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
💫 Community Model> DeepSeek-Coder-V2-Lite-Instruct by DeepSeek
👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord.
Model creator: DeepSeek
Original model: DeepSeek-Coder-V2-Lite-Instruct
GGUF quantization: provided by bartowski based on llama.cpp release b3166
Model Settings:
Requires LM Studio 0.2.25, update can be downloaded from here: https://lmstudio.ai
Flash attention MUST be disabled for this model to work.
Model Summary:
This is a brand new Mixture of Export (MoE) model from DeepSeek, specializing in coding instructions.
This model performs well across a series of coding benchmarks and should be used for both instruction following and code completion.
Prompt template:
The best performing template is Deepseek Coder preset in your LM Studio.
This will format the prompt as follows:
You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science.",
### Instruction: {user_message}
### Response: {assistant_message}
The "official" template seems to tend towards generating Chinese, however if you'd like to use it you can set it up by choosing the LM Studio Blank Preset preset in your LM Studio and then:
Set your User Message Prefix to User:
Set your User Message Suffix to \n\nAssistant:
This will format the prompt as follows:
User: {user_message}
Assistant: {assistant_message}
Technical Details
This model is an MoE architecture, using 16B total weights with only 2.4B activated to achieve excellent inference speed.
DeepSeek-Coder-V2 is based on the DeepSeek-V2 model, further trained on 6 trillion high quality coding tokens to enhance coding and mathematical reasoning.
It supports an incredible 128k context length.
For more details, read their paper here: https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf
Special thanks
🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp
🙏 Special thanks to Kalomaze and Dampf for their work on the dataset (linked here) that was used for calculating the imatrix for all sizes.
Disclaimers
LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
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Model tree for lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF
Base model
deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct