lm-human-preferences
language model tuning
Training methods and tools for fine-tuning language models using human preferences
Code for the paper Fine-Tuning Language Models from Human Preferences
1k stars
23 watching
164 forks
Language: Python
last commit: over 1 year ago Related projects:
Repository | Description | Stars |
---|---|---|
| This project provides code and model for improving language understanding through generative pre-training using a transformer-based architecture. | 2,167 |
| Provides pre-trained language models and tools for fine-tuning and evaluation | 439 |
| A repository providing tools and datasets to fine-tune language models for specific tasks | 1,484 |
| Implementing OpenAI's transformer language model in PyTorch with pre-trained weights and fine-tuning capabilities | 1,511 |
| A framework for training and fine-tuning multimodal language models on various data types | 601 |
| A guide to using pre-trained large language models in source code analysis and generation | 1,789 |
| Automates model building and deployment process by optimizing hyperparameters and compressing models for edge computing. | 200 |
| A lightweight, multilingual language model with a long context length | 920 |
| Training and deploying large language models on computer vision tasks using region-of-interest inputs | 517 |
| An open-source implementation of a vision-language instructed large language model | 513 |
| Library that provides a unified API to interact with various Large Language Models (LLMs) | 367 |
| This project demonstrates the effectiveness of reinforcement learning from human feedback (RLHF) in improving small language models like GPT-2. | 214 |
| Trains a large Chinese language model on massive data and provides a pre-trained model for downstream tasks | 230 |
| Generates reviews and discovers sentiment using a language model | 1,512 |
| Provides pre-trained binary models for natural language text processing across multiple languages | 4 |