Update README.md

README.md

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 ---
-license: other
-license_name: modified-mit
-library_name: transformers
+base_model:
+- moonshotai/Kimi-K2-Instruct
+pipeline_tag: text-generation
 ---
-<div align="center">
-  <picture>
-      <img src="figures/kimi-logo.png" width="30%" alt="Kimi K2: Open Agentic Intellignece">
-  </picture>
-</div>
 
-<hr>
+[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
 
-<div align="center" style="line-height:1">
-  <a href="https://www.kimi.com" target="_blank"><img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-Kimi%20K2-ff6b6b?color=1783ff&logoColor=white"/></a>
-  <a href="https://www.moonshot.ai" target="_blank"><img alt="Homepage" src="https://img.shields.io/badge/Homepage-Moonshot%20AI-white?logo=Kimi&logoColor=white"/></a>
-</div>
+'Make knowledge free for everyone'
 
-<div align="center" style="line-height: 1;">
-  <a href="https://huggingface.co/moonshotai" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Moonshot%20AI-ffc107?color=ffc107&logoColor=white"/></a>
-  <a href="https://twitter.com/kimi_moonshot" target="_blank"><img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-Kimi.ai-white?logo=x&logoColor=white"/></a>
-    <a href="https://discord.gg/TYU2fdJykW" target="_blank"><img alt="Discord" src="https://img.shields.io/badge/Discord-Kimi.ai-white?logo=discord&logoColor=white"/></a>
-</div>
-
-<div align="center" style="line-height: 1;">
-  <a href="https://github.com/moonshotai/Kimi-K2/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Modified_MIT-f5de53?&color=f5de53"/></a>
-</div>
-
-<p align="center">
-<b>📰&nbsp;&nbsp;<a href="https://moonshotai.github.io/Kimi-K2/">Tech Blog</a></b> &nbsp;&nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; <b>📄&nbsp;&nbsp;Paper Link (comming soon)</b>
-</p>
-
-## 1. Model Introduction
-
-Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities.
-
-### Key Features
-- Large-Scale Training: Pre-trained a 1T parameter MoE model on 15.5T tokens with zero training instability.
-- MuonClip Optimizer: We apply the Muon optimizer to an unprecedented scale, and develop novel optimization techniques to resolve instabilities while scaling up.
-- Agentic Intelligence: Specifically designed for tool use, reasoning, and autonomous problem-solving.
-
-### Model Variants
-- **Kimi-K2-Base**: The foundation model, a strong start for researchers and builders who want full control for fine-tuning and custom solutions.
-- **Kimi-K2-Instruct**: The post-trained model best for drop-in, general-purpose chat and agentic experiences. It is a reflex-grade model without long thinking.
-
-<div align="center">
-  <picture>
-      <img src="figures/banner.png" width="80%" alt="Evaluation Results">
-  </picture>
-</div>
-
-## 2. Model Summary
-
-<div align="center">
-
-
-| | |
-|:---:|:---:|
-| **Architecture** | Mixture-of-Experts (MoE) |
-| **Total Parameters** | 1T |
-| **Activated Parameters** | 32B |
-| **Number of Layers** (Dense layer included) | 61 |
-| **Number of Dense Layers** | 1 |
-| **Attention Hidden Dimension** | 7168 |
-| **MoE Hidden Dimension** (per Expert) | 2048 |
-| **Number of Attention Heads** | 64 |
-| **Number of Experts** | 384 |
-| **Selected Experts per Token** | 8 |
