GIANTS-4B

GIANTS-4B is a language model for insight anticipation from scientific literature, introduced in the paper:

GIANTS: Generative Insight Anticipation from Scientific Literature

Given summaries of two parent papers, GIANTS-4B generates the key insight of a downstream paper that builds on both parent papers. It is fine-tuned from Qwen3-4B.

Training Data

This model was trained on GiantsBench-train.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset

# Load model and tokenizer
model_name = "giants2026/GIANTS-4B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# Load a sample prompt from the GiantsBench test set
dataset = load_dataset("giants2026/GiantsBench-test", split="train")
query = dataset[0]["query"]

# Format as chat messages
messages = [{"role": "user", "content": query}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

# Generate
output = model.generate(
    **inputs,
    max_new_tokens=2048,
    temperature=0.6,
    top_p=0.95,
    top_k=20,
    min_p=0.0,
)

# Decode and print the generated insight
response = tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response)

Evaluation

See GiantsBench-test for the evaluation benchmark.

License

This model is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.