Instructions to use unsloth/DeepSeek-V3-0324-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/DeepSeek-V3-0324-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/DeepSeek-V3-0324-GGUF", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("unsloth/DeepSeek-V3-0324-GGUF", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-V3-0324-GGUF", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unsloth/DeepSeek-V3-0324-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL
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 unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL
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 unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/DeepSeek-V3-0324-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/DeepSeek-V3-0324-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": "unsloth/DeepSeek-V3-0324-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL
- SGLang
How to use unsloth/DeepSeek-V3-0324-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "unsloth/DeepSeek-V3-0324-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/DeepSeek-V3-0324-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "unsloth/DeepSeek-V3-0324-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/DeepSeek-V3-0324-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use unsloth/DeepSeek-V3-0324-GGUF with Ollama:
ollama run hf.co/unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL
- Unsloth Desktop
- Docker Model Runner
How to use unsloth/DeepSeek-V3-0324-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/DeepSeek-V3-0324-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/DeepSeek-V3-0324-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.DeepSeek-V3-0324-GGUF-UD-Q4_K_XL
List all available models
lemonade list
- Atomic Chat
How many bits of Quantization is enough for Code Generation Tasks?
I'd like to share some experiments at https://medium.com/@GenerationAI/deepseek-v3-0324-for-code-generation-tasks-how-many-bits-of-quantization-is-enough-56b2548fc7a5
Question is not really correct.
Correct is - how low to be and avoid hallucinations in model produce, large models cannot be used in low quants like smaller 32-70B etc. That was obvious during first open large model Falcon 180B, which was unusable at low quants, it was horrible. Next was Meta 405B, same and even slower x2 than deepseek (they really improved speed). Unfortunately Q6 is ideal, because Q8 require additionally +100Gb RAM. Q5 is kinda good too, but i've registered difference in Q5 vs Q6-latter is slightly better and mostly never hallucinate. I've tested Q6 already, it uses still same 567Gb RAM/VRAM locally.