huggingface / large_language_model_training_playbook
An open collection of implementation tips, tricks and resources for training large language models
☆460Updated last year
Related projects ⓘ
Alternatives and complementary repositories for large_language_model_training_playbook
- An open collection of methodologies to help with successful training of large language models.☆462Updated 9 months ago
- NeurIPS Large Language Model Efficiency Challenge: 1 LLM + 1GPU + 1Day☆252Updated last year
- Scaling Data-Constrained Language Models☆321Updated last month
- Build, evaluate, understand, and fix LLM-based apps☆485Updated 10 months ago
- ☆685Updated last month
- Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization)☆457Updated 2 years ago
- ☆451Updated 3 weeks ago
- Manage scalable open LLM inference endpoints in Slurm clusters☆236Updated 4 months ago
- Reverse Instructions to generate instruction tuning data with corpus examples☆206Updated 8 months ago
- Extend existing LLMs way beyond the original training length with constant memory usage, without retraining☆675Updated 7 months ago
- Code for fine-tuning Platypus fam LLMs using LoRA☆623Updated 9 months ago
- Open Instruction Generalist is an assistant trained on massive synthetic instructions to perform many millions of tasks☆206Updated 10 months ago
- Expanding natural instructions☆959Updated 11 months ago
- A set of scripts and notebooks on LLM finetunning and dataset creation☆93Updated last month
- Central place for the engineering/scaling WG: documentation, SLURM scripts and logs, compute environment and data.☆980Updated 3 months ago
- batched loras☆336Updated last year
- Crosslingual Generalization through Multitask Finetuning☆516Updated last month
- Code repository for supporting the paper "Atlas Few-shot Learning with Retrieval Augmented Language Models",(https//arxiv.org/abs/2208.03…☆517Updated 11 months ago
- Code used for sourcing and cleaning the BigScience ROOTS corpus☆306Updated last year
- OpenICL is an open-source framework to facilitate research, development, and prototyping of in-context learning.☆541Updated last year
- This project studies the performance and robustness of language models and task-adaptation methods.☆141Updated 6 months ago
- Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"☆431Updated last year
- Fast & Simple repository for pre-training and fine-tuning T5-style models☆970Updated 3 months ago
- DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection and Instruction-Aware Models for Conversational AI☆478Updated 6 months ago
- Implementation of the specific Transformer architecture from PaLM - Scaling Language Modeling with Pathways☆821Updated 2 years ago
- Easily embed, cluster and semantically label text datasets☆462Updated 7 months ago
- ☆191Updated 9 months ago
- Used for adaptive human in the loop evaluation of language and embedding models.☆304Updated last year
- Organize your experiments into discrete steps that can be cached and reused throughout the lifetime of your research project.☆534Updated 5 months ago