Instructions to use richardyoung/uigen-x-30b-moe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use richardyoung/uigen-x-30b-moe with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="richardyoung/uigen-x-30b-moe", filename="models/uigen-x-30b-moe--Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use richardyoung/uigen-x-30b-moe with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf richardyoung/uigen-x-30b-moe:Q4_K_M # Run inference directly in the terminal: llama-cli -hf richardyoung/uigen-x-30b-moe:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf richardyoung/uigen-x-30b-moe:Q4_K_M # Run inference directly in the terminal: llama-cli -hf richardyoung/uigen-x-30b-moe: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 richardyoung/uigen-x-30b-moe:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf richardyoung/uigen-x-30b-moe: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 richardyoung/uigen-x-30b-moe:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf richardyoung/uigen-x-30b-moe:Q4_K_M
Use Docker
docker model run hf.co/richardyoung/uigen-x-30b-moe:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use richardyoung/uigen-x-30b-moe with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "richardyoung/uigen-x-30b-moe" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "richardyoung/uigen-x-30b-moe", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/richardyoung/uigen-x-30b-moe:Q4_K_M
- Ollama
How to use richardyoung/uigen-x-30b-moe with Ollama:
ollama run hf.co/richardyoung/uigen-x-30b-moe:Q4_K_M
- Unsloth Studio new
How to use richardyoung/uigen-x-30b-moe 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 richardyoung/uigen-x-30b-moe 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 richardyoung/uigen-x-30b-moe to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for richardyoung/uigen-x-30b-moe to start chatting
- Pi new
How to use richardyoung/uigen-x-30b-moe with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf richardyoung/uigen-x-30b-moe: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": "richardyoung/uigen-x-30b-moe:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use richardyoung/uigen-x-30b-moe with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf richardyoung/uigen-x-30b-moe: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 richardyoung/uigen-x-30b-moe:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use richardyoung/uigen-x-30b-moe with Docker Model Runner:
docker model run hf.co/richardyoung/uigen-x-30b-moe:Q4_K_M
- Lemonade
How to use richardyoung/uigen-x-30b-moe with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull richardyoung/uigen-x-30b-moe:Q4_K_M
Run and chat with the model
lemonade run user.uigen-x-30b-moe-Q4_K_M
List all available models
lemonade list
UIGEN-X 30B MoE (GGUF)
Quantized builds of the UIGEN-X 30B Mixture-of-Experts coding assistant for local inference with Ollama / llama.cpp runtimes. Each variant ships with the Modelfile exported from the Ollama registry plus the corresponding GGUF binary.
- Base model:
smirki/UIGEN-X-30B-MoE-merged-checkpoint-200 - Architecture: 30B parameter MoE tuned for software engineering tasks.
Variants
| Variant | Size | Blob |
|---|---|---|
q2_k |
10.49 GB | sha256-89dc7fdb0a4a30a6bd4e8a611db45fd821ccce4748d5bda1fc5151b5be0fc0fd |
q3_k_s |
12.38 GB | sha256-5dc18116ed7f2c98b96361b4a12e8b43fb4e75ee3dc162ba73a74226a3621a3c |
Q4_K_M |
17.28 GB | sha256-4c641495ea35d559011305746eeda3b9cc1c3ef6cabce16c2c260962715957e5 |
Q5_K_M |
20.23 GB | sha256-3848f1b66aeee5b454ad3c3d6133da2723e84e7cfa0bee9e67d81639cdbd9b4d |
Q6_K |
23.37 GB | sha256-4a08030b66cb100eff2c183500b7c96ef44e3f4816911d32fe55a1e2a4aa1d47 |
Q8_0 |
30.25 GB | sha256-fc84a665b1889d7c6bed55b2b5dedee464ba80400bf1fbd43164b48eb219e2a7 |
Usage with Ollama
Example with the Q5_K_M quantization:
ollama create uigen-x-30b-moe-q5-k-m -f modelfiles/uigen-x-30b-moe--Q5_K_M.Modelfile
ollama run uigen-x-30b-moe-q5-k-m
Swap Q5_K_M for any other variant listed above.
Source
Originally published on my Ollama profile: https://ollama.com/richardyoung/uigen-x-30b-moe
- Downloads last month
- 61
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
Model tree for richardyoung/uigen-x-30b-moe
Base model
smirki/UIGEN-X-30B-MoE-merged-checkpoint-200