Language Models
Showing 17 of 17 projects
A framework for programming language models with Python instead of prompting, enabling modular AI systems with automatic prompt optimization.
A comprehensive library for post-training foundation models using reinforcement learning and fine-tuning techniques.
An open-source offline translation library and toolkit written in Python, supporting over 30 languages via downloadable language models.
A renderer and parser for the harmony response format used with OpenAI's gpt-oss open-weight models.
Generate personalized Twitter bios using AI models like Qwen 3.5 9B and GPT OSS 20B with streaming responses.
A method to steer topic and attributes of GPT-2 language models without fine-tuning, enabling controlled text generation.
A long-range genomic foundation model that processes DNA sequences up to 1 million nucleotides at single nucleotide resolution.
Open-source language models and tools for protein engineering and design using AI.
A video-language understanding framework that treats video narration as vocabulary and videos as long documents for efficient analysis.
A command-line tool for holistic comparison and error analysis of language generation systems like machine translation and summarization.
A curated list of resources for Biomedical Information Extraction (BioIE), including datasets, tools, libraries, and research.
A Rust implementation of OpenAI's tiktoken tokenizer for working with GPT models and token counting.
A curated list of resources for Natural Language Processing (NLP) in Polish, including datasets, models, and tools.
A curated list of free tools, datasets, models, and resources for Hungarian Natural Language Processing.
A community-curated list of NLP tools, libraries, datasets, and resources across speech processing, text analysis, and machine translation.
A curated collection of open-source libraries, models, datasets, and tools for Natural Language Processing (NLP) in Norwegian.
A Java library for integrating with AI models like ChatGPT, DALL·E, and Cohere using minimal code.
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