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Volpidash
/
deepseek-r1-14b-security

Text Generation
PEFT
Safetensors
GGUF
English
cybersecurity
security-research
cve
ios
deepseek-r1
qlora
lm-studio
conversational
Model card Files Files and versions
xet
Community

Instructions to use Volpidash/deepseek-r1-14b-security with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use Volpidash/deepseek-r1-14b-security with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForCausalLM
    
    base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-14B")
    model = PeftModel.from_pretrained(base_model, "Volpidash/deepseek-r1-14b-security")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use Volpidash/deepseek-r1-14b-security 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 Volpidash/deepseek-r1-14b-security:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf Volpidash/deepseek-r1-14b-security:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf Volpidash/deepseek-r1-14b-security:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf Volpidash/deepseek-r1-14b-security:Q4_K_M
    Build from source code
    git clone https://github.com/ggml-org/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-app
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama serve -hf Volpidash/deepseek-r1-14b-security:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama cli -hf Volpidash/deepseek-r1-14b-security:Q4_K_M
    Use Docker
    docker model run hf.co/Volpidash/deepseek-r1-14b-security:Q4_K_M
  • Llama new
  • LM Studio
  • Jan
  • vLLM

    How to use Volpidash/deepseek-r1-14b-security with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Volpidash/deepseek-r1-14b-security"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Volpidash/deepseek-r1-14b-security",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/Volpidash/deepseek-r1-14b-security:Q4_K_M
  • Ollama

    How to use Volpidash/deepseek-r1-14b-security with Ollama:

    ollama run hf.co/Volpidash/deepseek-r1-14b-security:Q4_K_M
  • Unsloth Desktop
  • Docker Model Runner

    How to use Volpidash/deepseek-r1-14b-security with Docker Model Runner:

    docker model run hf.co/Volpidash/deepseek-r1-14b-security:Q4_K_M
  • Lemonade

    How to use Volpidash/deepseek-r1-14b-security with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull Volpidash/deepseek-r1-14b-security:Q4_K_M
    Run and chat with the model
    lemonade run user.deepseek-r1-14b-security-Q4_K_M
    List all available models
    lemonade list
  • Atomic Chat
deepseek-r1-14b-security / evaluation
24.9 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
Volpidash's picture
Volpidash
Publish security benchmark protocol, measured coverage and available results
8a2e047 verified 1 day ago
  • benchmark_data.py
    13.1 kB
    Publish security benchmark protocol, measured coverage and available results 1 day ago
  • benchmark_metrics.py
    2.36 kB
    Publish security benchmark protocol, measured coverage and available results 1 day ago
  • run_benchmarks.py
    9.48 kB
    Publish security benchmark protocol, measured coverage and available results 1 day ago