-| **Number of Shared Experts** | 1 |
-| **Vocabulary Size** | 160K |
-| **Context Length** | 128K |
-| **Attention Mechanism** | MLA |
-| **Activation Function** | SwiGLU |
-</div>
-
-## 3. Evaluation Results
-
-#### Instruction model evaluation results
-
-<div align="center">
-<table>
-<thead>
-<tr>
-<th align="center">Benchmark</th>
-<th align="center">Metric</th>
-<th align="center"><sup>Kimi K2 Instruct</sup></th>
-<th align="center"><sup>DeepSeek-V3-0324</sup></th>
-<th align="center"><sup>Qwen3-235B-A22B <br><sup>(non-thinking)</sup></sup></th>
-<th align="center"><sup>Claude Sonnet 4 <br><sup>(w/o extended thinking)</sup></sup></th>
-<th align="center"><sup>Claude Opus 4 <br><sup>(w/o extended thinking)</sup></sup></th>
-<th align="center"><sup>GPT-4.1</sup></th>
-<th align="center"><sup>Gemini 2.5 Flash <br> Preview (05-20)</sup></th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center" colspan=9><strong>Coding Tasks</strong></td>
-</tr>
-<tr>
-<td align="center">LiveCodeBench v6<br><sup>(Aug 24 - May 25)</sup></td>
-<td align="center">Pass@1</td>
-<td align="center"><strong>53.7</strong></td>
-<td align="center">46.9</td>
-<td align="center">37.0</td>
-<td align="center">48.5</td>
-<td align="center">47.4</td>
-<td align="center">44.7</td>
-<td align="center">44.7</td>
-</tr>
-<tr>
-<td align="center">OJBench</td>
-<td align="center">Pass@1</td>
-<td align="center"><strong>27.1</strong></td>
-<td align="center">24.0</td>
-<td align="center">11.3</td>
-<td align="center">15.3</td>
-<td align="center">19.6</td>
-<td align="center">19.5</td>
-<td align="center">19.5</td>
-</tr>
-
-<tr>
-<td align="center">MultiPL-E</td>
-<td align="center">Pass@1</td>
-<td align="center"><ins><strong>85.7</strong></ins></td>
-<td align="center">83.1</td>
-<td align="center">78.2</td>
-<td align="center">88.6</td>
-<td align="center"><strong>89.6</strong></td>
-<td align="center">86.7</td>
-<td align="center">85.6</td>
-</tr>
-
-<tr>
-<td align="center">SWE-bench Verified <br/><sup>(Agentless Coding)</sup></td>
-<td align="center">Single Patch w/o Test (Acc)</td>
-<td align="center"><ins><strong>51.8</strong></ins></td>
-<td align="center">36.6</td>
-<td align="center">39.4</td>
-<td align="center">50.2</td>
-<td align="center"><strong>53.0</strong></td>
-<td align="center">40.8</td>
-<td align="center">32.6</td>
-</tr>
-
-<tr>
-<td align="center" rowspan="2">SWE-bench Verified <br/> <sup>(Agentic Coding)</sup></td>
-<td align="center">Single Attempt (Acc)</td>
-<td align="center"><ins><strong>65.8</strong></ins></td>
-<td align="center">38.8</td>
-<td align="center">34.4</td>
-<td align="center"><strong>72.7</strong><sup>*</sup></td>
-<td align="center">72.5<sup>*</sup></td>
-<td align="center">54.6</td>
-<td align="center">—</td>
-</tr>
-
-<tr>
-<!--<td align="center">(Agentic Coding)</td>-->
-<td align="center">Multiple Attempts (Acc)</td>
-<td align="center"><ins><strong>71.6</strong></ins></td>
-<td align="center">—</td>
-<td align="center">—</td>
-<td align="center"><strong>80.2</strong></td>
-<td align="center">79.4<sup>*</sup></td>
-<td align="center">—</td>
-<td align="center">—</td>
-</tr>
-
-<tr>
-<td align="center">SWE-bench Multilingual<br /> <sup>(Agentic Coding)</sup></td>
-<td align="center">Single Attempt (Acc)</td>
