Update README.md

README.md

@@ -8,7 +8,6 @@ library_name: transformers
       <img src="figures/kimi-logo.png" width="30%" alt="Kimi K2: Open Agentic Intellignece">
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@@ -22,7 +21,6 @@ library_name: transformers
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   <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>
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@@ -34,22 +32,13 @@ library_name: transformers
 
 ## 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.
+Kimi K2-Instruct-0905 is the latest, most capable version of Kimi K2. It is a state-of-the-art mixture-of-experts (MoE) language model, featuring 32 billion activated parameters and a total of 1 trillion parameters.
 
 ### 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.
+- Enhanced agentic coding intelligence: Kimi K2-Instruct-0905 demonstrates significant improvements in performance on public benchmarks and real-world coding agent tasks.
+- Improved frontend coding experience: Kimi K2-Instruct-0905 offers advancements in both the aesthetics and practicality of frontend programming.
+- Extended context length: Kimi K2-Instruct-0905’s context window has been increased from 128k to 256k tokens, providing better support for long-horizon tasks.
 
-### 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
 
@@ -77,582 +66,21 @@ Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 bi
 
 ## 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>
-
----
+| Benchmark              | Metric | K2-Instruct-0905 | K2-Instruct-0711 | Qwen3-Coder-480B-A35B-Instruct    | GLM-4.5    | DeepSeek-V3.1 | Claude-Sonnet-4 | Claude-Opus-4 |
+|------------------------|--------|------------------|------------------|--------|--------|--------|-----------------|---------------|
+| SWE-Bench verified     | ACC    | 69.2 ± 0.63      | 65.8             | 69.6*  | 64.2*  | 66.0*  | 72.7            | 72.5          |
+| SWE-Bench Multilingual | ACC    | 55.9 ± 0.72      | 47.3             | 54.7*  | 52.7   | 54.5*  | 53.3*           | -             |
+| Multi-SWE-Bench        | ACC    | 33.5 ± 0.28      | 31.3             | 32.7   | 31.7   | 29.0   | 35.7            | -             |
+| Terminal-Bench         | ACC    | 44.5 ± 2.03      | 37.5             | 37.5*  | 39.9*  | 31.3*  | 36.4*           | 43.2*         |
+| SWE-Dev                | ACC    | 66.6 ± 0.72      | 61.9             | 64.7   | 63.2   | 53.3   | 67.1            | -             |
 
-#### Base model evaluation results
 
-<div align="center">
+All K2-Instruct-0905 numbers are reported as mean ± std over five independent, full-test-set runs.
+Before each run we prune the repository so that every Git object unreachable from the target commit disappears; this guarantees the agent sees only the code that would legitimately be available at that point in history.
 
-<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.
+Except for Terminal-Bench (Terminus-2), every result was produced with our in-house evaluation harness. The harness is derived from SWE-agent, but we clamp the context windows of the Bash and Edit tools and rewrite the system prompt to match the task semantics. All baseline figures denoted with an asterisk (*) are excerpted directly from their official report or public leaderboard; the remaining metrics were evaluated by us under conditions identical to those used for K2-Instruct-0905.
 
-</sup>
+For SWE-Dev we go one step further: we overwrite the original repository files and delete any test file that exercises the functions the agent is expected to generate, eliminating any indirect hints about the desired implementation.
 
 
 ## 4. Deployment
@@ -713,7 +141,6 @@ The following example demonstrates calling a weather tool end-to-end:
 # Your tool implementation
 def get_weather(city: str) -> dict:
     return {"weather": "Sunny"}
-
 # Tool schema definition
 tools = [{
     "type": "function",
@@ -732,12 +159,10 @@ tools = [{
         }
     }
 }]
-
 # 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."},
@@ -762,7 +187,6 @@ def tool_call_with_client(client: OpenAI, model_name: str):
                 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,