Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

JammyMachina
/
improved_4bars-mdl

Text Generation
Transformers
PyTorch
gpt2
Generated from Trainer
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use JammyMachina/improved_4bars-mdl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use JammyMachina/improved_4bars-mdl with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="JammyMachina/improved_4bars-mdl")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMultimodalLM
    
    tokenizer = AutoTokenizer.from_pretrained("JammyMachina/improved_4bars-mdl")
    model = AutoModelForMultimodalLM.from_pretrained("JammyMachina/improved_4bars-mdl")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use JammyMachina/improved_4bars-mdl with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "JammyMachina/improved_4bars-mdl"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "JammyMachina/improved_4bars-mdl",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/JammyMachina/improved_4bars-mdl
  • SGLang

    How to use JammyMachina/improved_4bars-mdl 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 "JammyMachina/improved_4bars-mdl" \
        --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": "JammyMachina/improved_4bars-mdl",
    		"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 "JammyMachina/improved_4bars-mdl" \
            --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": "JammyMachina/improved_4bars-mdl",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use JammyMachina/improved_4bars-mdl with Docker Model Runner:

    docker model run hf.co/JammyMachina/improved_4bars-mdl
improved_4bars-mdl / manual_upload
54 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
m4lw4r3exe's picture
m4lw4r3exe
Upload with huggingface_hub
d126247 over 3 years ago
  • manual_upload
    Upload with huggingface_hub over 3 years ago
  • .gitattributes
    1.48 kB
    Upload manual_upload with huggingface_hub over 3 years ago
  • .gitignore
    13 Bytes
    Upload manual_upload with huggingface_hub over 3 years ago
  • special_tokens_map.json
    27 Bytes
    Upload manual_upload with huggingface_hub over 3 years ago
  • tokenizer.json
    8.57 kB
    Upload manual_upload with huggingface_hub over 3 years ago
  • tokenizer_config.json
    106 Bytes
    Upload manual_upload with huggingface_hub over 3 years ago
  • trainer_state.json
    325 Bytes
    Upload manual_upload with huggingface_hub over 3 years ago
  • training_args.json
    2.98 kB
    Upload with huggingface_hub over 3 years ago