NuExtract is a fine-tuned version of phi-3-mini for information extraction. It requires a JSON template describing the information to extract and an input text. Provides tiny (0.5B) and large (7B) versions.
Hugging Face introduces a unified tool use API across multiple model families, making it easier to implement tool use in language models.
Hugging Face has extended chat templates to support tools, offering a unified approach to tool use with the following features:
- Defining tools: Tools can be defined using JSON schema or Python functions with clear names, accurate type hints, and complete docstrings.
- Adding tool calls to the chat: Tool calls are added as a field of assistant messages, including the tool type, name, and arguments.
- Adding tool responses to the chat: Tool responses are added as tool messages containing the tool name and content.
Hugging Face has extended chat templates to support tools, offering a unified approach to tool use with the following features:
- Defining tools: Tools can be defined using JSON schema or Python functions with clear names, accurate type hints, and complete docstrings.
- Adding tool calls to the chat: Tool calls are added as a field of assistant messages, including the tool type, name, and arguments.
- Adding tool responses to the chat: Tool responses are added as tool messages containing the tool name and content.
DavidAU's model collection on Hugging Face includes various AI and ML models, such as GALAXY-XB, Mini-MOEs, TinyLlama, and Psyonic-Cetacean. These models are designed for text generation, single/multiple LLMs, and automation tasks.
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.
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.
- 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
