Instructions to use MaziyarPanahi/VibeThinker-1.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use MaziyarPanahi/VibeThinker-1.5B-GGUF 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 MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use MaziyarPanahi/VibeThinker-1.5B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MaziyarPanahi/VibeThinker-1.5B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MaziyarPanahi/VibeThinker-1.5B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M
- Ollama
How to use MaziyarPanahi/VibeThinker-1.5B-GGUF with Ollama:
ollama run hf.co/MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use MaziyarPanahi/VibeThinker-1.5B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use MaziyarPanahi/VibeThinker-1.5B-GGUF with Docker Model Runner:
docker model run hf.co/MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M
- Lemonade
How to use MaziyarPanahi/VibeThinker-1.5B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.VibeThinker-1.5B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use MaziyarPanahi/VibeThinker-1.5B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MaziyarPanahi/VibeThinker-1.5B-GGUF: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 MaziyarPanahi/VibeThinker-1.5B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use MaziyarPanahi/VibeThinker-1.5B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MaziyarPanahi/VibeThinker-1.5B-GGUF: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 "MaziyarPanahi/VibeThinker-1.5B-GGUF: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"
MaziyarPanahi/VibeThinker-1.5B-GGUF
- Model creator: WeiboAI
- Original model: WeiboAI/VibeThinker-1.5B
๐จ We recommend using this model for competitive-style math and algorithm coding problems. It works better to ask the question in English. We do not advise using it for other tasks, as this is an experimental release aimed at exploring the reasoning capabilities of small models.
Description
MaziyarPanahi/VibeThinker-1.5B-GGUF contains GGUF format model files for WeiboAI/VibeThinker-1.5B.
About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
- llama.cpp. The source project for GGUF. Offers a CLI and a server option.
- llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
- LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
- text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
- KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
- GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
- LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
- Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
- candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
- ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
Special thanks
๐ Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.
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