Instructions to use lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF", filename="DeepSeek-Coder-V2-Lite-Instruct-IQ3_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 lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF: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 lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF: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 lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-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": "lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M
- Ollama
How to use lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF with Ollama:
ollama run hf.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M
- Unsloth Studio new
How to use lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF 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 lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF 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 lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF to start chatting
- Docker Model Runner
How to use lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M
- Lemonade
How to use lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.DeepSeek-Coder-V2-Lite-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
GGUF for the 236B model
Any plans for the GGUF of the 236B parameters model?
Not onto this page, we want to keep this to models that the average user should run, and even at Q2 (the absolute bare minimum people should run) the 236B model is 86GB, which almost no one will be able to run, so we don't want to recommend it
You can find some quants here if you do have the specs for it: https://huggingface.co/bartowski/DeepSeek-Coder-V2-Instruct-GGUF
Thanks for your reply.
I'm running a machine with an A100 GPU, and also have 220GB of RAM on this machine.
Do you think I should be able to run the following?:
https://huggingface.co/bartowski/DeepSeek-Coder-V2-Instruct-GGUF/tree/main/DeepSeek-Coder-V2-Instruct-Q4_K_M.gguf
Not onto this page, we want to keep this to models that the average user should run, and even at Q2 (the absolute bare minimum people should run) the 236B model is 86GB, which almost no one will be able to run, so we don't want to recommend it
You can find some quants here if you do have the specs for it: https://huggingface.co/bartowski/DeepSeek-Coder-V2-Instruct-GGUF
https://pastebin.com/wRVCpcep [hardware specs (i need to update this to p40's) maybe, but I'm crazy enough to try]