squ11z1
/

Hypnos-Colossus-1T

+---
+license: other
+license_name: modified-mit
+library_name: transformers
+---
+<div align="center">
+  <picture>
+      <img src="figures/kimi-logo.png" width="30%" alt="Kimi K2: Open Agentic Intellignece">
+  </picture>
+</div>
+<hr>
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+  <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>
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+  <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>
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+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/moonshotai/Kimi-K2-Thinking/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/thinking.html">Tech Blog</a></b>
+</p>
+## 1. Model Introduction
+Kimi K2 Thinking is the latest, most capable version of open-source thinking model. Starting with Kimi K2, we built it as a thinking agent that reasons step-by-step while dynamically invoking tools. It sets a new state-of-the-art on Humanity's Last Exam (HLE), BrowseComp, and other benchmarks by dramatically scaling multi-step reasoning depth and maintaining stable tool-use across 200–300 sequential calls. At the same time, K2 Thinking is a native INT4 quantization model with 256k context window, achieving lossless reductions in inference latency and GPU memory usage.
+### Key Features
+- **Deep Thinking & Tool Orchestration**: End-to-end trained to interleave chain-of-thought reasoning with function calls, enabling autonomous research, coding, and writing workflows that last hundreds of steps without drift.
+- **Native INT4 Quantization**: Quantization-Aware Training (QAT) is employed in post-training stage to achieve lossless 2x speed-up in low-latency mode.
+- **Stable Long-Horizon Agency**: Maintains coherent goal-directed behavior across up to 200–300 consecutive tool invocations, surpassing prior models that degrade after 30–50 steps.
+## 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** | 256K |
+| **Attention Mechanism** | MLA |
+| **Activation Function** | SwiGLU |
+</div>
+## 3. Evaluation Results
+**Reasoning Tasks**
+| Benchmark | Setting | K2 Thinking | GPT-5<br> (High) | Claude Sonnet 4.5<br> (Thinking) | K2 0905 | DeepSeek-V3.2 | Grok-4 |
+|:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:|:-------:|
+| **HLE (Text-only)** | no tools | 23.9 | 26.3 | 19.8* | 7.9 | 19.8 | 25.4 |
+| | w/ tools | 44.9 | 41.7* | 32.0* | 21.7 | 20.3* | 41.0 |
+| | heavy | 51.0 | 42.0 | - | - | - | 50.7 |
+| **AIME25** | no tools | 94.5 | 94.6 | 87.0 | 51.0 | 89.3 | 91.7 |
+| | w/ python | 99.1 | 99.6 | 100.0 | 75.2 | 58.1* | 98.8 |
+| | heavy | 100.0 | 100.0 | - | - | - | 100.0 |
+| **HMMT25** | no tools | 89.4 | 93.3 | 74.6* | 38.8 | 83.6 | 90.0 |
+| | w/ python | 95.1 | 96.7 | 88.8* | 70.4 | 49.5* | 93.9 |
+| | heavy | 97.5 | 100.0 | - | - | - | 96.7 |
+| **IMO-AnswerBench** | no tools | 78.6 | 76.0* | 65.9* | 45.8 | 76.0* | 73.1 |
+| **GPQA** | no tools | 84.5 | 85.7 | 83.4 | 74.2 | 79.9 | 87.5 |
+**General Tasks**
+| Benchmark | Setting | K2 Thinking | GPT-5<br> (High) | Claude Sonnet 4.5<br> (Thinking) | K2 0905 | DeepSeek-V3.2 |
+|:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:|
+| **MMLU-Pro** | no tools | 84.6 | 87.1 | 87.5 | 81.9 | 85.0 |
+| **MMLU-Redux** | no tools | 94.4 | 95.3 | 95.6 | 92.7 | 93.7 |
+| **Longform Writing** | no tools | 73.8 | 71.4 | 79.8 | 62.8 | 72.5 |
+| **HealthBench** | no tools | 58.0 | 67.2 | 44.2 | 43.8 | 46.9 |
+**Agentic Search Tasks**
+| Benchmark | Setting | K2 Thinking | GPT-5<br> (High) | Claude Sonnet 4.5<br> (Thinking) | K2 0905 | DeepSeek-V3.2 |
