,
|
| 116 |
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60.47314411958625,
|
| 117 |
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60.918782569507,
|
| 118 |
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61.29331890286281,
|
| 119 |
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61.60738599471455,
|
| 120 |
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61.87024727431288,
|
| 121 |
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62.089902123428836,
|
| 122 |
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62.27320880977746,
|
| 123 |
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62.42601274014111,
|
| 124 |
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62.55327203194878,
|
| 125 |
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62.65917552585329,
|
| 126 |
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62.74725058582382,
|
| 127 |
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62.82045955451526,
|
| 128 |
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62.88128472678279,
|
| 129 |
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62.931802319077946,
|
| 130 |
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62.97374626130382,
|
| 131 |
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63.008562806439365
|
| 132 |
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],
|
| 133 |
+
"original_max_position_embeddings": 65536,
|
| 134 |
+
"rope_type": "longrope",
|
| 135 |
+
"short_factor": [
|
| 136 |
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0.9982316082870437,
|
| 137 |
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1.033048153422584,
|
| 138 |
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1.0749920956484724,
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1.1255096879436193,
|
| 140 |
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1.1863348602111476,
|
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1.259543828902579,
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1.3476188888731149,
|
| 143 |
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1.4535223827776373,
|
| 144 |
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1.5807816745852985,
|
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1.7335856049489526,
|
| 146 |
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1.9168922912975785,
|
| 147 |
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2.1365471404135326,
|
| 148 |
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2.3994084200118646,
|
| 149 |
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2.713475511863602,
|
| 150 |
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3.0880118452194134,
|
| 151 |
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3.533650295140154,
|
| 152 |
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4.062463396503134,
|
| 153 |
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4.687974098908333,
|
| 154 |
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5.425075306704039,
|
| 155 |
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6.289818967956352,
|
| 156 |
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7.29902962722721,
|
| 157 |
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8.469695779093664,
|
| 158 |
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9.81809877306655,
|
| 159 |
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11.358657902065282,
|
| 160 |
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13.102505860712087,
|
| 161 |
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15.055862949967128,
|
| 162 |
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17.218348131364184,
|
| 163 |
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19.581439255386453,
|
| 164 |
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22.127353314656723,
|
| 165 |
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24.828633849376587,
|
| 166 |
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27.6486820771775,
|
| 167 |
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30.54334096108829,
|
| 168 |
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33.46345345363812,
|
| 169 |
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36.358112337548896,
|
| 170 |
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39.17816056534983,
|
| 171 |
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41.879441100069684,
|
| 172 |
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44.425355159339965,
|
| 173 |
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46.78844628336223,
|
| 174 |
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48.95093146475928,
|
| 175 |
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50.90428855401433,
|
| 176 |
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52.648136512661125,
|
| 177 |
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54.18869564165987,
|
| 178 |
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55.537098635632745,
|
| 179 |
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56.7077647874992,
|
| 180 |
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57.71697544677006,
|
| 181 |
+
58.58171910802236,
|
| 182 |
+
59.31882031581807,
|
| 183 |
+
59.94433101822328,
|
| 184 |
+
60.47314411958625,
|
| 185 |
+
60.918782569507,
|
| 186 |
+
61.29331890286281,
|
| 187 |
+
61.60738599471455,
|
| 188 |
+
61.87024727431288,
|
| 189 |
+
62.089902123428836,
|
| 190 |
+
62.27320880977746,
|
| 191 |
+
62.42601274014111,
|
| 192 |
+
62.55327203194878,
|
| 193 |
+
62.65917552585329,
|
| 194 |
+
62.74725058582382,
|
| 195 |
+
62.82045955451526,
|
| 196 |
+
62.88128472678279,
|
| 197 |
+
62.931802319077946,
|
| 198 |
+
62.97374626130382,
|
| 199 |
+
63.008562806439365
|
| 200 |
+
]
|
| 201 |
+
},
|
| 202 |
+
"rope_theta": 10000.0,
|
| 203 |
+
"scale_depth": 1.4,
|
| 204 |
+
"scale_emb": 12,
|
| 205 |
+
"sparse_config": null,
|
| 206 |
+
"tie_word_embeddings": false,
|
| 207 |
+
"transformers_version": "4.57.6",
|
| 208 |
+
"use_cache": false,
|
| 209 |
+
"vocab_size": 73448
|
| 210 |
+
}
|
configuration_minicpm.py
ADDED
|
@@ -0,0 +1,203 @@
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2025 The OpenBMB Team. All rights reserved.
