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
Re-converting the GGUF for MLA?
+1 to this please! 16K cache by default uses ~80GB VRAM, while with MLA it is barely near that (some few GBs instead)
+1 Would be a game changer for long context work.
Is there a script available anywhere to dynamically quant this model with the latest changes ourselves if Unsloth have no plans to?
Bumping this request in case anyone at UnSloth sees this. I pulled the q4_K_M last night and realized when I ran it that the current gguf doesn't support MLA. To give a comparison: The unsloth Deepseek R1 0528 q4_K_M requires around 16GB of KV Cache for 32768 tokens. This v3 0324 gguf requires around 156GB of KV Cache for 32768 tokens. This means on the M3 Ultra Mac Studio, I load up to a q5_K_M at 32k of R1 0528, while I might be able to squeeze a q3 of v3 0324... maybe.
I'm going to try to quantize this myself for my own uses to see if it improves the situation, but thought I'd toss the request out here for others, unless there's a reason that I'm missing why it wouldn't work.