Commit Graph

146 Commits (62f47fb844cff587d5effd54dedf05c2cbb5ac9d)

Author SHA1 Message Date
MiniMind-V User 62f47fb844 [fix] LoRA/RAG: correct target modules, vlm_checkpoint call, and gitignore
- lora_utils.py: fix target_modules to match MiniMind arch (q/k/v/o_proj
  instead of GPT-2 style c_attn/c_proj/c_fc)
- train_sft_vlm_lora.py: fix broken vlm_checkpoint call in train_epoch
  (was passing model/epoch/step/args instead of vlm_config); now saves
  inline like train_sft_vlm.py; remove unused SkipBatchSampler import
- .gitignore: add __pycache__, .claude/, dataset/sft_vlm.parquet
2026-03-06 04:49:40 +00:00
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
jingyaogong 56ab6ebd13 [update] readme 2026-02-05 00:23:20 +08:00
jingyaogong 33db76fe10 [update] dataset name 2026-02-05 00:22:58 +08:00
jingyaogong b28715e6fd [update] vlm params 2026-02-04 16:41:17 +08:00
jingyaogong 7e094afa59 [update] shuffle data 2026-01-18 17:02:24 +08:00
jingyaogong 326b24727e [update] sync minimind 2026-01-15 21:51:58 +08:00
jingyaogong d2bdff3269 [feat] test parquet 2025-12-27 10:56:07 +08:00
jingyaogong fef284776f [feat] delete images 2025-12-27 10:53:38 +08:00
jingyaogong 3684285a91 [feat] support parquet data 2025-12-27 10:49:50 +08:00
jingyaogong 1863743ee9 [feat] update readme 2025-12-27 10:48:54 +08:00
jingyaogong 32cf4c5c01 [fix] add gc 2025-12-27 01:09:33 +08:00
jingyaogong b7cb98c136 [fix] reduce aux_loss_alpha 2025-12-05 23:07:55 +08:00
jingyaogong 13c3a2c093 [fix] cuda memory 2025-12-01 16:35:05 +08:00
jingyaogong e03af46d12 [feat] update yarn 2025-12-01 16:34:00 +08:00
jingyaogong a157b66ace [feat] del cuda memory 2025-11-27 19:37:39 +08:00
jingyaogong e613613a13 [feat] update model 2025-11-27 19:36:41 +08:00
jingyaogong d698d98dc5 [feat] update readme 2025-11-11 11:12:09 +08:00
jingyaogong 92ca9959bb [feat] update readme 2025-10-30 23:32:12 +08:00
jingyaogong ca15b0440f [feat] update readme 2025-10-30 23:31:19 +08:00
jingyaogong 2e548ba08e [feat] update datasets 2025-10-30 11:08:30 +08:00
jingyaogong 25d25383e7 [feat] update requirements 2025-10-30 11:06:20 +08:00
jingyaogong 7605850e1d [feat] update args 2025-10-30 10:50:54 +08:00
jingyaogong 88b0cd89ea [feat] update readme 2025-10-29 12:13:44 +08:00
jingyaogong 778aeb2946 [feat] update trainer 2025-10-28 23:34:16 +08:00
jingyaogong 2ce5c31170 [feat] update sft 2025-10-26 21:24:19 +08:00
jingyaogong c2bfead000 [feat] update readme 2025-10-26 20:55:02 +08:00
jingyaogong 54fcbb04c8 [feat] update eval-vlm 2025-10-26 20:54:37 +08:00
jingyaogong aebc6fb191 [feat] pause-training 2025-10-26 20:54:13 +08:00
jingyaogong 4c3b5bacbf [fix] 1024-bugs 2025-10-24 00:35:56 +08:00
jingyaogong 094f88a087 [feat] update readme 2025-10-22 10:58:52 +08:00
jingyaogong 8bed2ec00b [feat] update readme 2025-10-21 22:53:00 +08:00
jingyaogong 974a666741 [feat] update readme 2025-10-17 00:04:48 +08:00
jingyaogong 4d2accbcef [feat] update images 2025-10-17 00:03:58 +08:00
jingyaogong 58dfb9779c [feat] update minimind model 2025-10-17 00:03:13 +08:00
jingyaogong a8572190a8 update eval 2025-04-27 18:38:51 +08:00
jingyaogong 623a130c34 250427 2025-04-27 18:13:22 +08:00
jingyaogong 26de815253 update mask 2025-04-10 22:36:34 +08:00
jingyaogong 5f130d142d moe note 2025-04-09 17:37:51 +08:00
jingyaogong 139cc98bb9 moe note 2025-04-09 17:37:06 +08:00
jingyaogong bb0ec7a420 update readme 2025-04-09 17:03:13 +08:00
jingyaogong 6f00fc7954 update dataset path 2025-04-03 17:55:43 +08:00
jingyaogong 9f1d67246b update readme 2025-04-01 17:45:55 +08:00
jingyaogong 5c7ac3a090 update structure image 2025-04-01 16:31:54 +08:00
gongjy c1a069b532 fix a bug 2025-02-23 18:59:35 +08:00
gongjy 694ce87e39 update readme 2025-02-20 23:47:34 +08:00
gongjy 38a84c2dd2 update minimind2-v 2025-02-20 23:38:48 +08:00
gongjy 62e999fbda update minimind2-v 2025-02-20 23:37:00 +08:00
gongjy 60b265d01c update minimind2-v 2025-02-20 23:34:51 +08:00
gongjy 61a4468bab update readme 2025-02-10 20:45:41 +08:00