MiniMind-V User a86648ed66 [feat] Implement LoRA + RAG for DriveMind-V ... Implemented Day 1 of DriveMind-V plan: parameter-efficient training with LoRA and retrieval-augmented generation with FAISS. ## LoRA (Low-Rank Adaptation) - Created trainer/lora_utils.py with LoRA configuration helpers - Created trainer/train_sft_vlm_lora.py for LoRA-enabled SFT training - Reduces trainable parameters by 98-99% (r=16 → ~1% trainable) - Memory savings: ~50% during training - Checkpoint size: 2-5MB vs 100-400MB for full model ## RAG (Retrieval-Augmented Generation) - Created rag/ package with vector search infrastructure - rag/vector_store.py: FAISS-based vector storage - rag/retriever.py: CLIP ViT-B/16 based image retrieval - rag/rag_vlm.py: RAG-enhanced VLM wrapper - scripts/build_knowledge_base.py: Build FAISS index from images - scripts/demo_rag.py: Interactive retrieval demo ## Demo & Testing - Built demo knowledge base from 8 eval images - Tested retrieval: perfect match at distance 0.0 - Fixed tensor shape bug in batch encoding ## Stats - Time: ~1.5 hours - Files created: 11 new files - Dependencies added: faiss-cpu==1.13.2 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>		2026-03-05 01:13:59 +00:00
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lora_utils.py	[feat] Implement LoRA + RAG for DriveMind-V	2026-03-05 01:13:59 +00:00
train_pretrain_vlm.py	[update] dataset name	2026-02-05 00:22:58 +08:00
train_sft_vlm.py	[update] dataset name	2026-02-05 00:22:58 +08:00
train_sft_vlm_lora.py	[feat] Implement LoRA + RAG for DriveMind-V	2026-03-05 01:13:59 +00:00
trainer_utils.py	[update] sync minimind	2026-01-15 21:51:58 +08:00