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Tushar9802
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Tushar9802
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mistralai/Mistral-7B-v0.1
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| Metric | Baseline | Phase 2 (this) | Phase 3 |
|---|---|---|---|
| Video ROUGE-1 | 0.263 | 0.381 (+45%) | 0.417 (+58%) |
| Video ROUGE-2 | 0.032 | 0.101 (+216%) | 0.119 (+272%) |
| Video PVR | 9.9% | 10.2% (near baseline) | 14.1% |
| CMC | 0.356 | 0.473 | 0.531 |
| Paper ROUGE-1 | 0.320 | 0.296 | 0.309 |
Key trade-off: Phase 2 has the cleanest style (10.2% PVR) at the cost of ~9% less video R-1 than Phase 3.
python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelimport torch bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True,)model = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", quantization_config=bnb_config, device_map="auto",)model = PeftModel.from_pretrained(model, "Tushar9802/hybrid-summariser-phase2-lora")tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") prompt = "Summarize the following text.\n\nText: {your_text}\n\nSummary:"inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024).to("cuda")outputs = model.generate(**inputs, max_new_tokens=256, num_beams=4, no_repeat_ngram_size=3)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))| Phase | Data Mix | Methods | Status |
|---|---|---|---|
| 1 | 100% papers | LoRA+ (ratio 8x) | completed |
| 2 (this) | 50P/40V/10Pr | +OPLoRA (k=16), +EWC (lambda=200) | this checkpoint |
| 3 | 30P/60V/10Pr | +CrossCLR, EWC lambda=400 | Phase 3 adapter |
4,324 CS samples: 2,368 arXiv papers + 738 YouTube lectures + 1,218 SBERT-mined cross-modal pairs.
| Use Case | Recommended |
|---|---|
| Balanced paper + video quality | Phase 2 |
| Minimal style contamination | Phase 2 (PVR 10.2%) |
| Maximum video summarization quality | Phase 3 (R-1 0.417) |
| Maximum cross-modal consistency | Phase 3 (CMC 0.531) |
bibtex
@inproceedings{jaju2025crossmodal, title={Cross-Modal Transfer Learning in Domain-Adaptive Video Summarization}, author={Jaju, Tushar and Saharawat, Tanishka and Bhatia, Shruti and Rastogi, Shivansh}, booktitle={Proc. IMPACT 2025}, publisher={Springer}, year={2025}}Tushar Jaju (training infrastructure, implementation, experiments), Tanishka Saharawat, Shruti Bhatia, Shivansh Rastogi
Guide: Dr. Neha Yadav — ABES Engineering College, Ghaziabad (AKTU)
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelimport torch bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True,)model = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", quantization_config=bnb_config, device_map="auto",)model = PeftModel.from_pretrained(model, "Tushar9802/hybrid-summariser-phase2-lora")tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") prompt = "Summarize the following text.\n\nText: {your_text}\n\nSummary:"inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024).to("cuda")outputs = model.generate(**inputs, max_new_tokens=256, num_beams=4, no_repeat_ngram_size=3)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))@inproceedings{jaju2025crossmodal, title={Cross-Modal Transfer Learning in Domain-Adaptive Video Summarization}, author={Jaju, Tushar and Saharawat, Tanishka and Bhatia, Shruti and Rastogi, Shivansh}, booktitle={Proc. IMPACT 2025}, publisher={Springer}, year={2025}}