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tinh2406
/
llama2_retrieve

Text Generation
Transformers
Safetensors
llama
trl
sft
text-generation-inference
4-bit precision
bitsandbytes
Model card Files Files and versions
xet
Community
1

Instructions to use tinh2406/llama2_retrieve with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use tinh2406/llama2_retrieve with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="tinh2406/llama2_retrieve")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("tinh2406/llama2_retrieve")
    model = AutoModelForCausalLM.from_pretrained("tinh2406/llama2_retrieve")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use tinh2406/llama2_retrieve with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "tinh2406/llama2_retrieve"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "tinh2406/llama2_retrieve",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/tinh2406/llama2_retrieve
  • SGLang

    How to use tinh2406/llama2_retrieve 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 "tinh2406/llama2_retrieve" \
        --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": "tinh2406/llama2_retrieve",
    		"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 "tinh2406/llama2_retrieve" \
            --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": "tinh2406/llama2_retrieve",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use tinh2406/llama2_retrieve with Docker Model Runner:

    docker model run hf.co/tinh2406/llama2_retrieve
llama2_retrieve
4.96 GB
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  • 1 contributor
History: 8 commits
tinh2406's picture
tinh2406
Upload model
b3a628c verified 11 months ago
  • .gitattributes
    1.52 kB
    initial commit 12 months ago
  • README.md
    5.18 kB
    Upload LlamaForCausalLM (#9) 12 months ago
  • adapter_config.json
    788 Bytes
    Upload model 11 months ago
  • adapter_model.safetensors
    134 MB
    xet
    Upload model 11 months ago
  • config.json
    1.24 kB
    Upload LlamaForCausalLM (#9) 12 months ago
  • generation_config.json
    132 Bytes
    Upload LlamaForCausalLM (#9) 12 months ago
  • model.safetensors
    4.83 GB
    xet
    Upload LlamaForCausalLM (#9) 12 months ago