LlaMa-2 Local-Inferencing - NO GPU Requried - Only CPU

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AI Summary

## Summary: Setting Up LLaMA2 Inferencing Notebook  
  
- **Accessing LLaMA2 Model:**  
  - Obtain access by filling out a form on Meta's official page.  
  - Approval email allows downloading models.  
  - Additional approval needed from Hugging Face for hosted models.  
  
- **Using [[[[LLaMA]]]] 7B Model:**  
  - Smallest of three available [[[[LLaMA]]]] models.  
  - Local inferencing; works with GPU or high RAM (e.g., 34 GB).  
  
- **Task:**  
  - Text generation based on a given question.  
  
- **Setup:**  
  - Initialize text generation pipeline with Hugging Face Transformers.  
  - Define the model (`meta [[[[LLaMA]]]] llama27b-hf`), tokenizer, and access token.  
  
- **Code Execution:**  
  - Install Transformers library if not present.  
  - Import necessary libraries (e.g., torch).  
  - Check for GPU availability; alternatively, ensure sufficient RAM.  
  - Log in to Hugging Face with API token.  
  - Obtain the relevant tokenizer for the model.  
  
- **Tokenizer Importance:**  
  - Converts text strings into a format usable by pre-trained models.  
  - Handles tokenization, mapping, attention masks, and other requirements.  
  - `Transformers.AutoTokenizer` automatically matches the correct tokenizer to the model.  
  
- **Running the Pipeline:**  
  - Execute `transformers.pipeline` with parameters like model name, tokenizer, task type, and settings for text generation (e.g., temperature, max tokens, repetition penalty).  
  - Input prompt and print the generated text.  
  
- **Output Example:**  
  - Explanation of nuclear fission vs. fusion.  
  - Higher accuracy with larger models (e.g., 70B), but requires more computing power.