|
| 3 |
+
#
|
| 4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
+
# you may not use this file except in compliance with the License.
|
| 6 |
+
# You may obtain a copy of the License at
|
| 7 |
+
#
|
| 8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
+
#
|
| 10 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
+
# See the License for the specific language governing permissions and
|
| 14 |
+
# limitations under the License.
|
| 15 |
+
""" MiniCPM model configuration"""
|
| 16 |
+
|
| 17 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 18 |
+
from transformers.utils import logging
|
| 19 |
+
|
| 20 |
+
logger = logging.get_logger(__name__)
|
| 21 |
+
|
| 22 |
+
MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
class MiniCPMConfig(PretrainedConfig):
|
| 26 |
+
r"""
|
| 27 |
+
This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
|
| 28 |
+
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
| 29 |
+
defaults will yield a similar configuration to that of the MiniCPM-7B.
|
| 30 |
+
|
| 31 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| 32 |
+
documentation from [`PretrainedConfig`] for more information.
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
Args:
|
| 36 |
+
vocab_size (`int`, *optional*, defaults to 32000):
|
| 37 |
+
Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
|
| 38 |
+
`inputs_ids` passed when calling [`MiniCPMModel`]
|
| 39 |
+
hidden_size (`int`, *optional*, defaults to 4096):
|
| 40 |
+
Dimension of the hidden representations.
|
| 41 |
+
intermediate_size (`int`, *optional*, defaults to 11008):
|
| 42 |
+
Dimension of the MLP representations.
|
| 43 |
+
num_hidden_layers (`int`, *optional*, defaults to 32):
|
| 44 |
+
Number of hidden layers in the Transformer decoder.
|
| 45 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
| 46 |
+
Number of attention heads for each attention layer in the Transformer decoder.
|
| 47 |
+
num_key_value_heads (`int`, *optional*):
|
| 48 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
| 49 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
| 50 |
+
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
| 51 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
| 52 |
+
by meanpooling all the original heads within that group. For more details checkout [this
|
| 53 |
+
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
| 54 |
+
`num_attention_heads`.
|
| 55 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
| 56 |
+
The non-linear activation function (function or string) in the decoder.
|
| 57 |
+
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
| 58 |
+
The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
|
| 59 |
+
MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
|
| 60 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
| 61 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| 62 |
+
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
| 63 |
+
The epsilon used by the rms normalization layers.
|
| 64 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
| 65 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
| 66 |
+
relevant if `config.is_decoder=True`.
|
| 67 |
+
pad_token_id (`int`, *optional*):
|
| 68 |
+
Padding token id.
|
| 69 |
+
bos_token_id (`int`, *optional*, defaults to 1):
|
| 70 |
+
Beginning of stream token id.
|
| 71 |
+
eos_token_id (`int`, *optional*, defaults to 2):
|
| 72 |
+
End of stream token id.
|
| 73 |
+
pretraining_tp (`int`, *optional*, defaults to 1):
|
| 74 |
+
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
| 75 |
+
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
|
| 76 |
+
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
| 77 |
+
issue](https://github.com/pytorch/pytorch/issues/76232).
|
| 78 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
| 79 |
+
Whether to tie weight embeddings
|
| 80 |
+
rope_theta (`float`, *optional*, defaults to 10000.0):
|
| 81 |
+
The base period of the RoPE embeddings.
|
| 82 |
+
rope_scaling (`Dict`, *optional*):
|
| 83 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
| 84 |
+
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
| 85 |
+
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
| 86 |
+
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
| 87 |
+
these scaling strategies behave:
|
| 88 |
+
https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
| 89 |
+
experimental feature, subject to breaking API changes in future versions.
|
| 90 |
+
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
| 91 |
+
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
| 92 |
+
attention_dropout (`float`, *optional*, defaults to 0.0):
|
| 93 |
+
The dropout ratio for the attention probabilities.
