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Harsha001
/
humanizer-12b-ROCmFPX

Text Generation
GGUF
English
Chinese
rocmfpx
rocmfp4
amd
rocm
strix-halo
quantized
humanizer
text-rewriting
gemma4
imatrix
conversational
Model card Files Files and versions
xet
Community

Instructions to use Harsha001/humanizer-12b-ROCmFPX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use Harsha001/humanizer-12b-ROCmFPX 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 Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
    # Run inference directly in the terminal:
    llama cli -hf Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
    # Run inference directly in the terminal:
    llama cli -hf Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
    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 Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
    # Run inference directly in the terminal:
    ./llama-cli -hf Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
    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 Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
    Use Docker
    docker model run hf.co/Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
  • Llama new
  • LM Studio
  • Jan
  • vLLM

    How to use Harsha001/humanizer-12b-ROCmFPX with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Harsha001/humanizer-12b-ROCmFPX"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Harsha001/humanizer-12b-ROCmFPX",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
  • Ollama

    How to use Harsha001/humanizer-12b-ROCmFPX with Ollama:

    ollama run hf.co/Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
  • Unsloth Desktop
  • Docker Model Runner

    How to use Harsha001/humanizer-12b-ROCmFPX with Docker Model Runner:

    docker model run hf.co/Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
  • Lemonade

    How to use Harsha001/humanizer-12b-ROCmFPX with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull Harsha001/humanizer-12b-ROCmFPX:Q4_0_ROCMFP
    Run and chat with the model
    lemonade run user.humanizer-12b-ROCmFPX-Q4_0_ROCMFP
    List all available models
    lemonade list
  • Atomic Chat
humanizer-12b-ROCmFPX
35.2 GB
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  • 1 contributor
History: 1 commit
Harsha001's picture
Harsha001
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  • .gitattributes
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  • NOTICE
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  • README.md
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  • humanizer-12b-Q4_0_ROCMFP4_FAST.gguf
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  • humanizer-12b-Q4_0_ROCMFP4_STRIX.gguf
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  • humanizer-12b-Q6_K_ROCMFPX.gguf
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  • humanizer-12b-Q8_0_ROCMFPX.gguf
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  • prompt_format.json
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