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unsloth
/
DeepSeek-V3-0324-GGUF

Text Generation
Transformers
GGUF
English
deepseek_v3
deepseek
unsloth
custom_code
fp8
conversational
Model card Files Files and versions
xet
Community
17

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
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

IQ2_XXS optimal for me

#17 opened over 1 year ago by
jweb

World's Largest Dataset

#16 opened over 1 year ago by deleted

Re-converting the GGUF for MLA?

👍 6
3
#15 opened over 1 year ago by
Silver267

What tool/framework to test gguf models?

1
#14 opened over 1 year ago by
bobchenyx

Request: DOI

#13 opened over 1 year ago by
jeffhoule01

How to run ollama using these new quantized weights?

👀 1
2
#12 opened over 1 year ago by
vadimkantorov

Running Model "unsloth/DeepSeek-V3-0324-GGUF" with vLLM does not working

2
#11 opened over 1 year ago by
puppadas

The UD-IQ2_XXS is surprisingly good, but it's good to know that it degrades gradually but significantly after about 1000 tokens.

1
#9 opened over 1 year ago by
mmbela

671B params or 685B params?

6
#8 opened over 1 year ago by
createthis

how to run tools use correctly

#7 opened over 1 year ago by
rockcat-miao

How many bits of Quantization is enough for Code Generation Tasks?

1
#5 opened over 1 year ago by
luweigen

Added IQ1_S version to Ollama

3
#4 opened over 1 year ago by
Muhammadreza

Is the 2.51bit model using imatrix?

7
#3 opened over 1 year ago by
daweiba12

Will you release the imatrix.dat used for the quants?

2
#2 opened over 1 year ago by
tdh111

Would There be Dynamic Qunatized Versions like 2.51bit

8
#1 opened over 1 year ago by
MotorBottle
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