-<td align="center"><ins><strong>47.3</strong> </ins></td>
-<td align="center">25.8</td>
-<td align="center">20.9</td>
-<td align="center"><strong>51.0</strong></td>
-<td align="center">—</td>
-<td align="center">31.5</td>
-<td align="center">—</td>
-</tr>
-
-<tr>
-<td align="center" rowspan="2">TerminalBench</td>
-<td align="center">Inhouse Framework (Acc)</td>
-<td align="center"><ins><strong>30.0</strong></ins></td>
-<td align="center">—</td>
-<td align="center">—</td>
-<td align="center">35.5</td>
-<td align="center"><strong>43.2</strong></td>
-<td align="center">8.3</td>
-<td align="center">—</td>
-</tr>
-
-<tr>
-<!--<td align="center">TerminalBench</td>-->
-<td align="center">Terminus (Acc)</td>
-<td align="center"><ins><strong>25.0</strong> </ins></td>
-<td align="center">16.3</td>
-<td align="center">6.6</td>
-<td align="center">—</td>
-<td align="center">—</td>
-<td align="center"><strong>30.3</strong></td>
-<td align="center">16.8</td>
-</tr>
-<tr>
-<td align="center">Aider-Polyglot</td>
-<td align="center">Acc</td>
-<td align="center">60.0</td>
-<td align="center">55.1</td>
-<td align="center"><ins><strong>61.8</strong></ins></td>
-<td align="center">56.4</td>
-<td align="center"><strong>70.7</strong></td>
-<td align="center">52.4</td>
-<td align="center">44.0</td>
-</tr>
-<tr>
-<td align="center" colspan=9><strong>Tool Use Tasks</strong></td>
-</tr>
-<tr>
-<td align="center">Tau2 retail</td>
-<td align="center">Avg@4</td>
-<td align="center"><ins><strong>70.6</strong></ins></td>
-<td align="center">69.1</td>
-<td align="center">57.0</td>
-<td align="center">75.0</td>
-<td align="center"><strong>81.8</strong></td>
-<td align="center">74.8</td>
-<td align="center">64.3</td>
-</tr>
-<tr>
-<td align="center">Tau2 airline</td>
-<td align="center">Avg@4</td>
-<td align="center"><ins><strong>56.5</strong></ins></td>
-<td align="center">39.0</td>
-<td align="center">26.5</td>
-<td align="center">55.5</td>
-<td align="center"><strong>60.0</strong></td>
-<td align="center">54.5</td>
-<td align="center">42.5</td>
-</tr>
-<tr>
-<td align="center">Tau2 telecom</td>
-<td align="center">Avg@4</td>
-<td align="center"><strong>65.8</strong></td>
-<td align="center">32.5</td>
-<td align="center">22.1</td>
-<td align="center">45.2</td>
-<td align="center">57.0</td>
-<td align="center">38.6</td>
-<td align="center">16.9</td>
-</tr>
-<tr>
-<td align="center">AceBench</td>
-<td align="center">Acc</td>
-<td align="center"><ins><strong>76.5</strong></ins></td>
-<td align="center">72.7</td>
-<td align="center">70.5</td>
-<td align="center">76.2</td>
-<td align="center">75.6</td>
-<td align="center"><strong>80.1</strong></td>
-<td align="center">74.5</td>
-</tr>
-<tr>
-<td align="center" colspan=9><strong>Math &amp; STEM Tasks</strong></td>
-</tr>
-<tr>
-<td align="center">AIME 2024</td>
-<td align="center">Avg@64</td>
-<td align="center"><strong>69.6</strong></td>
-<td align="center">59.4<sup>*</sup></td>
-<td align="center">40.1<sup>*</sup></td>
-<td align="center">43.4</td>
-<td align="center">48.2</td>
-<td align="center">46.5</td>
-<td align="center">61.3</td>
-</tr>
-<tr>
-<td align="center">AIME 2025</td>
-<td align="center">Avg@64</td>
-<td align="center"><strong>49.5</strong></td>
-<td align="center">46.7</td>
-<td align="center">24.7<sup>*</sup></td>