+|:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:|
+| **BrowseComp** | w/ tools | 60.2 | 54.9 | 24.1 | 7.4 | 40.1 |
+| **BrowseComp-ZH** | w/ tools | 62.3 | 63.0* | 42.4* | 22.2 | 47.9 |
+| **Seal-0** | w/ tools | 56.3 | 51.4* | 53.4* | 25.2 | 38.5* |
+| **FinSearchComp-T3** | w/ tools | 47.4 | 48.5* | 44.0* | 10.4 | 27.0* |
+| **Frames** | w/ tools | 87.0 | 86.0* | 85.0* | 58.1 | 80.2* |
+**Coding Tasks**
+| Benchmark | Setting | K2 Thinking | GPT-5<br> (High) | Claude Sonnet 4.5<br> (Thinking) | K2 0905 | DeepSeek-V3.2 |
+|:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:|
+| **SWE-bench Verified** | w/ tools | 71.3 | 74.9 | 77.2 | 69.2 | 67.8 |
+| **SWE-bench Multilingual** | w/ tools | 61.1 | 55.3* | 68.0 | 55.9 | 57.9 |
+| **Multi-SWE-bench** | w/ tools | 41.9 | 39.3* | 44.3 | 33.5 | 30.6 |
+| **SciCode** | no tools | 44.8 | 42.9 | 44.7 | 30.7 | 37.7 |
+| **LiveCodeBenchV6** | no tools | 83.1 | 87.0* | 64.0* | 56.1* | 74.1 |
+| **OJ-Bench (cpp)** | no tools | 48.7 | 56.2* | 30.4* | 25.5* | 38.2* |
+| **Terminal-Bench** | w/ simulated tools (JSON) | 47.1 | 43.8 | 51.0 | 44.5 | 37.7 |
+<details>
+<summary><b>Footnotes</b></summary>
+1. To ensure a fast, lightweight experience, we selectively employ a subset of tools and reduce the number of tool call steps under the chat mode on kimi.com. As a result, chatting on kimi.com may not reproduce our benchmark scores. Our agentic mode will be updated soon to reflect the full capabilities of K2 Thinking.
+2. **Testing Details**:
+ 2.1. All benchmarks were evaluated at temperature = 1.0 and 256 k context length for K2 Thinking, except for SciCode, for which we followed the official temperature setting of 0.0.
+ 2.2. HLE (no tools), AIME25, HMMT25, and GPQA were capped at a 96k thinking-token budget, while IMO-Answer Bench, LiveCodeBench and OJ-Bench were capped at a 128k thinking-token budget. Longform Writing was capped at a 32k completion-token budget.
+ 2.3. For AIME and HMMT (no tools), we report the average of 32 runs (avg@32). For AIME and HMMT (with Python), we report the average of 16 runs (avg@16). For IMO-AnswerBench, we report the average of 8 runs (avg@8).
+3. **Baselines**:
+ 3.1 GPT-5, Claude-4.5-sonnet, Grok-4 results and DeepSeek-V3.2 results are quoted from the [GPT-5 post](https://openai.com/index/introducing-gpt-5/), [GPT-5 for Developers post](https://openai.com/index/introducing-gpt-5-for-developers/), [GPT-5 system card](https://openai.com/index/gpt-5-system-card/), [claude-sonnet-4-5 post](https://www.anthropic.com/news/claude-sonnet-4-5), [grok-4 post](https://x.ai/news/grok-4), [deepseek-v3.2 post](https://api-docs.deepseek.com/news/news250929), the [public Terminal-Bench leaderboard](https://www.tbench.ai/leaderboard) (Terminus-2), the [public Vals AI leaderboard](https://vals.ai/) and [artificialanalysis](https://artificialanalysis.ai/). Benchmarks for which no available public scores were re-tested under the same conditions used for k2 thinking and are marked with an asterisk(*). For the GPT-5 test, we set the reasoning effort to high.
+ 3.2 The GPT-5 and Grok-4 on the HLE full set with tools are 35.2 and 38.6 from the official posts. In our internal evaluation on the HLE text-only subset, GPT-5 scores 41.7 and Grok-4 scores 38.6 (Grok-4’s launch cited 41.0 on the text-only subset). For GPT-5's HLE text-only w/o tool, we use score from <a href="https://scale.com/leaderboard/humanitys_last_exam_text_only" target="_blank">Scale.ai</a>. The official GPT5 HLE full set w/o tool is 24.8.
