TigerLLM-9B-it-LoRA-Hallucination

Model Card for tigerllm_9b_qlora_adapter

This model is a fine-tuned version of md-nishat-008/TigerLLM-9B-it. 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.

Train Result Values

TrainOutput(global_step=27, training_loss=0.7693650877034223, metrics={'train_runtime': 1460.9886, 'train_samples_per_second': 0.552, 'train_steps_per_second': 0.018, 'total_flos': 5988877375821120.0, 'train_loss': 0.7693650877034223})

Framework versions

• PEFT 0.19.1
• TRL: 0.19.1
• Transformers: 5.12.1
• Pytorch: 2.10.0+cu128
• Datasets: 5.0.0
• Tokenizers: 0.22.2

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}}
}