Instructions to use Shadow0482/mythos_fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shadow0482/mythos_fast with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Shadow0482/mythos_fast",
	filename="VibeThinker-3B.BF16.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 Shadow0482/mythos_fast 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 Shadow0482/mythos_fast:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Shadow0482/mythos_fast:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Shadow0482/mythos_fast:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Shadow0482/mythos_fast: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 Shadow0482/mythos_fast:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Shadow0482/mythos_fast: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 Shadow0482/mythos_fast:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Shadow0482/mythos_fast:Q4_K_M

Use Docker

docker model run hf.co/Shadow0482/mythos_fast:Q4_K_M

LM Studio
Jan
Ollama
How to use Shadow0482/mythos_fast with Ollama:
```
ollama run hf.co/Shadow0482/mythos_fast:Q4_K_M
```

Unsloth Studio

How to use Shadow0482/mythos_fast 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 Shadow0482/mythos_fast 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 Shadow0482/mythos_fast to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Shadow0482/mythos_fast to start chatting

How to use Shadow0482/mythos_fast with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shadow0482/mythos_fast: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": "Shadow0482/mythos_fast:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Shadow0482/mythos_fast with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shadow0482/mythos_fast: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 Shadow0482/mythos_fast:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Shadow0482/mythos_fast with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shadow0482/mythos_fast: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 "Shadow0482/mythos_fast: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 Shadow0482/mythos_fast with Docker Model Runner:
```
docker model run hf.co/Shadow0482/mythos_fast:Q4_K_M
```

Lemonade

How to use Shadow0482/mythos_fast with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Shadow0482/mythos_fast:Q4_K_M

Run and chat with the model

lemonade run user.mythos_fast-Q4_K_M

List all available models

lemonade list

mythos_fast / README.md

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metadata

tags:
  - gguf
  - llama.cpp
  - agent
  - tool-use
base_model: WeiboAI/VibeThinker-3B
pipeline_tag: reinforcement-learning

mythos_fast : GGUF

Model Description

mythos_fast is a fine-tuned version of WeiboAI/VibeThinker-3B, adapted for custom tool-use and agentic task execution. The base model was trained on a rich dataset of approximately 2 million samples covering multi-step tool calls, function-calling formats, and agent-style reasoning traces, then converted to GGUF format for efficient local inference with llama.cpp.

Training Details

Base model: WeiboAI/VibeThinker-3B
Fine-tuning focus: tool-use / function calling, agentic task completion
Dataset size: ~2,000,000 samples
Output format: GGUF (F16)

Available Model Files

VibeThinker-3B.F16.gguf

Usage

For text-only LLMs:

llama-cli -hf Shadow0482/mythos_fast --jinja

For multimodal models:

llama-mtmd-cli -hf Shadow0482/mythos_fast --jinja

Intended Use

This model is intended for local inference scenarios that require tool-calling and agent-style task execution, such as autonomous agents, function-calling pipelines, and multi-step reasoning workflows.

Limitations

Performance on tool-use tasks depends on the format and structure of tool definitions provided at inference time. Results may vary outside the training distribution covered by the fine-tuning dataset.