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PsycheFoundation
/
llama2-3b-gqa-init

Text Generation
Transformers
Safetensors
llama
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use PsycheFoundation/llama2-3b-gqa-init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use PsycheFoundation/llama2-3b-gqa-init with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="PsycheFoundation/llama2-3b-gqa-init")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("PsycheFoundation/llama2-3b-gqa-init")
    model = AutoModelForCausalLM.from_pretrained("PsycheFoundation/llama2-3b-gqa-init")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use PsycheFoundation/llama2-3b-gqa-init with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "PsycheFoundation/llama2-3b-gqa-init"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "PsycheFoundation/llama2-3b-gqa-init",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/PsycheFoundation/llama2-3b-gqa-init
  • SGLang

    How to use PsycheFoundation/llama2-3b-gqa-init 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 "PsycheFoundation/llama2-3b-gqa-init" \
        --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": "PsycheFoundation/llama2-3b-gqa-init",
    		"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 "PsycheFoundation/llama2-3b-gqa-init" \
            --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": "PsycheFoundation/llama2-3b-gqa-init",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use PsycheFoundation/llama2-3b-gqa-init with Docker Model Runner:

    docker model run hf.co/PsycheFoundation/llama2-3b-gqa-init
llama2-3b-gqa-init
6.03 GB
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  • 1 contributor
History: 3 commits
emozilla's picture
emozilla
Upload tokenizer
6f6abae verified about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 1 year ago
  • README.md
    5.17 kB
    Upload LlamaForCausalLM about 1 year ago
  • config.json
    676 Bytes
    Upload LlamaForCausalLM about 1 year ago
  • generation_config.json
    111 Bytes
    Upload LlamaForCausalLM about 1 year ago
  • model-00001-of-00002.safetensors
    4.98 GB
    xet
    Upload LlamaForCausalLM about 1 year ago
  • model-00002-of-00002.safetensors
    1.05 GB
    xet
    Upload LlamaForCausalLM about 1 year ago
  • model.safetensors.index.json
    21 kB
    Upload LlamaForCausalLM about 1 year ago
  • special_tokens_map.json
    548 Bytes
    Upload tokenizer about 1 year ago
  • tokenizer.json
    3.62 MB
    Upload tokenizer about 1 year ago
  • tokenizer_config.json
    949 Bytes
    Upload tokenizer about 1 year ago