Instructions to use Jithendra-k/interACT_LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jithendra-k/interACT_LLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Jithendra-k/interACT_LLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Jithendra-k/interACT_LLM") model = AutoModelForCausalLM.from_pretrained("Jithendra-k/interACT_LLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Jithendra-k/interACT_LLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jithendra-k/interACT_LLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jithendra-k/interACT_LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Jithendra-k/interACT_LLM
- SGLang
How to use Jithendra-k/interACT_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 "Jithendra-k/interACT_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": "Jithendra-k/interACT_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 "Jithendra-k/interACT_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": "Jithendra-k/interACT_LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Jithendra-k/interACT_LLM with Docker Model Runner:
docker model run hf.co/Jithendra-k/interACT_LLM
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Here is an Example Input/Output:<br>
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<img src="example.png"><br>
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Code to finetune a Llama-2_7B_chat model: [Google_Colab_file](https://colab.research.google.com/drive/
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## Ethical Considerations and Limitations
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Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
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Here is an Example Input/Output:<br>
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<img src="example.png"><br>
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Code to finetune a Llama-2_7B_chat model: [Google_Colab_file](https://colab.research.google.com/drive/1vDyO8pP12VoSjZ5qXZrd1OHUtLNRapte?usp=sharing)
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## Ethical Considerations and Limitations
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Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
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