-<td align="center">33.1<sup>*</sup></td>
-<td align="center">33.9<sup>*</sup></td>
-<td align="center">37.0</td>
-<td align="center">46.6</td>
-</tr>
-<tr>
-<td align="center">MATH-500</td>
-<td align="center">Acc</td>
-<td align="center"><strong>97.4</strong></td>
-<td align="center">94.0<sup>*</sup></td>
-<td align="center">91.2<sup>*</sup></td>
-<td align="center">94.0</td>
-<td align="center">94.4</td>
-<td align="center">92.4</td>
-<td align="center">95.4</td>
-</tr>
-<tr>
-<td align="center">HMMT 2025</td>
-<td align="center">Avg@32</td>
-<td align="center"><strong>38.8</strong></td>
-<td align="center">27.5</td>
-<td align="center">11.9</td>
-<td align="center">15.9</td>
-<td align="center">15.9</td>
-<td align="center">19.4</td>
-<td align="center">34.7</td>
-</tr>
-<tr>
-<td align="center">CNMO 2024</td>
-<td align="center">Avg@16</td>
-<td align="center">74.3</td>
-<td align="center"><ins><strong>74.7</strong></ins></td>
-<td align="center">48.6</td>
-<td align="center">60.4</td>
-<td align="center">57.6</td>
-<td align="center">56.6</td>
-<td align="center"><strong>75.0</strong></td>
-</tr>
-<tr>
-<td align="center">PolyMath-en</td>
-<td align="center">Avg@4</td>
-<td align="center"><strong>65.1</strong></td>
-<td align="center">59.5</td>
-<td align="center">51.9</td>
-<td align="center">52.8</td>
-<td align="center">49.8</td>
-<td align="center">54.0</td>
-<td align="center">49.9</td>
-</tr>
-
-<tr>
-<td align="center">ZebraLogic</td>
-<td align="center">Acc</td>
-<td align="center"><strong>89.0</strong></td>
-<td align="center">84.0</td>
-<td align="center">37.7<sup>*</sup></td>
-<td align="center">73.7</td>
-<td align="center">59.3</td>
-<td align="center">58.5</td>
-<td align="center">57.9</td>
-</tr>
-
-<tr>
-<td align="center">AutoLogi</td>
-<td align="center">Acc</td>
-<td align="center"><ins><strong>89.5</strong></ins></td>
-<td align="center">88.9</td>
-<td align="center">83.3</td>
-<td align="center"><strong>89.8</strong></td>
-<td align="center">86.1</td>
-<td align="center">88.2</td>
-<td align="center">84.1</td>
-</tr>
-
-<tr>
-<td align="center">GPQA-Diamond</td>
-<td align="center">Avg@8</td>
-<td align="center"><strong>75.1</strong></td>
-<td align="center">68.4<sup>*</sup></td>
-<td align="center">62.9<sup>*</sup></td>
-<td align="center">70.0<sup>*</sup></td>
-<td align="center">74.9<sup>*</sup></td>
-<td align="center">66.3</td>
-<td align="center">68.2</td>
-</tr>
-
-<tr>
-<td align="center">SuperGPQA</td>
-<td align="center">Acc</td>
-<td align="center"><strong>57.2</strong></td>
-<td align="center">53.7</td>
-<td align="center">50.2</td>
-<td align="center">55.7</td>
-<td align="center">56.5</td>
-<td align="center">50.8</td>
-<td align="center">49.6</td>
-</tr>
-
-<tr>
-<td align="center">Humanity's Last Exam<br><sup>(Text Only)</sup></td>
-<td align="center">-</td>
-<td align="center">4.7</td>
-<td align="center">5.2</td>
-<td align="center"><ins><strong>5.7</strong></ins></td>
-<td align="center">5.8</td>
-<td align="center"><strong>7.1</strong></td>
-<td align="center">3.7</td>
-<td align="center">5.6</td>
-</tr>
-
-<tr>
-<td align="center" colspan=9><strong>General Tasks</strong></td>
-</tr>
-
-<tr>
-<td align="center">MMLU</td>
-<td align="center">EM</td>
-<td align="center"><ins><strong>89.5</strong></ins></td>
