Quality Assurance & Testing
Axolotl
Provides guidance for fine-tuning large language models with Axolotl, covering YAML training configs, LoRA and QLoRA, preference training with DPO, KTO, ORPO and GRPO, multimodal models, FSDP and multi-GPU setups, sequence (context) parallelism, NCCL bandwidth tests, dataset formats, compressed model saving for vLLM and llmcompressor, and custom integrations. Use when writing or debugging an Axolotl YAML config, choosing a dataset format for a fine-tuning run, setting up FSDP or context_parallel_size across GPUs, or running DPO, KTO, ORPO or GRPO training. Use when saving a compressed model for vLLM inference, or when writing a custom Axolotl plugin or integration. Not for inference serving or general Hugging Face Trainer scripts outside Axolotl.
- Publisher / source
- KalarisLabs/research-agent-skills (found on the CSV list)
- Upstream record
- KalarisLabs/research-agent-skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
814f62478d7f0ae20ef14f57ac57cb8c2f35b0ad91e2993b47ae5259edc11fb9