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HiteshSaai/arxiv_research-cat-classifier-adapter
arxiv_research-cat-classifier-adapter is a machine learning model from HiteshSaai. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct. It has been trained using TRL.
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Updated Feb 1, 2026
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From the Hugging Face model README
This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct. It has been trained using TRL.
Give title and the abstract of any research paper and the llama model should be able to correctly classify their category
To avoid any dependency issue with torchvision, run the following command in terminal
pip uninstall -y torch torchvision torchaudio
pip install --no-cache-dir torch torchvision torchaudio \
--index-url https://download.pytorch.org/whl/cu118
# Load model directly
from transformers import AutoModel
device_type = 'cuda'
model = AutoModel.from_pretrained("HiteshSaai/arxiv_research-cat-classifier-adapter", dtype="auto")
input_sequence = {'content': 'TITLE: Beyond the Finite Variant Property: Extending Symbolic Diffie-Hellman Group Models (Extended Version) \n Abstract: Diffie-Hellman groups are commonly used in cryptographic protocols. While most state-of-the-art, symbolic protocol verifiers support them to some degr \n\n Task: Classify this paper into one research category.\nAnswer with only the category name.',
'role': 'user'}
prompt = tokenizer.apply_chat_template(input_sequence, tokenize=False, add_generation_prompt=True)
tokenized_input = tokenizer(prompt, return_tensors='pt', add_special_tokens=False).to(device_type)
out = model.generate(**tokenized_input, max_new_tokens=5)
response = tokenizer.batch_decode(out, skip_special_tokens=False)
classified_output = response[0][len(prompt):]
This model was trained with SFT.
Cite TRL as:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}