-<td align="center">89.4</td>
-<td align="center">87.0</td>
-<td align="center">91.5</td>
-<td align="center"><strong>92.9</strong></td>
-<td align="center">90.4</td>
-<td align="center">90.1</td>
-</tr>
-
-<tr>
-<td align="center">MMLU-Redux</td>
-<td align="center">EM</td>
-<td align="center"><ins><strong>92.7</strong></ins></td>
-<td align="center">90.5</td>
-<td align="center">89.2</td>
-<td align="center">93.6</td>
-<td align="center"><strong>94.2</strong></td>
-<td align="center">92.4</td>
-<td align="center">90.6</td>
-</tr>
-
-<tr>
-<td align="center">MMLU-Pro</td>
-<td align="center">EM</td>
-<td align="center">81.1</td>
-<td align="center"><ins><strong>81.2</strong></ins><sup>*</sup></td>
-<td align="center">77.3</td>
-<td align="center">83.7</td>
-<td align="center"><strong>86.6</strong></td>
-<td align="center">81.8</td>
-<td align="center">79.4</td>
-</tr>
-
-<tr>
-<td align="center">IFEval</td>
-<td align="center">Prompt Strict</td>
-<td align="center"><strong>89.8</strong></td>
-<td align="center">81.1</td>
-<td align="center">83.2<sup>*</sup></td>
-<td align="center">87.6</td>
-<td align="center">87.4</td>
-<td align="center">88.0</td>
-<td align="center">84.3</td>
-</tr>
-
-<tr>
-<td align="center">Multi-Challenge</td>
-<td align="center">Acc</td>
-<td align="center"><strong>54.1</strong></td>
-<td align="center">31.4</td>
-<td align="center">34.0</td>
-<td align="center">46.8</td>
-<td align="center">49.0</td>
-<td align="center">36.4</td>
-<td align="center">39.5</td>
-</tr>
-
-<tr>
-<td align="center">SimpleQA</td>
-<td align="center">Correct</td>
-<td align="center"><ins><strong>31.0</strong></ins></td>
-<td align="center">27.7</td>
-<td align="center">13.2</td>
-<td align="center">15.9</td>
-<td align="center">22.8</td>
-<td align="center"><strong>42.3</strong></td>
-<td align="center">23.3</td>
-</tr>
-
-<tr>
-<td align="center">Livebench</td>
-<td align="center">Pass@1</td>
-<td align="center"><strong>76.4</strong></td>
-<td align="center">72.4</td>
-<td align="center">67.6</td>
-<td align="center">74.8</td>
-<td align="center">74.6</td>
-<td align="center">69.8</td>
-<td align="center">67.8</td>
-</tr>
-</tbody>
-</table>
-</div>
-<sup>
-• Bold denotes global SOTA, and underlined denotes open-source SOTA.
-</sup><br/><sup>
-• Data points marked with * are taken directly from the model's tech report or blog.
-</sup><br/><sup>
-• All metrics, except for SWE-bench Verified (Agentless), are evaluated with an 8k output token length. SWE-bench Verified (Agentless) is limited to a 16k output token length.
-</sup><br/><sup>
-• Kimi K2 achieves 65.8% pass@1 on the SWE-bench Verified tests with bash/editor tools (single-attempt patches, no test-time compute). It also achieves a 47.3% pass@1 on the SWE-bench Multilingual tests under the same conditions. Additionally, we report results on SWE-bench Verified tests (71.6%) that leverage parallel test-time compute by sampling multiple sequences and selecting the single best via an internal scoring model.
-</sup><br/><sup>
-• To ensure the stability of the evaluation, we employed avg@k on the AIME, HMMT, CNMO, PolyMath-en, GPQA-Diamond, EvalPlus, Tau2.
-</sup><br/><sup>
-• Some data points have been omitted due to prohibitively expensive evaluation costs.
