Video-Text-to-Text
PEFT
llava
video-understanding
long-video
visual-question-answering
lora
qwen2
cvpr2026
Instructions to use Fujitsu/QViC-MF-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Fujitsu/QViC-MF-7B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lmms-lab/LLaVA-Video-7B-Qwen2") model = PeftModel.from_pretrained(base_model, "Fujitsu/QViC-MF-7B") - Notebooks
- Google Colab
- Kaggle
Download adapter_config.json from Fujitsu/QViC-MF-7B: direct link, hf CLI and curl.
- Browser
- Download file 535 Bytes
-
https://huggingface.co/Fujitsu/QViC-MF-7B/resolve/main/adapter_config.json
- Command line
-
hf download hf://Fujitsu/QViC-MF-7B/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/Fujitsu/QViC-MF-7B/resolve/main/adapter_config.json
535 Bytes
| { | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "lmms-lab/LLaVA-Video-7B-Qwen2", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "lora_alpha": 16, | |
| "lora_dropout": 0.05, | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "r": 64, | |
| "revision": null, | |
| "target_modules": "encoder\\.model\\.layers\\.\\d+\\.(mlp\\.(up_proj|gate_proj|down_proj)|self_attn\\.(q_proj|v_proj|k_proj|o_proj))", | |
| "task_type": "CAUSAL_LM" | |
| } |