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
Weird tokens
Thanks for converting this model! Although I see some weird tokens when running llama.cpp (compiled from master).
llm_load_print_meta: general.name = deepseek-ai_deepseek-coder-33b-instruct
llm_load_print_meta: BOS token = 32013 '<|begin▁of▁sentence|>'
llm_load_print_meta: EOS token = 32014 '<|end▁of▁sentence|>'
llm_load_print_meta: PAD token = 32014 '<|end▁of▁sentence|>'
llm_load_print_meta: LF token = 30 '?'
Yeah it is weird, but that's exactly what's defined in tokenizer_config.json:
[pytorch2] tomj@MC:/workspace/git/gguf-llama (master ✘)✭ ᐅ grep -A5 -i eos /workspace/process/deepseek-ai_deepseek-coder-33b-instruct/source/tokenizer_config.json | cat -
ve
"add_eos_token": false,$
"bos_token": {$
"__type": "AddedToken",$
"content": "<M-oM-=M-^\beginM-bM-^VM-^AofM-bM-^VM-^AsentenceM-oM-=M-^\>",$
"lstrip": false,$
"normalized": true,$
--$
"eos_token": {$
"__type": "AddedToken",$
"content": "<M-oM-=M-^\endM-bM-^VM-^AofM-bM-^VM-^AsentenceM-oM-=M-^\>",$
"lstrip": false,$
"normalized": true,$
"rstrip": false,$
I don't know why they've used those weird chars, but this isn't a llama.cpp issue; it's using the tokens as defined by the original model.
FYI I'm just about to re-make all the GGUFs after an update to the convert.py I'm using, which affects special tokens. It won't change this, but might affect other aspects of special token usage.
That weird combination of characters is probably to reduce the odds of them being present in random input.
The output being garbled on dranger003's run is just a console character set issue.