Instructions to use TheBloke/deepseek-coder-6.7B-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/deepseek-coder-6.7B-instruct-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheBloke/deepseek-coder-6.7B-instruct-GGUF", dtype="auto") - llama-cpp-python
How to use TheBloke/deepseek-coder-6.7B-instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TheBloke/deepseek-coder-6.7B-instruct-GGUF", filename="deepseek-coder-6.7b-instruct.Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use TheBloke/deepseek-coder-6.7B-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 TheBloke/deepseek-coder-6.7B-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf TheBloke/deepseek-coder-6.7B-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 TheBloke/deepseek-coder-6.7B-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf TheBloke/deepseek-coder-6.7B-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 TheBloke/deepseek-coder-6.7B-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf TheBloke/deepseek-coder-6.7B-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 TheBloke/deepseek-coder-6.7B-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf TheBloke/deepseek-coder-6.7B-instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use TheBloke/deepseek-coder-6.7B-instruct-GGUF with Ollama:
ollama run hf.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use TheBloke/deepseek-coder-6.7B-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 TheBloke/deepseek-coder-6.7B-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 TheBloke/deepseek-coder-6.7B-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 TheBloke/deepseek-coder-6.7B-instruct-GGUF to start chatting
- Docker Model Runner
How to use TheBloke/deepseek-coder-6.7B-instruct-GGUF with Docker Model Runner:
docker model run hf.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF:Q4_K_M
- Lemonade
How to use TheBloke/deepseek-coder-6.7B-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TheBloke/deepseek-coder-6.7B-instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.deepseek-coder-6.7B-instruct-GGUF-Q4_K_M
List all available models
lemonade list
Reducing Latency in Locally Hosted model
I've been working on hosting the DeepSeek 6.7b LLM model locally on my machine for some time now, and while the results are impressive, I'm encountering higher latency than I'd like. I'm reaching out to gather insights and strategies from this community on how to optimize and reduce this latency.
@anshulchandel instead of using gguf, use exllamav2 if you can fit the model onto your gpu. That will be slightly below 2x faster then llama.cpp