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simlamkr1
/
llama2_finetuned_chatbot

Text Generation
Transformers
PyTorch
TensorBoard
llama
Generated from Trainer
text-generation-inference
Model card Files Files and versions
xet
Metrics Training metrics Community
1

Instructions to use simlamkr1/llama2_finetuned_chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use simlamkr1/llama2_finetuned_chatbot with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="simlamkr1/llama2_finetuned_chatbot")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("simlamkr1/llama2_finetuned_chatbot")
    model = AutoModelForCausalLM.from_pretrained("simlamkr1/llama2_finetuned_chatbot", device_map="auto")
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use simlamkr1/llama2_finetuned_chatbot with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "simlamkr1/llama2_finetuned_chatbot"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "simlamkr1/llama2_finetuned_chatbot",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/simlamkr1/llama2_finetuned_chatbot
  • SGLang

    How to use simlamkr1/llama2_finetuned_chatbot 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 "simlamkr1/llama2_finetuned_chatbot" \
        --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": "simlamkr1/llama2_finetuned_chatbot",
    		"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 "simlamkr1/llama2_finetuned_chatbot" \
            --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": "simlamkr1/llama2_finetuned_chatbot",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use simlamkr1/llama2_finetuned_chatbot with Docker Model Runner:

    docker model run hf.co/simlamkr1/llama2_finetuned_chatbot
llama2_finetuned_chatbot / runs
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  • 1 contributor
History: 12 commits
simlamkr1's picture
simlamkr1
End of training
71f6cdf almost 3 years ago
  • Aug28_15-02-10_3fd1e1c2ef86
    End of training almost 3 years ago
  • Aug28_15-42-40_3fd1e1c2ef86
    End of training almost 3 years ago
  • Aug28_23-17-31_47949a51b7a6
    End of training almost 3 years ago
  • Aug29_11-23-39_1a874cb68c7e
    End of training almost 3 years ago
  • Aug29_12-23-29_1a874cb68c7e
    End of training almost 3 years ago
  • Aug29_14-14-47_a86d6f0f970b
    End of training almost 3 years ago