squ11z1
/

Hypnos-Colossus-1T

@@ -2,46 +2,39 @@
 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>
-<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>
-<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://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">
 | | |
@@ -61,9 +54,6 @@ Kimi K2 Thinking is the latest, most capable version of open-source thinking mod
 | **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 |
@@ -80,202 +70,60 @@ Kimi K2 Thinking is the latest, most capable version of open-source thinking mod
 | **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).

 license: other
 license_name: modified-mit
 library_name: transformers
+base_model:
+- moonshotai/Kimi-K2-Thinking
+pipeline_tag: text-generation
+tags:
+- quantum
+- reasoning
+- physics
+- entropy-injection
 ---
+# Hypnos-Colossus 1T (Quantum-Informed Reasoning)
+<div align="center">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/aW_lb399B5CFxxDlMhjSZ.jpeg" width="70%" alt="Hypnos Colossus Header">
 </div>
+<div align="center">
+    The Largest Quantum-Regularized Model in Existence.
 </div>
+**🪐 Overview**
+**Hypnos-Colossus 1T** is a massive-scale reasoning engine derived from the [Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking) architecture. It represents a radical experiment in Post-Training Weight Perturbation.
+Instead of standard fine-tuning, we applied a Quantum Scale Injection protocol using real entropy data derived from three sources:
+1. IBM Quantum Processors (Superconducting Qubit Decoherence).
+2. IQM Quantum Processor (Superconducting Transmon Qubits with star topology).
+3. Cosmic Microwave Background (CMB) data from the Planck satellite.
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/lYduUOLOljHUxF6iPvjzs.jpeg" width="60%" alt="Cosmic_Microwave_Background_(CMB)">
+This process introduces a unique, non-deterministic "fingerprint" into the model's scaling tensors, aimed at breaking local minima overfitting and enforcing stricter logical adherence during inference.
+📊 **Kimi-K2's Thinkings Model Summary & Reasoning Benchmarks**
 | | |
 | **Context Length** | 256K |
 | **Attention Mechanism** | MLA |
 | **Activation Function** | SwiGLU |
 **Reasoning Tasks**
 | Benchmark | Setting | K2 Thinking | GPT-5<br> (High) | Claude Sonnet 4.5<br> (Thinking) | K2 0905 | DeepSeek-V3.2 | Grok-4 |
 | **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 |
+</div>
+**Quantum Augmentation Specs**
+Entropy Sources: IBM Quantum ibm_fez + IQM Sirius + Planck CMB Data
+Injection Target: Scaling Tensors (Scales/Norms) via Direct Perturbation ($\epsilon=1e^{-5}$)
+Format: Native INT4/FP8 Compressed
+<img src="https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/ii-jSyWx3KAXAi1j3ifVs.jpeg" width="70%" alt="qub">
+**🔬 The "Quantum Injection" Hypothesis**
+Standard quantization (INT4) often locks massive models into rigid behavioral patterns.
+By injecting high-quality quantum noise into the scales and norms of the model, we theoretically increase the model's epistemic uncertainty without degrading its knowledge base.
+This forces the inference path to rely less on "memorized" token sequences and more on robust semantic links.
+Source Data Integrity:
+The noise injection was seeded using a cryptographically secure hash of the Planck CMB radiation map combined with raw qubit readouts from IBM's ibm_fez & IQM Sirius backends.
+## 🧬 The Hypnos Family
+| Model | Parameters | Quantum Sources | Best For | Status |
+|-------|------------|-----------------|----------|--------|
+| **Hypnos-Colossus-1T** | **1T (MoE)** | **3 (IBM + IQM + Cosmic)** | **Deep Simulation, Grand Challenges** | 🌌 **Flagship** |
+| **Hypnos-i2-32B** | 32B | 3 (Matter + Light + Nucleus) | Production, Research | ✅ Stable |
+| **Hypnos-i1-8B** | 8B | 1 (Matter only) | Edge, Experiments | ✅ 10k+ Downloads |
+**Which one to choose?**
+* **Colossus 1T:** For when you need maximum reasoning depth.
+* **i2-32B:** The "Giant Killer" - best balance of logic and efficiency for consumer GPUs.
+* **i1-8B:** Perfect for laptops and rapid prototyping.
+**🚀 How to Run**
+Inference with Transformers
 ```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model_id = "squ11z1/Hypnos-Colossus-1T"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+prompt = "Analyze the implications of quantum entropy on AI reasoning:"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+output = model.generate(**inputs, max_new_tokens=512, temperature=0.6)
+print(tokenizer.decode(output[0]))
 ```