Anyone interested in using artificial intelligence to scour through boring company documents and annual reports might be interested in a new version of Llama Banker which has been built by developer Nicholas Renotte using LLaMA 2 70B running on a single GPU.
Llama Banker is a new tool has emerged that promises to revolutionize the way we analyze company documents and annual reports. This tool, known as Llama Banker, was ingeniously crafted using LLaMA 2 70B running on one GPU. To bring this innovative tool to life, Renotte had to install Pytorch and other dependencies. He faced challenges in running the entire app on run pod and had to install additional dependencies including Lang chain Transformers.
For those of you wondering what Llama 2 is, Llama 2 is a groundbreaking open-source Large Language Model (LLM) presented by Meta. Central to Meta’s vision of fostering openness in the realm of artificial intelligence, Llama 2 is made freely available to a diverse audience, encompassing individual enthusiasts, established researchers, creators, and businesses alike. This gesture is seen as an earnest endeavor to stimulate widespread testing, thereby driving innovation and continuous enhancement in the field of AI.
Running LLaMA 2 70B running on a single GPU
Renotte’s creation, Llama Banker, is an open-source retrieval augmented generation engine that has been built using the Llama 270b model. This powerful engine is capable of answering questions, summarizing, and analyzing a 300-page annual reports, all while running on a single GPU.
“Doing RAG for Finance using LLama2. Highly recommend you run this in a GPU accelerated environment. I used a A100-80GB GPU on Runpod for the video!”
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Renotte utilized Auto tokenizer, auto model for causal LM, and a new class text streamer to integrate the model. He encountered issues in using the original Meta weights and had to apply for access on the Meta website. To convert raw input strings to unique numerical identifiers, Renotte employed the Transformer class. He faced issues with the documentation code and had to find a solution on GitHub.
The versatility of Llama 2 is evident in its array of pretrained models, spanning a wide spectrum from 7 billion to a staggering 70 billion parameters. A notable member of this suite is the Llama-2-chat, meticulously fine-tuned to excel in dialogue scenarios. At the heart of its training process lies a trove of publicly accessible online data. Moreover, to refine its capabilities, Llama-2-chat underwent a rigorous supervised fine-tuning regimen. Further sophistication was infused using Reinforcement Learning from Human Feedback (RLHF), leveraging state-of-the-art techniques such as rejection sampling and proximal policy optimization (PPO).
In terms of sheer performance, Llama 2 and its various iterations tower above many contemporary open-source chat models. Comprehensive evaluations vouch for their helpfulness and safety, making them potential replacements even for some closed-source alternatives. By releasing Llama 2, Meta’s aspiration is not merely to showcase a piece of advanced technology. Instead, the objective is to arm developers with a potent instrument, catalyzing AI-powered projects that resonate with experimentation, boundless innovation, and the ethical scaling of pioneering ideas.
The very essence of Llama 2’s release resonates deeply with Meta’s overarching commitment to an open AI ecosystem. This perspective emphasizes the symbiotic collaboration among a vast community of developers and researchers, all striving for shared advancements. To culminate, Meta’s clarion call beckons users from all walks of life to access, download, and harness the boundless potential of Llama 2, underscoring its readiness to shape myriad applications in the AI landscape.
Llama Banker, built using LLaMA 2 70B running on a single GPU, is a game-changer in the world of company and annual report analysis, learn more by checking it out on GitHub. Llama Banker is a testament to the power of artificial intelligence and the potential it holds in transforming the way we work. I highly recommend jumping over to the Nicholas Renotte YouTube channel for more information on how to use the latest AI models to create a wide variety of different applications.
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