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
Is the 2.51bit model using imatrix?
I've played with both the 2.51bit and 2.22bit R1 models before and 2.22 is much better than 2.51. For the new 0324 2.51bit model, is it using imatrix?
On a side note, 2.22bit is like 20% slower than 2.51bit on KTransformers. Not sure if it's caused by imatrix.
It's not due to imatrix.
2.22 uses more complex scaling to save bits and with current inference methods this costs time.
2.51 is not, however 2.22 is. Yes, imatrix might be making it slower
It's not due to imatrix.
2.22 uses more complex scaling to save bits and with current inference methods this costs time.
It is partially due to imatrix making it slower
That would be surprising. I'll do measurements to understand why this happens in 2 bit quant and not in 4bit.
I measured IQ4_NL before and there is no speed difference when using imatrix or not.
Yeah imatrix shouldn't affect the speed much. It's just that K Quants are easier on the cpu compared to iq quants.
I'm writing a new llama.cpp backend that inferences the IQ quant family much more efficiently, even faster than current K quants.
IQ4_NL and IQ4_XS will be the first two data types supported, that's why I care about them so much and benchmark in that area.
I'm writing a new llama.cpp backend that inferences the IQ quant family much more efficiently, even faster than current K quants.
IQ4_NL and IQ4_XS will be the first two data types supported, that's why I care about them so much and benchmark in that area.
That's nice to hear. Please just make a PR on llama.cpp, so everyone can benefit if you deem it successful.