model-training
A quick guide (especially) for trending instruction finetuning datasets
Tuning LLMs with no tears💦; Sample Design Engineering (SDE) for more efficient downstream-tuning.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Example models using DeepSpeed
Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"
An Open-Source Framework for Prompt-Learning.
Large-scale, Informative, and Diverse Multi-round Chat Data (and Models)
methods2test is a supervised dataset consisting of Test Cases and their corresponding Focal Methods from a set of Java software repositories
H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/
LLM training code for Databricks foundation models
Full description can be found here: https://discuss.huggingface.co/t/pretrain-gpt-neo-for-open-source-github-copilot-model/7678?u=ncoop57
Plain pytorch implementation of LLaMA
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3, Qwen3-MoE, DeepSeek-R1, GLM4.5, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM4.5v, Llava, …
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
A dataset template for guiding chat-models to self-cognition, including information about the model’s identity, capabilities, usage, limitations, etc.
Praetor is a lightweight finetuning data and prompt management tool