LLaMA

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• Meta unveils a new large language model that can run on a single GPU | Benj Edwards - Ars Technica ... LLaMA-13B reportedly outperforms ChatGPT-like tech despite being 10x smaller.
• Meta heats up Big Tech's AI arms race with new language model | Yuvraj Malik and Katie Paul - Reuters
• You can now run a GPT-3 level AI model on your laptop, phone, and Raspberry Pi | Benj Edwards - Ars Technica ... On Friday, a software developer named Georgi Gerganov created a tool called "llama.cpp" that can run Meta's new GPT-3-class AI large language model, LLaMA, locally on a Mac laptop. Soon thereafter, people worked out how to run LLaMA on Windows as well. Then someone showed it running on a Pixel 6 phone, and next came a Raspberry Pi (albeit running very slowly).
• Introducing Meta Llama 3: The most capable openly available LLM to date | Meta
• llama_index_mediawiki-service github ... a container-virtualised service that aims to run a local Large Language Model (LLM) to assist wiki users.
• Meta releases Llama 3, claims it’s among the best open models available | Kyle Wiggers - Techcrunch
• Meta AI: What is Llama 3 and why does it matter? | Harry Guinness - Zapier
• Meta-Llama-3-8B | Hugging Face

LLaMA is a Large Language Model (LLM) released by Meta Platforms Inc. (formerly Facebook Inc.). LLaMA represents a significant advancement in open-source large language models from Meta, establishing them as a leader in this space with highly capable and scalable models that are now widely accessible. The key points are:

• LLaMA 3 models are now available in 8 billion and 70 billion parameter sizes, representing a significant increase in scale and capability compared to the previous LLaMA 2 models.

• The LLaMA 3 models have been trained on over 15 trillion tokens of data, 7 times more than the LLaMA 2 models, including 4 times more code data. This has resulted in major improvements in performance on benchmarks like MMLU, GSM-K, and HumanEval.

• Key new capabilities of LLaMA 3 include enhanced reasoning, code generation, and instruction following, as well as improved safety features like reduced false refusal rates and increased response diversity.

• Meta is also currently training even larger LLaMA 3 models over 400 billion parameters, which will add multimodal and multilingual capabilities.

• The LLaMA 3 models are being made openly available by Meta to the developer community, with support from major hardware providers like Intel, Qualcomm, and AMD. This establishes LLaMA 3 as a leading open-source AI model.

Implement a Chat with LLaMA

When Meta shares the "weights" of the LLaMA model, they are providing the parameters that have been learned during the training process, which include embedding, self-attention, feedforward, and bias weights. These weights are essential for the model to function correctly and are what enable the model to process natural language and generate coherent and contextually relevant text.

To implement a chat locally on your machine using the weights from Meta for LLaMA, you will need to follow these steps. By following these steps, you can set up and run a LLaMA-based chat locally on your machine, allowing you to interact with the model and develop applications that leverage its natural language processing capabilities:

1. Download the Pretrained Model Weights: Obtain the pretrained model weights from official sources such as Meta's webpage, GitHub, Hugging Face, or Ollama.

2. Set Up Your Local Environment: Ensure that your local machine has the necessary hardware specifications, such as a strong CPU and a significant amount of GPU memory, to run a large language model like LLaMA. If you have enough GPU memory, you can run larger models with full precision.

3. Install Required Libraries and Dependencies: Use Python to write the script for setting up and running the model. Install the `transformers` and `accelerate` libraries from Hugging Face using the commands `pip install transformers` and `pip install accelerate`.

4. Write Your Python Script: Import necessary modules such as `LlamaForCausalLM`, `LlamaTokenizer`, `pipeline`, and `torch`. Load the LLaMA model with the downloaded weights, define and instantiate the tokenizer and pipeline, and run the pipeline to generate responses based on input prompts.

5. Run the Model Locally: Save your Python script and execute it using the command `python <name of script>.py`. Provide different prompts as input to generate responses and test the model's performance.

6. Use Open-Source Tools for Local Execution: Utilize open-source tools like Hugging Face's Transformers library to pull the models from the Hugging Face Hub. After installing the necessary libraries and upgrading the transformers library, you can install the model and start querying using the provided code snippet.

7. Interactive Chat Interface: For an interactive chat interface, you can wrap the model inside Gradio. Install Gradio and run the provided code to create a demo of the Gradio app and LLaMA in action.

8. Running LLaMA through Ollama: For Linux/MacOS users, Ollama is recommended for running LLaMA models locally. You can use the CLI command `ollama run llama3` or the API command `curl -X POST http://localhost:11434/api/generate -d '{ "model": "llama3", "prompt":"Why is the sky blue?" }'` to interact with the model.

9. Quantization for Reduced Model Size: If necessary, you can reduce the size of the LLM models while maintaining performance by quantizing the model's parameters, which can result in a significant reduction in model size.