This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
A space on Hugging Face showcasing the LLM-Model-VRAM-Calculator, a tool designed to calculate the required VRAM for a specific machine learning model.
Learn how to build an open LLM app using Hermes 2 Pro, a powerful LLM based on Meta's Llama 3 architecture. This tutorial explains how to deploy Hermes 2 Pro locally, create a function to track flight status using FlightAware API, and integrate it with the LLM.
Cerebrum 8x7b is a large language model (LLM) created specifically for reasoning tasks. It is based on the Mixtral 8x7b model. Similar to its smaller version, Cerebrum 7b, it is fine-tuned on a small custom dataset of native chain of thought data and further improved with targeted RLHF (tRLHF), a novel technique for sample-efficient LLM alignment. Unlike numerous other recent fine-tuning approaches, our training pipeline includes under 5000 training prompts and even fewer labeled datapoints for tRLHF.
Native chain of thought approach means that Cerebrum is trained to devise a tactical plan before tackling problems that require thinking. For brainstorming, knowledge intensive, and creative tasks Cerebrum will typically omit unnecessarily verbose considerations.
- 14 free colab notebooks providing hands-on experience in fine-tuning large language models (LLMs).
- The notebooks cover topics from efficient training methodologies like LoRA and Hugging Face to specialized models such as Llama, Guanaco, and Falcon.
- They also include advanced techniques like PEFT Finetune, Bloom-560m-tagger, and Meta_OPT-6–1b_Model.
efficient method for fine-tuning LLM using LoRA and QLoRA, making it possible to train them even on consumer hardware
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