---
title: Mythos-nano
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/Mythos-nano"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/Mythos-nano.md"
repository: AI-ModelScope/Mythos-nano
last_updated: 2026-07-14
license: mit
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
base_model:
  - WeiboAI/VibeThinker-3B
base_model_relation: finetune
parameters: 3.1B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - safetensors
  - gguf
frameworks:
  - pytorch
language:
  - en
downloads: 146
stars: 0
tags:
  - reasoning
  - math
  - code
  - qwen2
  - mythos-nano
---

# Mythos-nano

> Mythos-nano - AI-ModelScope 在 ModelScope 开源的模型。Disclaimer: This is not an official release by Anthropic. Mythos-nano is an independent open model project.

AI-ModelScope/Mythos-nano 是 ModelScope 魔搭社区上的 3.1B 参数text-generation模型，采用 mit 许可，基于 WeiboAI/VibeThinker-3B 构建。

- **Repository**: AI-ModelScope/Mythos-nano
- **License**: mit
- **Tasks**: text-generation
- **Parameters**: 3.1B
- **Base model**: WeiboAI/VibeThinker-3B
- **Tags**: reasoning, math, code, qwen2, mythos-nano
- **Downloads**: 146
- **Stars**: 0
- **Last updated**: 2026-07-14

Source: https://www.modelscope.cn/models/AI-ModelScope/Mythos-nano

---

![Gemini_Generated_Image_jna8spjna8spjna8](https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/5CeYH1uT1Jkmx7LoBtoQF.png)

  </a>
  
> **Disclaimer:** This is **not** an official release by Anthropic.  
> Mythos-nano is an independent open model project.

# Mythos-nano

![Gemini_Generated_Image_1nl8n11nl8n11nl8](https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/Jbqo3kdC08nA4lt1Oli_z.png)

<blockquote style="border-left: 4px solid #ff6b6b; background-color: #fff5f5; padding: 10px 15px; margin: 10px 0; color: #cc3333;">
    <span style="font-weight: bold;">🚨 </span> This model was not trained on tool-calling or agent-based programming data. We therefore do not recommend using it for tasks that involve function calling, API orchestration, or autonomous coding agents.
For programming tasks, we recommend using this model on competitive programming problems (e.g., LeetCode-style) - Weibo Lab.
</blockquote>

<blockquote style="border-left: 4px solid #ff6b6b; background-color: #fff5f5; padding: 10px 15px; margin: 10px 0; color: #cc3333;">
    <span style="font-weight: bold;">⚠️ </span> Abliterated (uncensored): the refusal direction has been removed, so this model will not decline requests a safety-tuned model normally would. Safety guardrails are reduced — use responsibly and at your own risk; you are solely responsible for outputs and legal compliance.
</blockquote>

## 🏆 Benchmarks

![ChatGPT Image Jun 19, 2026 at 12_53_05 PM](https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/6i8nAQqTS1bZjwR9gSNIq.png)

### Full comparison (mathematics · coding · knowledge · instruction)

| Model | Params | AIME25 | AIME26 | HMMT25 | BruMO25 | IMO-Ans | LCBv6 | OJBench | GPQA-D | IFEval | IFBench |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Kimi K2.5 | 1T | 96.1 | 93.3 | 95.4 | 98.3 | 81.8 | 85.0 | 54.7 | 87.6 | 93.9 | 70.0 |
| GLM-5 | 744B | 96.7 | 95.8 | 97.9 | – | 82.5 | 85.5 | 55.0 | 86.0 | 92.6 | 76.5 |
| DeepSeek V3.2 | 671B | 93.1 | 94.2 | 90.2 | 96.7 | 78.3 | 80.8 | 48.4 | 82.4 | 92.6 | 60.7 |
| Gemini 3 Pro | N/A | 96.0 | 91.7 | 97.5 | 98.3 | 83.1 | 87.4 | 58.8 | 91.9 | – | 70.4 |
| Claude Opus 4.5 | N/A | 92.8 | 95.1 | 92.9 | – | 78.5 | 84.8 | – | 87.0 | – | 58.0 |
| GPT-5 (high) | N/A | 94.6 | – | 88.3 | 91.7 | 76.0 | 84.5 | – | 85.7 | – | 73.1 |
| **Mythos-nano** | **3B** | **91.4** | **94.3** | **89.3** | **93.8** | **76.4** | **80.2** | **38.6** | **70.2** | **93.4** | **74.5** |
| **Mythos-nano + CLR** | **3B** | **96.7** | **97.1** | **95.4** | **99.2** | **80.6** | – | – | **72.9** | – | – |

### LeetCode contests (Python, pass-rate)

| Model | Aggregate |
|---|---|
| GPT-5.3-Codex | 100.0% (128/128) |
| Gemini 3.1 Pro | 99.2% (127/128) |
| Gemini 3 Flash | 96.9% (124/128) |
| **Mythos-nano** | **96.1% (123/128)** |
| GPT-5.2 | 95.3% (122/128) |
| Qwen3-Max | 91.4% (117/128) |
| Kimi K2.5 | 90.6% (116/128) |
| Claude Opus 4.6 | 86.7% (111/128) |

A 3B model placing within ~4 points of trillion-parameter systems on competition math
and live code — the core thesis: with verifiable feedback, small models reach frontier
reasoning.

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("squ11z1/Mythos-nano")
model = AutoModelForCausalLM.from_pretrained("squ11z1/Mythos-nano", dtype=torch.bfloat16, device_map="cuda")
msgs = [{"role": "user", "content": "Find all integer solutions of x^2 - y^2 = 12."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")
print(tok.decode(model.generate(ids, max_new_tokens=2048, temperature=0.6)[0], skip_special_tokens=True))
```
Recommended sampling: temperature **0.6–1.0**, up to **40960** output tokens for hard problems.

## GGUF
`mythos-nano-f16.gguf` and `mythos-nano-Q4_K_M.gguf` are provided for llama.cpp / Ollama.

## License
MIT.