torchrun --nproc_per_node=8 train_VQA_diflr.py
--model_id HayatoHongo/lfm2-vl-ja-finetuned-enmt1ep-jamt10eponall
--chat_json /workspace/Japanese_VQA_192642.jsonl
--image_folder /workspace/images
--output_dir /workspace/output
--epochs 1
--lr 1e-5
--text_lr_scale 0.1
--per_device_train_batch_size 16
--gradient_accumulation_steps 1
--bf16
--tf32
--report_to wandb
--logging_steps 20
--seed 42

Model Card for output

This model is a fine-tuned version of HayatoHongo/lfm2-vl-ja-finetuned-enmt1ep-jamt10eponall. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.22.2
Transformers: 4.55.0
Pytorch: 2.8.0+cu126
Datasets: 4.0.0
Tokenizers: 0.21.4

Citations

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 Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}