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zihan-aiml/
vismem-novis-sft-qwen7b

Name: vismem-novis-sft-qwen7b
Availability: InStock

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VisMemVideo NoVis SFT Checkpoint

Model: Qwen2.5-VL-7B-Instruct + VisMemVideo (QueryBuilder + CrossAttentionFusion + LoRA Memory Former)

Architecture

Reasoner: Frozen Qwen2.5-VL-7B-Instruct (provides visual context H)
Memory Former: Separate Qwen2.5-VL-7B + LoRA adapter (trainable)
Video Memory Bank: QwenVideoMemoryBank (reuses reasoner ViT, 3584-dim)
QueryBuilder: 1-layer transformer encoder (8 heads, ff_mult=2)
CrossAttentionFusion: 2-layer cross-attention (d_inner=512, 8 heads)
Training mode: NoVis SFT (text-only input with memory augmentation, visual info only through memory tokens)

Training Config

Parameter	Value
Dataset	Video-R1-COT-10k-video-only-4k (4000 samples)
Epochs	2
Effective batch size	16 (4 GPU × bs2 × grad_accum 2)
Learning rate	1e-5 (custom params: 5e-5)
Scheduler	Cosine with 3% warmup
invoke_prob	0.5
query_len / mem_len	8 / 8
LoRA	r=16, alpha=32, targets=q,k,v,o_proj
Trainable params	134M (QB: 110M, CA: 14M, LoRA: 10M)

Training Results

Metric	Value
Final loss	0.912
Average loss	0.972
Ref loss (no memory)	~1.14
Final mem_lift	+0.19
Training time	2h 8min (4× GPU)

Checkpoints

epoch1/ — Checkpoint at step 250 (end of epoch 1)
epoch2/ — Checkpoint at step 500 (end of epoch 2, final)

Loading

python

from open_r1.vismem_video_model import load_vismem_video_checkpoint

# Point to epoch1/ or epoch2/ directory
load_vismem_video_checkpoint(model, "path/to/epoch2")

Or via CLI argument:

--latent_checkpoint path/to/epoch2

zihan-aiml/vismem-novis-sft-qwen7b