---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- reasoning
- math
- code
- qwen2
- mythos-nano
base_model:
- squ11z1/Mythos-nano
base_model_relation: finetune
library_name: transformers
---

> [!NOTE]
> I, (me), just removed the gguf's for those who dont want to download 40GB of model weighs, so now you have the safetensors now for easier use and fine tuning.

<br>

> **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("FlameF0X/Mythos-nano-safetensors-only")
model = AutoModelForCausalLM.from_pretrained("FlameF0X/Mythos-nano-safetensors-only", 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.

## License
MIT.