Instructions to use luclima/mythos-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luclima/mythos-models with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="luclima/mythos-models",
	filename="models/DeepSeek-R1-Distill-Qwen-8B-Abliterated.i1-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use luclima/mythos-models 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 luclima/mythos-models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf luclima/mythos-models:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf luclima/mythos-models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf luclima/mythos-models: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 luclima/mythos-models:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf luclima/mythos-models: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 luclima/mythos-models:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf luclima/mythos-models:Q4_K_M

Use Docker

docker model run hf.co/luclima/mythos-models:Q4_K_M

LM Studio
Jan
Ollama
How to use luclima/mythos-models with Ollama:
```
ollama run hf.co/luclima/mythos-models:Q4_K_M
```

Unsloth Studio

How to use luclima/mythos-models 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 luclima/mythos-models 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 luclima/mythos-models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for luclima/mythos-models to start chatting

Atomic Chat new
Docker Model Runner
How to use luclima/mythos-models with Docker Model Runner:
```
docker model run hf.co/luclima/mythos-models:Q4_K_M
```

Lemonade

How to use luclima/mythos-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull luclima/mythos-models:Q4_K_M

Run and chat with the model

lemonade run user.mythos-models-Q4_K_M

List all available models

lemonade list

MYTHOS AI - Models & Research Repos

Hugging Face companion repository for MYTHOS AI.

GGUF Models (47 GB)

Qwen3 series (0.6B, 1.7B, 4B, 8B, 14B)
Phi-4 series (mini, full)
Gemma 3 (4B, 12B)
Llama 3.3 8B
DeepSeek R1 8B
Gemma 2 2B

Asian AI Research Repos (23 repos, 1 GB)

Chinese: DeepSeek-V3/R1/V4, Qwen/A3, GLM-4/5, MiniMax-M2, RAGFlow, FastGPT, Dify, MetaGPT, PaddleOCR, DeepEP, DeepGEMM, DeepSpec, 3FS Japanese: ELYZA, rinna, Stockmark, PLaMo Korean: EXAONE, OmniServe, Upstage Solar

Global AI Research Repos (12 repos, 2.9 GB)

USA: llama-models, gemma, llamafile, whisper, NeMo Russia: YaLM-100B Ukraine: silero-models, RHVoice Canada: cohere-toolkit Europe: mistral-inference, flux

Downloads last month: -

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

luclima
/

mythos-models

MYTHOS AI - Models & Research Repos

Contents

GGUF Models (47 GB)

Asian AI Research Repos (23 repos, 1 GB)

Global AI Research Repos (12 repos, 2.9 GB)