2 repository-uri
Awesome GitHub RepositoriesPreference Dataset Loaders
Fetching preference datasets from Hugging Face repositories by specifying source name and split for alignment training.
Distinct from Hugging Face: Distinct from Hugging Face model conversion: focuses on loading preference datasets, not converting model formats.
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Awesome Preference Dataset Loaders GitHub Repositories
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- meta-pytorch/torchtune
meta-pytorch/torchtune
5,774Vezi pe GitHubTorchtune is a PyTorch-native library for fine-tuning, aligning, and quantizing large language models. It provides a config-driven system for instantiating components, orchestrating distributed training, and managing parameter-efficient fine-tuning with quantization support, all through YAML-based configurations and command-line overrides. The library distinguishes itself through its comprehensive post-training workflow orchestration, combining supervised fine-tuning, preference optimization (DPO, PPO, GRPO), knowledge distillation, and quantization-aware training in a single configurable pip
Fetches preference datasets from Hugging Face repositories and tokenizes them for alignment training.
Python - pytorch/torchtune
pytorch/torchtune
5,774Vezi pe GitHubTorchtune is a PyTorch-native library for fine-tuning, aligning, and quantizing large language models. It provides a configurable training pipeline orchestrated through YAML recipes, with CLI overrides and component swapping, distributed training via FSDP2, memory optimizations, and parameter-efficient fine-tuning methods like LoRA, DoRA, and QLoRA. The library distinguishes itself through its YAML-driven configuration system that defines all training parameters and instantiates components from config files, with full CLI override capability for any field or component at launch time. It suppo
Fetches preference datasets from Hugging Face repositories for alignment training.
Python
