This is a full-parameter fine-tuned checkpoint of google/t5gemma-2-270m-270m for SciHigh-2026 Task 1. It generates concise research highlights from scientific paper abstracts.

Model Details

Parameters: 786M trainable
Framework: Transformers 5.14.1, PyTorch 2.13.0+cu130
License: Must be used in accordance with upstream Gemma terms.
Training Data: 10,000 examples (SciHigh Task 1 MixSub-SciHigh split)
Hyperparameters: 3 epochs, lr 3e-5, batch size 32, max input/target lengths 1024/320, bfloat16, SDPA attention.

Validation Results (1,985 examples)

Metric	Score
ROUGE-1	0.3818
ROUGE-2	0.1389
ROUGE-L / Lsum	0.2590 / 0.2591
METEOR	0.2983
BERTScore F1	0.8775

Inference

python

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "uthayamurthy/origin-task1-t5gemma2-270m"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForSeq2SeqLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    attn_implementation="sdpa",
).to("cuda").eval()

abstract = "..."
inputs = tokenizer(abstract, return_tensors="pt", truncation=True, max_length=1024)
inputs = {name: value.to("cuda") for name, value in inputs.items()}

with torch.inference_mode():
    generated = model.generate(
        **inputs,
        max_length=128,
        min_length=0,
        num_beams=4,
        length_penalty=1.0,
        no_repeat_ngram_size=3,
        early_stopping=True,
    )
print(tokenizer.decode(generated[0], skip_special_tokens=True))