Issue 30: NVIDIA's Llama 3.1 Fine-Tune Outperforms GPT-4o and Claude 3.5? - October 21, 2024

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This week's AI newsletter covers groundbreaking advancements in LLMs, from NVIDIA's surprising Llama 3.1 fine-tune surpassing GPT-4o and Claude 3.5 to efficient quantization techniques and novel model architectures.

News

NVIDIA's Llama 3.1 Nemotron 70B Achieves State-of-the-Art Performance

NVIDIA quietly released Llama 3.1 Nemotron 70B Instruct, a further RLHFed model, achieving state-of-the-art performance on several benchmarks. This model surpasses GPT-4o and Claude 3.5 Sonnet on benchmarks like Arena Hard and AlpacaEval 2 LC. The release also includes a corresponding reward model on RewardBench.

Zyphra Releases Zamba2, a 7B LLM with Hybrid Architecture

Zyphra introduced Zamba2, a new 7B LLM that leverages a hybrid SSM-attention architecture, outperforming Meta Llama 3.1, Google Deepmind Gemma 2, and Mistral AI. They also released their 5T token pertaining dataset, Zyda-2, and a demo on Hugging Face Spaces. This model offers improved speed and throughput compared to pure transformer models.

Local GGUF Model Execution with Ollama

Hugging Face now supports seamless integration of GGUF models with Ollama, simplifying local execution. This allows you to run any GGUF model directly from the Hugging Face Hub, facilitating local experimentation with models like Llama 3.2 3B.

Research

Self-Taught Reasoners: An Iterative Approach to Enhanced Reasoning

The Self-Taught Reasoner (STaR) utilizes an iterative approach for improved reasoning capabilities in LLMs. By iteratively generating, correcting, and fine-tuning rationales, STaR achieves performance comparable to much larger models on tasks like GSM8K and CommonsenseQA.

Efficient Process Reward Models with Progress-Based Feedback

Google's new approach to Process Reward Models (PRM) focuses on progress-based feedback, reducing the reliance on large labeled datasets. This method improves data efficiency and accuracy by leveraging likelihood improvements after each reasoning step and employing a "prover" LLM.

Quantization's Minimal Impact on LLM Performance

A new study by NeuralMagic demonstrates that quantization has minimal impact on LLM performance while offering significant benefits in inference speed and model size. The research reveals that quantized models retain high accuracy while achieving notable speedups and size reductions.

General

Deploying Llama 3.2 Vision on AWS with Hugging Face TGI

A new guide simplifies the deployment of Llama 3.2 Vision models on Amazon SageMaker using the Hugging Face Text Generation (TGI) container. This allows for optimized inference with flash attention and seamless switching to open models via an OpenAI-compatible API.

Deploying Llama 3.2 Vision on Google Cloud

Two new tutorials demonstrate how to deploy Llama 3.2 Vision on Google Kubernetes Engine and Google Cloud Vertex AI with the Hugging Face TGI container. These tutorials guide you through deploying various model sizes, leveraging the OpenAI-compatible API, and optimizing for efficient inference.


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