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jialinyyzz
/
humanizer

Text Generation
Transformers
Safetensors
GGUF
MLX
English
Chinese
gemma4_unified
image-text-to-text
humanizer
text-rewriting
rewriting
paraphrase
style-transfer
llama.cpp
gemma4
imatrix
Model card Files Files and versions
xet
Community
1

Instructions to use jialinyyzz/humanizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use jialinyyzz/humanizer with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="jialinyyzz/humanizer")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("jialinyyzz/humanizer")
    model = AutoModelForMultimodalLM.from_pretrained("jialinyyzz/humanizer", device_map="auto")
  • MLX

    How to use jialinyyzz/humanizer with MLX:

    # Make sure mlx-lm is installed
    # pip install --upgrade mlx-lm
    # if on a CUDA device, also pip install mlx[cuda]
    
    # Generate text with mlx-lm
    from mlx_lm import load, generate
    
    model, tokenizer = load("jialinyyzz/humanizer")
    
    prompt = "Once upon a time in"
    text = generate(model, tokenizer, prompt=prompt, verbose=True)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use jialinyyzz/humanizer 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 jialinyyzz/humanizer:Q6_K
    # Run inference directly in the terminal:
    llama cli -hf jialinyyzz/humanizer:Q6_K
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf jialinyyzz/humanizer:Q6_K
    # Run inference directly in the terminal:
    llama cli -hf jialinyyzz/humanizer:Q6_K
    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 jialinyyzz/humanizer:Q6_K
    # Run inference directly in the terminal:
    ./llama-cli -hf jialinyyzz/humanizer:Q6_K
    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 jialinyyzz/humanizer:Q6_K
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf jialinyyzz/humanizer:Q6_K
    Use Docker
    docker model run hf.co/jialinyyzz/humanizer:Q6_K
  • LM Studio
  • Jan
  • vLLM

    How to use jialinyyzz/humanizer with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "jialinyyzz/humanizer"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "jialinyyzz/humanizer",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/jialinyyzz/humanizer:Q6_K
  • SGLang

    How to use jialinyyzz/humanizer 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 "jialinyyzz/humanizer" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "jialinyyzz/humanizer",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    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 "jialinyyzz/humanizer" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "jialinyyzz/humanizer",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Ollama

    How to use jialinyyzz/humanizer with Ollama:

    ollama run hf.co/jialinyyzz/humanizer:Q6_K
  • Unsloth Desktop
  • MLX LM

    How to use jialinyyzz/humanizer with MLX LM:

    Generate or start a chat session
    # Install MLX LM
    uv tool install mlx-lm
    # Generate some text
    mlx_lm.generate --model "jialinyyzz/humanizer" --prompt "Once upon a time"
  • Docker Model Runner

    How to use jialinyyzz/humanizer with Docker Model Runner:

    docker model run hf.co/jialinyyzz/humanizer:Q6_K
  • Lemonade

    How to use jialinyyzz/humanizer with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull jialinyyzz/humanizer:Q6_K
    Run and chat with the model
    lemonade run user.humanizer-Q6_K
    List all available models
    lemonade list
  • Atomic Chat
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Start here: prompt format, what it gets wrong, and what feedback we need

#1 opened 16 days ago by
jialinyyzz
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