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
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

anthonyfang
/
myllm2

Text Generation
Transformers
PyTorch
TensorBoard
llama
Trained with AutoTrain
text-generation-inference
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use anthonyfang/myllm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use anthonyfang/myllm2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="anthonyfang/myllm2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("anthonyfang/myllm2")
    model = AutoModelForCausalLM.from_pretrained("anthonyfang/myllm2", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use anthonyfang/myllm2 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "anthonyfang/myllm2"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "anthonyfang/myllm2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/anthonyfang/myllm2
  • SGLang

    How to use anthonyfang/myllm2 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 "anthonyfang/myllm2" \
        --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": "anthonyfang/myllm2",
    		"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 "anthonyfang/myllm2" \
            --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": "anthonyfang/myllm2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use anthonyfang/myllm2 with Docker Model Runner:

    docker model run hf.co/anthonyfang/myllm2

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

Gated model
You can list files but not access them

Preview of files found in this repository
  • README.md
    1.52 kB
    Upload folder using huggingface_hub almost 3 years ago
  • adapter_config.json
    456 Bytes
    Upload folder using huggingface_hub almost 3 years ago
  • adapter_model.bin
    33.6 MB
    xet
    Upload folder using huggingface_hub almost 3 years ago
  • optimizer.pt
    67.2 MB
    xet
    Upload folder using huggingface_hub almost 3 years ago
  • rng_state_0.pth
    15.5 kB
    xet
    Upload folder using huggingface_hub almost 3 years ago
  • rng_state_1.pth
    15.5 kB
    xet
    Upload folder using huggingface_hub almost 3 years ago
  • scheduler.pt
    627 Bytes
    xet
    Upload folder using huggingface_hub almost 3 years ago
  • special_tokens_map.json
    434 Bytes
    Upload folder using huggingface_hub almost 3 years ago
  • tokenizer.json
    1.84 MB
    Upload folder using huggingface_hub almost 3 years ago
  • tokenizer.model
    500 kB
    xet
    Upload folder using huggingface_hub almost 3 years ago
  • tokenizer_config.json
    649 Bytes
    Upload folder using huggingface_hub almost 3 years ago
  • trainer_state.json
    486 Bytes
    Upload folder using huggingface_hub almost 3 years ago
  • training_args.bin
    3.96 kB
    xet
    Upload folder using huggingface_hub almost 3 years ago