|
| 94 |
+
|
| 95 |
+
```python
|
| 96 |
+
>>> from transformers import MiniCPMModel, MiniCPMConfig
|
| 97 |
+
|
| 98 |
+
>>> # Initializing a MiniCPM minicpm-7b style configuration
|
| 99 |
+
>>> configuration = MiniCPMConfig()
|
| 100 |
+
|
| 101 |
+
>>> # Initializing a model from the minicpm-7b style configuration
|
| 102 |
+
>>> model = MiniCPMModel(configuration)
|
| 103 |
+
|
| 104 |
+
>>> # Accessing the model configuration
|
| 105 |
+
>>> configuration = model.config
|
| 106 |
+
```"""
|
| 107 |
+
|
| 108 |
+
model_type = 'minicpm'
|
| 109 |
+
keys_to_ignore_at_inference = ['past_key_values']
|
| 110 |
+
|
| 111 |
+
def __init__(
|
| 112 |
+
self,
|
| 113 |
+
vocab_size=32000,
|
| 114 |
+
hidden_size=4096,
|
| 115 |
+
intermediate_size=11008,
|
| 116 |
+
num_hidden_layers=32,
|
| 117 |
+
num_attention_heads=32,
|
| 118 |
+
num_key_value_heads=None,
|
| 119 |
+
hidden_act='silu',
|
| 120 |
+
max_position_embeddings=2048,
|
| 121 |
+
initializer_range=0.02,
|
| 122 |
+
rms_norm_eps=1e-6,
|
| 123 |
+
use_cache=True,
|
| 124 |
+
pad_token_id=None,
|
| 125 |
+
bos_token_id=1,
|
| 126 |
+
eos_token_id=2,
|
| 127 |
+
pretraining_tp=1,
|
| 128 |
+
tie_word_embeddings=True,
|
| 129 |
+
rope_theta=10000.0,
|
| 130 |
+
rope_scaling=None,
|
| 131 |
+
attention_bias=False,
|
| 132 |
+
attention_dropout=0.0,
|
| 133 |
+
scale_emb=1,
|
| 134 |
+
dim_model_base=1,
|
| 135 |
+
scale_depth=1,
|
| 136 |
+
mup_denominator=32,
|
| 137 |
+
sparse_config=None,
|
| 138 |
+
**kwargs):
|
| 139 |
+
|
| 140 |
+
self.vocab_size = vocab_size
|
| 141 |
+
self.max_position_embeddings = max_position_embeddings
|
| 142 |
+
self.hidden_size = hidden_size
|
| 143 |
+
self.intermediate_size = intermediate_size
|
| 144 |
+
self.num_hidden_layers = num_hidden_layers
|
| 145 |
+
self.num_attention_heads = num_attention_heads
|
| 146 |
+
|
| 147 |
+
# for backward compatibility
|
| 148 |
+
if num_key_value_heads is None:
|
| 149 |
+
num_key_value_heads = num_attention_heads
|
| 150 |
+
|
| 151 |
+
self.num_key_value_heads = num_key_value_heads
|
| 152 |
+
self.hidden_act = hidden_act
|
| 153 |
+
self.initializer_range = initializer_range
|
| 154 |
+
self.rms_norm_eps = rms_norm_eps
|
| 155 |
+
self.pretraining_tp = pretraining_tp
|
| 156 |
+
self.use_cache = use_cache
|
| 157 |
+
self.rope_theta = rope_theta
|
| 158 |
+
self.rope_scaling = rope_scaling
|
| 159 |
+
# self._rope_scaling_validation()
|
| 160 |
+
self.attention_bias = attention_bias
|
| 161 |
+
self.attention_dropout = attention_dropout
|
| 162 |
+
self.scale_emb = scale_emb
|
| 163 |
+
self.dim_model_base = dim_model_base
|
| 164 |
+
self.scale_depth = scale_depth
|
| 165 |
+
# only used for Eagle Head
|
| 166 |
+
self.mup_denominator = mup_denominator
|
| 167 |
+
|
| 168 |
+
# sparse config
|
| 169 |
+
self.sparse_config = sparse_config
|
| 170 |
+
|
| 171 |
+
super().__init__(
|
| 172 |
+
pad_token_id=pad_token_id,
|
| 173 |
+
bos_token_id=bos_token_id,
|
| 174 |
+
eos_token_id=eos_token_id,
|
| 175 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 176 |
+
**kwargs,
|
| 177 |
+
)
|
| 178 |
+
try:
|
| 179 |
+
import flash_attn
|
| 180 |
+
self._attn_implementation = 'flash_attention_2'
|
| 181 |
+
except:
|
| 182 |
+
pass
|
| 183 |
+
|
| 184 |
+
def _rope_scaling_validation(self):
|
| 185 |
+
"""
|
| 186 |
+
Validate the `rope_scaling` configuration.
