Instructions to use squ11z1/Mythos-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use squ11z1/Mythos-nano with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="squ11z1/Mythos-nano",
	filename="mythos-nano-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use squ11z1/Mythos-nano with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf squ11z1/Mythos-nano:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf squ11z1/Mythos-nano:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf squ11z1/Mythos-nano:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf squ11z1/Mythos-nano:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf squ11z1/Mythos-nano:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf squ11z1/Mythos-nano:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf squ11z1/Mythos-nano:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf squ11z1/Mythos-nano:Q4_K_M

Use Docker

docker model run hf.co/squ11z1/Mythos-nano:Q4_K_M

LM Studio
Jan

vLLM

How to use squ11z1/Mythos-nano with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "squ11z1/Mythos-nano"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "squ11z1/Mythos-nano",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/squ11z1/Mythos-nano:Q4_K_M

Ollama
How to use squ11z1/Mythos-nano with Ollama:
```
ollama run hf.co/squ11z1/Mythos-nano:Q4_K_M
```

Unsloth Studio

How to use squ11z1/Mythos-nano with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for squ11z1/Mythos-nano to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for squ11z1/Mythos-nano to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for squ11z1/Mythos-nano to start chatting

How to use squ11z1/Mythos-nano with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf squ11z1/Mythos-nano:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "squ11z1/Mythos-nano:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use squ11z1/Mythos-nano with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf squ11z1/Mythos-nano:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default squ11z1/Mythos-nano:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use squ11z1/Mythos-nano with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf squ11z1/Mythos-nano:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "squ11z1/Mythos-nano:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use squ11z1/Mythos-nano with Docker Model Runner:
```
docker model run hf.co/squ11z1/Mythos-nano:Q4_K_M
```

Lemonade

How to use squ11z1/Mythos-nano with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull squ11z1/Mythos-nano:Q4_K_M

Run and chat with the model

lemonade run user.Mythos-nano-Q4_K_M

List all available models

lemonade list

Mythos-nano / README.md

squ11z1

Update README.md

f0f4789 verified 5 days ago

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	---
	license: mit
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- reasoning
	- math
	- code
	- qwen2
	- mythos-nano
	base_model:
	- WeiboAI/VibeThinker-3B
	base_model_relation: finetune
	---
	> Mythos-nano tool-calling is coming, but check out Merlin-Agent!
	https://huggingface.co/Merlin-Research/Merlin-Agent

	![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.