-    </sup>
-
----
-
-#### Base model evaluation results
-
-<div align="center">
-
-<table>
-<thead>
-<tr>
-<th align="center">Benchmark</th>
-<th align="center">Metric</th>
-<th align="center">Shot</th>
-<th align="center">Kimi K2 Base</th>
-<th align="center">Deepseek-V3-Base</th>
-<th align="center">Qwen2.5-72B</th>
-<th align="center">Llama 4 Maverick</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center" colspan="7"><strong>General Tasks</strong></td>
-</tr>
-<tr>
-<td align="center">MMLU</td>
-<td align="center">EM</td>
-<td align="center">5-shot</td>
-<td align="center"><strong>87.8</strong></td>
-<td align="center">87.1</td>
-<td align="center">86.1</td>
-<td align="center">84.9</td>
-</tr>
-<tr>
-<td align="center">MMLU-pro</td>
-<td align="center">EM</td>
-<td align="center">5-shot</td>
-<td align="center"><strong>69.2</strong></td>
-<td align="center">60.6</td>
-<td align="center">62.8</td>
-<td align="center">63.5</td>
-</tr>
-<tr>
-<td align="center">MMLU-redux-2.0</td>
-<td align="center">EM</td>
-<td align="center">5-shot</td>
-<td align="center"><strong>90.2</strong></td>
-<td align="center">89.5</td>
-<td align="center">87.8</td>
-<td align="center">88.2</td>
-</tr>
-<tr>
-<td align="center">SimpleQA</td>
-<td align="center">Correct</td>
-<td align="center">5-shot</td>
-<td align="center"><strong>35.3</strong></td>
-<td align="center">26.5</td>
-<td align="center">10.3</td>
-<td align="center">23.7</td>
-</tr>
-<tr>
-<td align="center">TriviaQA</td>
-<td align="center">EM</td>
-<td align="center">5-shot</td>
-<td align="center"><strong>85.1</strong></td>
-<td align="center">84.1</td>
-<td align="center">76.0</td>
-<td align="center">79.3</td>
-</tr>
-<tr>
-<td align="center">GPQA-Diamond</td>
-<td align="center">Avg@8</td>
-<td align="center">5-shot</td>
-<td align="center">48.1</td>
-<td align="center"><strong>50.5</strong></td>
-<td align="center">40.8</td>
-<td align="center">49.4</td>
-</tr>
-<tr>
-<td align="center">SuperGPQA</td>
-<td align="center">EM</td>
-<td align="center">5-shot</td>
-<td align="center"><strong>44.7</strong></td>
-<td align="center">39.2</td>
-<td align="center">34.2</td>
-<td align="center">38.8</td>
-</tr>
-<tr>
-<td align="center" colspan="7"><strong>Coding Tasks</strong></td>
-</tr>
-<tr>
-<td align="center">LiveCodeBench v6</td>
-<td align="center">Pass@1</td>
-<td align="center">1-shot</td>
-<td align="center"><strong>26.3</strong></td>
-<td align="center">22.9</td>
-<td align="center">21.1</td>
-<td align="center">25.1</td>
-</tr>
-<tr>
-<td align="center">EvalPlus</td>
-<td align="center">Pass@1</td>
-<td align="center">-</td>
-<td align="center"><strong>80.3</strong></td>
-<td align="center">65.6</td>
-<td align="center">66.0</td>
-<td align="center">65.5</td>
-</tr>
-<tr>
-<td align="center" colspan="7"><strong>Mathematics Tasks</strong></td>
-</tr>
-<tr>
-<td align="center">MATH</td>
-<td align="center">EM</td>
-<td align="center">4-shot</td>
-<td align="center"><strong>70.2</strong></td>
-<td align="center">60.1</td>
-<td align="center">61.0</td>
-<td align="center">63.0</td>
-</tr>
-<tr>
-<td align="center">GSM8k</td>
-<td align="center">EM</td>
-<td align="center">8-shot</td>
-<td align="center"><strong>92.1</strong></td>
-<td align="center">91.7</td>
-<td align="center">90.4</td>
-<td align="center">86.3</td>
-</tr>
-<tr>
-<td align="center" colspan="7"><strong>Chinese Tasks</strong></td>
-</tr>
-<tr>
-<td align="center">C-Eval</td>
-<td align="center">EM</td>
-<td align="center">5-shot</td>
-<td align="center"><strong>92.5</strong></td>
-<td align="center">90.0</td>
-<td align="center">90.9</td>
-<td align="center">80.9</td>
-</tr>
-<tr>
-<td align="center">CSimpleQA</td>
-<td align="center">Correct</td>
-<td align="center">5-shot</td>
-<td align="center"><strong>77.6</strong></td>
-<td align="center">72.1</td>
-<td align="center">50.5</td>
-<td align="center">53.5</td>
-</tr>
-</tbody>
-</table>
-</div>
-<sup>
-• We only evaluate open-source pretrained models in this work. We report results for Qwen2.5-72B because the base checkpoint for Qwen3-235B-A22B was not open-sourced at the time of our study.
-</sup><br/><sup>
-• All models are evaluated using the same evaluation protocol.
-
-</sup>
-
-
-## 4. Deployment
-> [!Note]
-> You can access Kimi K2's API on https://platform.moonshot.ai , we provide OpenAI/Anthropic-compatible API for you.