+ 3.3 For <a href="https://aclanthology.org/2025.emnlp-main.1794.pdf" target="_blank">IMO-AnswerBench</a>: GPT-5 scored 65.6 in the benchmark paper. We re-evaluated GPT-5 with official API and obtained a score of 76.
+4. **For HLE (w/ tools) and the agentic-search benchmarks**:
+ 4.1. K2 Thinking was equipped with search, code-interpreter, and web-browsing tools.
+ 4.2. BrowseComp-ZH, Seal-0 and FinSearchComp-T3 were run 4 times independently and the average is reported (avg@4).
+ 4.3. The evaluation used o3-mini as judge, configured identically to the official HLE setting; judge prompts were taken verbatim from the official repository.
+ 4.4. On HLE, the maximum step limit was 120, with a 48 k-token reasoning budget per step; on agentic-search tasks, the limit was 300 steps with a 24 k-token reasoning budget per step.
+ 4.5. When tool execution results cause the accumulated input to exceed the model's context limit (256k), we employ a simple context management strategy that hides all previous tool outputs.
+ 4.6. The web access to Hugging Face may lead to data leakage in certain benchmark tests, such as HLE. K2 Thinking can achieve a score of 51.3 on HLE without blocking Hugging Face. To ensure a fair and rigorous comparison, we blocked access to Hugging Face during testing.
+5. **For Coding Tasks**:
+ 5.1. Terminal-Bench scores were obtained with the default agent framework (Terminus-2) and the provided JSON parser.
+ 5.2. For other coding tasks, the 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.
+ 5.3. All reported scores of coding tasks are averaged over 5 independent runs.
+6. **Heavy Mode**: K2 Thinking Heavy Mode employs an efficient parallel strategy: it first rolls out eight trajectories simultaneously, then reflectively aggregates all outputs to generate the final result. Heavy mode for GPT-5 denotes the official GPT-5 Pro score.
+</details>
+## 4. Native INT4 Quantization
+Low-bit quantization is an effective way to reduce inference latency and GPU memory usage on large-scale inference servers. However, thinking models use excessive decoding lengths, and thus quantization often results in substantial performance drops.
+To overcome this challenge, we adopt Quantization-Aware Training (QAT) during the post-training phase, applying INT4 weight-only quantization to the MoE components. It allows K2 Thinking to support native INT4 inference with a roughly 2x generation speed improvement while achieving state-of-the-art performance. All benchmark results are reported under INT4 precision.
+The checkpoints are saved in compressed-tensors format, supported by most of mainstream inference engine. If you need the checkpoints in higher precision such as FP8 or BF16, you can refer to [official repo of compressed-tensors](https://github.com/vllm-project/compressed-tensors) to unpack the int4 weights and convert to any higher precision.
+## 5. Deployment
+> [!Note]
+> You can access K2 Thinking's API on https://platform.moonshot.ai , we provide OpenAI/Anthropic-compatible API for you.
+Currently, Kimi-K2-Thinking is recommended to run on the following inference engines:
+* vLLM
+* SGLang
+* KTransformers
+Deployment examples can be found in the [Model Deployment Guide](docs/deploy_guidance.md).
+---
+## 6. 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.OpenAI, model_name: str):
+    messages = [
+        {"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
+        {"role": "user", "content": [{"type": "text", "text": "which one is bigger, 9.11 or 9.9? think carefully."}]},
+    ]
+    response = client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        stream=False,
+        temperature=1.0,
+        max_tokens=4096
+    )
+    print(f"k2 answer: {response.choices[0].message.content}")
+    print("=====below is reasoning content======")
+    print(f"reasoning content: {response.choices[0].message.reasoning_content}")
+```
+> [!NOTE]
+> The recommended temperature for Kimi-K2-Thinking is `temperature = 1.0`.
+> If no special instructions are required, the system prompt above is a good default.
+---
+### Tool Calling
+Kimi-K2-Thinking has the same tool calling settings as Kimi-K2-Instruct.
+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=1.0,
+            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 more information, see the [Tool Calling Guide](docs/tool_call_guidance.md).
+---
+## 7. License
+Both the code repository and the model weights are released under the [Modified MIT License](LICENSE).
+---
+## 8. Third Party Notices
+See [THIRD PARTY NOTICES](THIRD_PARTY_NOTICES.md)
+---
+## 9. Contact Us
+If you have any questions, please reach out at [support@moonshot.cn](mailto:support@moonshot.cn).