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Merge pull request #289 from NYU-RTS/Amanda-dong-patch-1
update intro and overview
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docs/hpc/08_ml_ai_hpc/01_intro.md

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@@ -6,6 +6,8 @@ For ML we'll cover two prominent open-source deep learning frameworks, PyTorch a
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For AI we'll cover how to run a Hugging Face Large Language Model (LLM) and how to fine tune an LLM. Specifically, we provide guides on:
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* **LLM Inference**: Use the standard Hugging Face transformers library for basic tasks, and also introduce vLLM, a high-throughput serving engine that offers faster inference and an OpenAI-compatible API.
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* **vLLM CLI**: We provide examples of using the vllm command-line tool to quickly serve models and engage in interactive chat sessions.
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* **Fine-tuning LLMs**: We provide a practical example of fine-tuning the Gemma model to follow specific instructions. This section compares the original model with our improved version, showing how to achieve better response quality on Torch.
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**LLM Inference**: Use the standard Hugging Face transformers library for basic tasks, and also introduce vLLM, a high-throughput serving engine that offers faster inference and an OpenAI-compatible API.
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**vLLM CLI**: We provide examples of using the vllm command-line tool to quickly serve models and engage in interactive chat sessions.
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**Fine-tuning LLMs**: We provide a practical example of fine-tuning the Gemma model to follow specific instructions. This section compares the original model with our improved version, showing how to achieve better response quality on Torch.

docs/hpc/08_ml_ai_hpc/llm_inference/05_run_hf_model.md renamed to docs/hpc/08_ml_ai_hpc/LLM Inference/05_run_hf_model.md

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