->
-> The Anthropic-compatible API maps temperature by `real_temperature = request_temperature * 0.6` for better compatible with existing applications.
-
-Our model checkpoints are stored in the block-fp8 format, you can find it on [Huggingface](https://huggingface.co/moonshotai/Kimi-K2-Instruct).
-
-Currently, Kimi-K2 is recommended to run on the following inference engines:
-
-* vLLM
-* SGLang
-* KTransformers
-* TensorRT-LLM
-
-Deployment examples for vLLM and SGLang can be found in the [Model Deployment Guide](docs/deploy_guidance.md).
-
----
-
-## 5. Model Usage
-
-### Chat Completion
-
-Once the local inference service is up, you can interact with it through the chat endpoint:
-
-```python
-def simple_chat(client: OpenAI, model_name: str):
-    messages = [
-        {"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
-        {"role": "user", "content": [{"type": "text", "text": "Please give a brief self-introduction."}]},
-    ]
-    response = client.chat.completions.create(
-        model=model_name,
-        messages=messages,
-        stream=False,
-        temperature=0.6,
-        max_tokens=256
-    )
-    print(response.choices[0].message.content)
-```
-
-> [!NOTE]
-> The recommended temperature for Kimi-K2-Instruct is `temperature = 0.6`.
-> If no special instructions are required, the system prompt above is a good default.
-
----
-
-### Tool Calling
-
-Kimi-K2-Instruct has strong tool-calling capabilities.
-To enable them, you need to pass the list of available tools in each request, then the model will autonomously decide when and how to invoke them.
-
-The following example demonstrates calling a weather tool end-to-end:
-
-```python
-# Your tool implementation
-def get_weather(city: str) -> dict:
-    return {"weather": "Sunny"}
-
-# Tool schema definition
-tools = [{
-    "type": "function",
-    "function": {
-        "name": "get_weather",
-        "description": "Retrieve current weather information. Call this when the user asks about the weather.",
-        "parameters": {
-            "type": "object",
-            "required": ["city"],
-            "properties": {
-                "city": {
-                    "type": "string",
-                    "description": "Name of the city"
-                }
-            }
-        }
-    }
-}]
-
-# Map tool names to their implementations
-tool_map = {
-    "get_weather": get_weather
-}
-
-def tool_call_with_client(client: OpenAI, model_name: str):
-    messages = [
-        {"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
-        {"role": "user", "content": "What's the weather like in Beijing today? Use the tool to check."}
-    ]
-    finish_reason = None
-    while finish_reason is None or finish_reason == "tool_calls":
-        completion = client.chat.completions.create(
-            model=model_name,
-            messages=messages,
-            temperature=0.6,
-            tools=tools,          # tool list defined above
-            tool_choice="auto"
-        )
-        choice = completion.choices[0]
-        finish_reason = choice.finish_reason
-        if finish_reason == "tool_calls":
-            messages.append(choice.message)
-            for tool_call in choice.message.tool_calls:
-                tool_call_name = tool_call.function.name
-                tool_call_arguments = json.loads(tool_call.function.arguments)
-                tool_function = tool_map[tool_call_name]
-                tool_result = tool_function(**tool_call_arguments)
-                print("tool_result:", tool_result)
-
-                messages.append({
-                    "role": "tool",
-                    "tool_call_id": tool_call.id,
-                    "name": tool_call_name,
-                    "content": json.dumps(tool_result)
-                })
-    print("-" * 100)
-    print(choice.message.content)
-```
-
-The `tool_call_with_client` function implements the pipeline from user query to tool execution.
-This pipeline requires the inference engine to support Kimi-K2’s native tool-parsing logic.
-For streaming output and manual tool-parsing, see the [Tool Calling Guide](docs/tool_call_guidance.md).
-
----
-
-## 6. License
-
-Both the code repository and the model weights are released under the [Modified MIT License](LICENSE).
-
----
-
-## 7. Contact Us
-
-If you have any questions, please reach out at [[email protected]](mailto:[email protected]).
+BF16 version of: [moonshotai/Kimi-K2-Instruct](https://huggingface.co/moonshotai/Kimi-K2-Instruct)
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