Instructions to use TheBloke/deepseek-coder-33B-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/deepseek-coder-33B-instruct-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheBloke/deepseek-coder-33B-instruct-GGUF", dtype="auto") - llama-cpp-python
How to use TheBloke/deepseek-coder-33B-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-33B-instruct-GGUF", filename="deepseek-coder-33b-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-33B-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-33B-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf TheBloke/deepseek-coder-33B-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-33B-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf TheBloke/deepseek-coder-33B-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-33B-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf TheBloke/deepseek-coder-33B-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-33B-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf TheBloke/deepseek-coder-33B-instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/TheBloke/deepseek-coder-33B-instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use TheBloke/deepseek-coder-33B-instruct-GGUF with Ollama:
ollama run hf.co/TheBloke/deepseek-coder-33B-instruct-GGUF:Q4_K_M
- Unsloth Studio new
How to use TheBloke/deepseek-coder-33B-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-33B-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-33B-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-33B-instruct-GGUF to start chatting
- Docker Model Runner
How to use TheBloke/deepseek-coder-33B-instruct-GGUF with Docker Model Runner:
docker model run hf.co/TheBloke/deepseek-coder-33B-instruct-GGUF:Q4_K_M
- Lemonade
How to use TheBloke/deepseek-coder-33B-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TheBloke/deepseek-coder-33B-instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.deepseek-coder-33B-instruct-GGUF-Q4_K_M
List all available models
lemonade list
Q4_K_S scores higher than Q8_0 on benchmarks
I'm testing Aider deployment with different models. Aider has its own in which it asks the model 134 programming questions and then tests the results. From previous tests on different models I know that yes, it is normal for the larger model to score better. In case of this model though, I'm seeing these scores:
modelname : first-shot : second-shot
deepseek-coder-33b-instruct.Q4_K_S.gguf : 43.3% : 52.2%
deepseek-coder-33b-instruct.Q8_0.gguf : 38.8% : 44.8%
This seems wrong. The Q4_K_S test was run twice on RTX4090 and once on M1Max with similar results. The Q8_0 test was run only once on the M1Max. I have no idea what is going on here. Could the Q8_0 be bad?
Both files were downloaded after your (several) fixes.