Nvidia recently released an open-source AI model built on Meta’s Llama-3 called “Nemotron” that surpasses OpenAI’s GPT-4o on many popular benchmarks.

Nvidia’s new open-source AI model beats GPT-4o on benchmarks

Expanding the open-source Meta Llama collection of models, the Llama 3.2 collection includes vision language models (VLMs), small language models (SLMs), and an updated Llama Guard model with support…

Deploying Accelerated Llama 3.2 from the Edge to the Cloud | NVIDIA Technical Blog

Gemma chatbots can run locally, and they reportedly outperform Meta&#x27;s Llama 2.

Google goes “open AI” with Gemma, a free, open-weights chatbot family | Ars Technica

Google AI—a division of the search giant—unveiled Gemma today, a new family of open-source language models derived from its recently released Gemini suite of AI tools. With Gemma, Google is directly challenging the leading position of LLMs based on Meta’s LLaMa and Mistral.

Google Has a New Open-Source AI Model—Can It Compete With Meta's Llama-2? - Decrypt

Google has unveiled Gemma, a new family of open models, leveraging the research and technology behind the existing Gemini models.

Google Challenges Meta’s Llama 2 with Lightweight Open Source LLM, Gemma

A serial entrepreneur and 0-1 problem ... with Llama. An IIT Kanpur alum, Krishan has the experience of building and scaling tech products from the ground up. At his previous venture, Krishan was responsible for product, strategy, growth, building the team and keeping their heads up in every situation. The startup was eventually acquired by a leading company in the ...

About Us | Llama

The Llama Company was an investment bank founded by Alice Walton as a subsidiary of Walton Enterprises. It was headquartered in Fayetteville, Arkansas, and was founded in 1988, and was engaged in corporate finance, public and structured finance, real estate finance and sales and trading.

Llama Company - Wikipedia

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Discover how large language models are transforming retrieval systems with advanced techniques like RepLLaMA and RankLLaMA

Fine-Tuning LLaMA for Multi-Stage Text Retrieval: Conclusion, Acknowledgements and References

Explore the evolution of large language models from BERT to LLaMA and their impact on multi-stage text retrieval pipelines.

Related Work on Fine-Tuning LLaMA for Multi-Stage Text Retrieval

Explore the impact of fine-tuning methods like LoRA versus full fine-tuning on RepLLaMA's effectiveness in passage retrieval.

Fine-Tuning LLaMA for Multi-Stage Text Retrieval: Ablation Study and Analysis

Explore how RepLLaMA and RankLLaMA models perform in multi-stage text retrieval experiments on MS MARCO datasets

Fine-Tuning LLaMA for Multi-Stage Text Retrieval: Experiments

Discover how LLaMA models revolutionize text retrieval with RepLLaMA and RankLLaMA


Optimizing Text Retrieval Pipelines with LLaMA Models

Discover how fine-tuning LLaMA models enhances text retrieval efficiency and accuracy

Fine-Tuning LLaMA for Multi-Stage Text Retrieval

Comparing Vicuna to alternative LLMs like ChatGPT, LLaMA, and Alpaca. Which is the best AI model for building chat apps?

Vicuna: How Does It Compare to Llama, Alpaca, and ChatGPT?

When should you use LLaMA13b-v2? What about Vicuna? What are their pros and cons?

Comparing LLMs for Chat Applications: Llama v2 Chat vs. Vicuna

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