{"library":"ms-swift","title":"ms-swift","description":"A scalable lightweight infrastructure for fine-tuning large language models (LLMs), vision-language models (VLMs), and embedding models. Supports LoRA, QLoRA, full fine-tuning, and reinforcement learning methods like DPO, GRPO, and PPO. Version 4.1.3 is the latest, with releases every few weeks.","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install ms-swift","pip install 'ms-swift[llm]'","pip install 'ms-swift[all]'"],"cli":{"name":"swift","version":""}},"imports":["from swift.trainers import SwiftTrainer","from swift.utils import get_dataset","from swift import LoRAConfig"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from swift import Swift, SwiftModel, LoRAConfig\nimport os\n\n# Load a pre-trained model and apply LoRA\nmodel = SwiftModel.from_pretrained(\"Qwen/Qwen2.5-1.5B\")\nlora_config = LoRAConfig(r=8, lora_alpha=32, target_modules=[\"q_proj\", \"v_proj\"])\nmodel = Swift.prepare_model(model, lora_config)\n\n# Quick training snippet (simplified)\nfrom swift.trainers import Seq2SeqTrainer\nfrom swift.utils import get_dataset\ntrain_dataset = get_dataset(\"json\", data_files=\"train.jsonl\")\ntrainer = Seq2SeqTrainer(model=model, train_dataset=train_dataset)\ntrainer.train()","lang":"python","description":"Basic LoRA fine-tuning with Qwen2.5. Demonstrates correct imports and usage.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}