This page showcases the diverse collection of machine learning models and datasets provided by Arcee AI on Hugging Face. The collections include the advanced Trinity family of models, such as Trinity-Large-Thinking and Trinity-Mini, designed for various text generation tasks. Additionally, the repository features specialized datasets like Teacher Logits for distillation, the AFM 4.5B series, and various quantized flagship models like Virtuoso and SuperNova. These resources cater to researchers and developers looking for high-performance, specialized AI models ranging from small-scale nano versions to massive 399B parameter models, supporting tasks like feature extraction and text generation.
This collection, curated by prism-ml, features a specialized series of 1-bit Bonsai models designed for efficient text generation. The repository includes various model architectures and sizes, such as the 8B, 4B, and 1.7B parameter versions, optimized through extreme quantization. Available in formats like GGUF and MLX-1bit, these models are highly compressed to maximize performance while minimizing the computational footprint. This makes them ideal for running large language model tasks on hardware with limited resources. The collection serves as a hub for exploring the potential of ultra-compact, highly compressed models in the evolving landscape of machine learning and efficient inference.
Google Sheets now allows users to generate text, summarize information, and categorize data using Gemini AI directly in cells. The feature supports text generation, summarization, categorization, and sentiment analysis with optional data ranges.
This page details the DeepSeek-R1-0528-Qwen3-8B model, a quantized version of DeepSeek-R1-0528, highlighting its improved reasoning capabilities, evaluation results, usage guidelines, and licensing information. It offers various quantization options (GGUF) for local execution.
Mercury dLLMs are up to 10x faster and cheaper than current LLMs, offering high-quality text generation with improved reasoning and error correction.
Steer LLM outputs towards a certain topic/subject and enhance response capabilities using activation engineering by adding steering vectors, now in oobabooga text generation webui!
An extension for Oobabooga's Text-Generation Web UI that retrieves and adds web content to the context of prompts for more informative AI responses.
This article explains what temperature is in the context of language models, how it works, its relationship to beam search, and how output generation can still go haywire despite these techniques.
