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Add InternVideo2-CLIP-anomaly-detection release files (TAO fine-tune, research release) (#2)
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# TAO video_clip model block for this release.
# model.safetensors (LoRA merged): build VideoCLIPPlModel with this config as-is (peft disabled) and load_state_dict(strict=True).
# model.pth (LoRA adapters unmerged, Lightning checkpoint): set peft.enabled: true and peft.vision.enabled: true, then
# VideoCLIPPlModel.load_from_checkpoint('model.pth', experiment_spec=cfg).
# model.text_encoder = Apple MobileCLIP-B-LT weights (mobileclip_blt.pt); base vision/alignment weights resolve from
# model.internvideo2clip_hf_id through the Hugging Face cache.
model:
type: internvideo2-clip-l14
adaptor_name: null
freeze_vision_encoder: true
freeze_text_encoder: true
image_size: 224
init_logit_scale: 4.605170185988092
init_logit_bias: 0.0
canonicalize_text: false
pretrained_ckpt: null
internvideo2clip_hf_id: OpenGVLab/InternVideo2_distillation_models
vision_encoder: null
text_encoder: /models/mobileclip_blt.pt
clip_head: null
num_frames: 8
use_flash_attn: false
use_fused_rmsnorm: false
use_fused_mlp: false
peft:
enabled: false
method: lora
vision:
enabled: false
target_modules:
- qkv
- proj
num_last_blocks: 12
rank: 8
alpha: 16
dropout: 0.05
text:
enabled: false
target_modules:
- qkv_proj
- out_proj
num_last_blocks: 3
rank: 8
alpha: 16
dropout: 0.05
regularization:
enabled: false