Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

farwew
/
model_3

Text Generation
Transformers
PyTorch
English
llama
text-generation-inference
unsloth
trl
Model card Files Files and versions
xet
Community
2

Instructions to use farwew/model_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use farwew/model_3 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="farwew/model_3")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("farwew/model_3")
    model = AutoModelForCausalLM.from_pretrained("farwew/model_3")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use farwew/model_3 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "farwew/model_3"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "farwew/model_3",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/farwew/model_3
  • SGLang

    How to use farwew/model_3 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 "farwew/model_3" \
        --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": "farwew/model_3",
    		"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 "farwew/model_3" \
            --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": "farwew/model_3",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Unsloth Studio new

    How to use farwew/model_3 with Unsloth Studio:

    Install Unsloth Studio (macOS, Linux, WSL)
    curl -fsSL https://unsloth.ai/install.sh | sh
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for farwew/model_3 to start chatting
    Install Unsloth Studio (Windows)
    irm https://unsloth.ai/install.ps1 | iex
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for farwew/model_3 to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for farwew/model_3 to start chatting
    Load model with FastModel
    pip install unsloth
    from unsloth import FastModel
    model, tokenizer = FastModel.from_pretrained(
        model_name="farwew/model_3",
        max_seq_length=2048,
    )
  • Docker Model Runner

    How to use farwew/model_3 with Docker Model Runner:

    docker model run hf.co/farwew/model_3
model_3
16.1 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 6 commits
farwew's picture
farwew
Update README.md
4f77c44 verified 12 months ago
  • .gitattributes
    1.57 kB
    Upload tokenizer 12 months ago
  • README.md
    567 Bytes
    Update README.md 12 months ago
  • config.json
    747 Bytes
    Trained with Unsloth 12 months ago
  • generation_config.json
    198 Bytes
    Trained with Unsloth 12 months ago
  • pytorch_model-00001-of-00004.bin
    4.98 GB
    xet
    Trained with Unsloth 12 months ago
  • pytorch_model-00002-of-00004.bin
    5 GB
    xet
    Trained with Unsloth 12 months ago
  • pytorch_model-00003-of-00004.bin
    4.92 GB
    xet
    Trained with Unsloth 12 months ago
  • pytorch_model-00004-of-00004.bin
    1.17 GB
    xet
    Trained with Unsloth 12 months ago
  • pytorch_model.bin.index.json
    24 kB
    Trained with Unsloth 12 months ago
  • special_tokens_map.json
    464 Bytes
    Upload tokenizer 12 months ago
  • tokenizer.json
    17.2 MB
    xet
    Upload tokenizer 12 months ago
  • tokenizer.model
    493 kB
    xet
    Upload tokenizer 12 months ago
  • tokenizer_config.json
    50.6 kB
    Upload tokenizer 12 months ago