|
| 187 |
+
"""
|
| 188 |
+
if self.rope_scaling is None:
|
| 189 |
+
return
|
| 190 |
+
|
| 191 |
+
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
| 192 |
+
raise ValueError(
|
| 193 |
+
'`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, '
|
| 194 |
+
f'got {self.rope_scaling}'
|
| 195 |
+
)
|
| 196 |
+
rope_scaling_type = self.rope_scaling.get('type', None)
|
| 197 |
+
rope_scaling_factor = self.rope_scaling.get('factor', None)
|
| 198 |
+
if rope_scaling_type is None or rope_scaling_type not in ['linear', 'dynamic']:
|
| 199 |
+
raise ValueError(
|
| 200 |
+
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
| 201 |
+
)
|
| 202 |
+
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
| 203 |
+
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
|
generation_config.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 1,
|
| 4 |
+
"eos_token_id": 73440,
|
| 5 |
+
"pad_token_id": 73440,
|
| 6 |
+
"transformers_version": "4.57.6",
|
| 7 |
+
"use_cache": false
|
| 8 |
+
}
|
model-00001-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:be8a2c4aa0d048194fbbfb9d4136b33eb194646426bcc90eeeade8615729dac4
|
| 3 |
+
size 4867913600
|
model-00002-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bfcdd62e82645ce87924fa05819088fe1be94dd8a1419bc10b4a0f452bae0ae7
|
| 3 |
+
size 601686144
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,747 @@
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 746 |
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|
| 747 |
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|
modeling_minicpm.py
ADDED
|
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|
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recipe.yaml
ADDED
|
@@ -0,0 +1,38 @@
|
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|
| 1 |
+
default_stage:
|
| 2 |
+
default_modifiers:
|
| 3 |
+
AWQModifier:
|
| 4 |
+
config_groups:
|
| 5 |
+
group_0:
|
| 6 |
+
targets: [Linear]
|
| 7 |
+
weights:
|
| 8 |
+
num_bits: 4
|
| 9 |
+
type: int
|
| 10 |
+
symmetric: true
|
| 11 |
+
group_size: 32
|
| 12 |
+
strategy: group
|
| 13 |
+
block_structure: null
|
| 14 |
+
dynamic: false
|
| 15 |
+
actorder: null
|
| 16 |
+
scale_dtype: null
|
| 17 |
+
zp_dtype: null
|
| 18 |
+
observer: mse
|
| 19 |
+
observer_kwargs: {}
|
| 20 |
+
input_activations: null
|
| 21 |
+
output_activations: null
|
| 22 |
+
format: null
|
| 23 |
+
targets: [Linear]
|
| 24 |
+
ignore: [lm_head, model.embed_tokens, 're:.*input_layernorm$', 're:.*post_attention_layernorm$',
|
| 25 |
+
model.norm]
|
| 26 |
+
sequential_targets: [MiniCPMDecoderLayer]
|
| 27 |
+
mappings:
|
| 28 |
+
- smooth_layer: re:.*input_layernorm$
|
| 29 |
+
balance_layers: ['re:.*q_proj$', 're:.*k_proj$', 're:.*v_proj$']
|
| 30 |
+
- smooth_layer: re:.*v_proj$
|
| 31 |
+
balance_layers: ['re:.*o_proj$']
|
| 32 |
+
- smooth_layer: re:.*post_attention_layernorm$
|
| 33 |
+
balance_layers: ['re:.*gate_proj$', 're:.*up_proj$']
|
| 34 |
+
- smooth_layer: re:.*up_proj$
|
| 35 |
+
balance_layers: ['re:.*down_proj$']
|
| 36 |
+
offload_device: !!python/object/apply:torch.device [cpu]
|
| 37 |
+
duo_scaling: true
|
| 38 |
+
n_grid: 20
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,40 @@
|
|
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|
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|
|
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|
|
|
|
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|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|im_end|>",
|
| 4 |
+
"<|im_start|>",
|
| 5 |
+
"<|tool_call|>",
|
| 6 |
+
"<|execute_start|>",
|
| 7 |
+
"<|execute_end|>",
|
| 8 |
+
"<|fim_prefix|>",
|
| 9 |
+
"<|fim_middle|>",
|
| 10 |
+
"<|fim_suffix|>"
|
| 11 |
+
],
|
| 12 |
+
"bos_token": {
|
| 13 |
+
"content": "<s>",
|
| 14 |
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"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false
|
| 18 |
+
},
|
| 19 |
+
"eos_token": {
|
| 20 |
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"content": "<|im_end|>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false
|
| 25 |
+
},
|
| 26 |
+
"pad_token": {
|
| 27 |
+
"content": "<|im_end|>",
|
| 28 |
+
"lstrip": false,
|
| 29 |
+
"normalized": false,
|
| 30 |
+
"rstrip": false,
|
| 31 |
+
"single_word": false
|
| 32 |
+
},
|
| 33 |
+
"unk_token": {
|
| 34 |
+
"content": "<unk>",
|
| 35 |
+
"lstrip": false,
|
| 36 |
+
"normalized": false,
|
| 37 |
+
"rstrip": false,
|
| 38 |
+
"single_word": false
|
| 39 |
+
}
|
| 40 |
+
}
|
tokenizer.json
ADDED
|
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|
|
|
tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bb74d51116831c3bf65db812c553f94ab0c88dcf97a5bbb37e3504f6d359c530
|
| 3 |
+
size 1181204
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,119 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
|
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|
|
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|
|
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|
| 1 |
+
{
|
| 2 |
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"add_bos_token": true,
|
| 3 |
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"add_eos_token": false,
|
| 4 |
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"add_prefix_space": null,
|
| 5 |
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"added_tokens_decoder": {
|
| 6 |
