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-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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-server -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-server -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
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

Disclaimer: This is not an official release by Anthropic.
Mythos-nano is an independent open model project.

Mythos-nano

🚨 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).

🏆 Benchmarks

Mythos-nano (3B) vs. frontier models. +CLR = with test-time CLR boost.

Benchmark	Mythos-nano	+CLR	Qwen3.6 Plus	Gemini 3 Pro	GLM-5	Kimi K2.5	Claude Opus 4.5
AIME'25	91.4	96.7	93.3	96.0	96.7	96.1	92.8
AIME'26	94.3	97.1	95.3	91.7	95.8	93.3	95.1
HMMT'25	89.3	95.4	96.7	97.5	97.9	95.4	92.9
IMO-AnswerBench	76.4	80.6	83.8	83.1	82.5	81.8	78.5
LiveCodeBench v6	80.2	—	87.1	87.4	85.5	85.0	84.8
IFBench	74.5	—	74.2	70.4	76.5	70.0	58.0

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

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))