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

  • Log In
  • Sign Up

currentlyexhausted
/
mobile-llm

Text Generation
Transformers
PyTorch
English
t5
text2text-generation
pocket
llm
mobile
alpaca
flan-t5
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use currentlyexhausted/mobile-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use currentlyexhausted/mobile-llm with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="currentlyexhausted/mobile-llm")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("currentlyexhausted/mobile-llm")
    model = AutoModelForSeq2SeqLM.from_pretrained("currentlyexhausted/mobile-llm")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use currentlyexhausted/mobile-llm with vLLM:

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

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

    How to use currentlyexhausted/mobile-llm with Docker Model Runner:

    docker model run hf.co/currentlyexhausted/mobile-llm
mobile-llm
991 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 8 commits
currentlyexhausted's picture
currentlyexhausted
Upload folder using huggingface_hub
4a83bd0 almost 3 years ago
  • .gitattributes
    1.48 kB
    initial commit almost 3 years ago
  • README.md
    205 Bytes
    Create README.md almost 3 years ago
  • config.json
    1.54 kB
    Upload folder using huggingface_hub almost 3 years ago
  • generation_config.json
    142 Bytes
    Upload folder using huggingface_hub almost 3 years ago
  • pytorch_model.bin
    990 MB
    xet
    Upload folder using huggingface_hub almost 3 years ago
  • special_tokens_map.json
    2.2 kB
    Upload folder using huggingface_hub almost 3 years ago
  • spiece.model
    792 kB
    xet
    Upload folder using huggingface_hub almost 3 years ago
  • tokenizer_config.json
    2.35 kB
    Upload folder using huggingface_hub almost 3 years ago