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"0": {
|
| 7 |
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|
| 8 |
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|
| 9 |
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"normalized": false,
|
| 10 |
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|
| 11 |
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|
| 12 |
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"special": true
|
| 13 |
+
},
|
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"1": {
|
| 15 |
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|
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|
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|
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|
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|
| 20 |
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"special": true
|
| 21 |
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},
|
| 22 |
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"2": {
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| 23 |
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"content": "</s>",
|
| 24 |
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"lstrip": false,
|
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|
| 26 |
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"rstrip": false,
|
| 27 |
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|
| 28 |
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"special": true
|
| 29 |
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},
|
| 30 |
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"73440": {
|
| 31 |
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"content": "<|im_end|>",
|
| 32 |
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| 33 |
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"normalized": false,
|
| 34 |
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|
| 35 |
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|
| 36 |
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"special": true
|
| 37 |
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},
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| 38 |
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| 39 |
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| 40 |
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|
| 41 |
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|
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| 44 |
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|
| 45 |
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},
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| 46 |
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| 47 |
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| 48 |
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| 52 |
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|
| 53 |
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},
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| 54 |
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"73443": {
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| 55 |
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| 56 |
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| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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"special": true
|
| 61 |
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},
|
| 62 |
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"73444": {
|
| 63 |
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"content": "<|execute_end|>",
|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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"special": true
|
| 69 |
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},
|
| 70 |
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"73445": {
|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
+
"single_word": false,
|
| 76 |
+
"special": true
|
| 77 |
+
},
|
| 78 |
+
"73446": {
|
| 79 |
+
"content": "<|fim_middle|>",
|
| 80 |
+
"lstrip": false,
|
| 81 |
+
"normalized": false,
|
| 82 |
+
"rstrip": false,
|
| 83 |
+
"single_word": false,
|
| 84 |
+
"special": true
|
| 85 |
+
},
|
| 86 |
+
"73447": {
|
| 87 |
+
"content": "<|fim_suffix|>",
|
| 88 |
+
"lstrip": false,
|
| 89 |
+
"normalized": false,
|
| 90 |
+
"rstrip": false,
|
| 91 |
+
"single_word": false,
|
| 92 |
+
"special": true
|
| 93 |
+
}
|
| 94 |
+
},
|
| 95 |
+
"additional_special_tokens": [
|
| 96 |
+
"<|im_end|>",
|
| 97 |
+
"<|im_start|>",
|
| 98 |
+
"<|tool_call|>",
|
| 99 |
+
"<|execute_start|>",
|
| 100 |
+
"<|execute_end|>",
|
| 101 |
+
"<|fim_prefix|>",
|
| 102 |
+
"<|fim_middle|>",
|
| 103 |
+
"<|fim_suffix|>"
|
| 104 |
+
],
|
| 105 |
+
"bos_token": "<s>",
|
| 106 |
+
"clean_up_tokenization_spaces": false,
|
| 107 |
+
"eos_token": "<|im_end|>",
|
| 108 |
+
"extra_special_tokens": {},
|
| 109 |
+
"legacy": true,
|
| 110 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 111 |
+
"pad_token": "<|im_end|>",
|
| 112 |
+
"padding_side": "right",
|
| 113 |
+
"sp_model_kwargs": {},
|
| 114 |
+
"spaces_between_special_tokens": false,
|
| 115 |
+
"split_special_tokens": false,
|
| 116 |
+
"tokenizer_class": "LlamaTokenizer",
|
| 117 |
+
"unk_token": "<unk>",
|
| 118 |
+
"use_default_system_prompt": false
|
| 119 |
+
}
|