vinhnx90/facebook-opt-350m-vinhnx90-love_poems-bnb-4bit

vinhnx90/facebook-opt-350m-vinhnx90-love_poems (Quantized)

Description

This model is a quantized version of the original model vinhnx90/facebook-opt-350m-vinhnx90-love_poems.

It's quantized using the BitsAndBytes library to 4-bit using the bnb-my-repo space.

Quantization Details

• Quantization Type: int4
• bnb_4bit_quant_type: fp4
• bnb_4bit_use_double_quant: True
• bnb_4bit_compute_dtype: float32
• bnb_4bit_quant_storage: bfloat16

📄 Original Model Information

Model Card for facebook-opt-350m-vinhnx90-love_poems

This model is a fine-tuned version of facebook/opt-350m. 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="vinhnx90/facebook-opt-350m-vinhnx90-love_poems", 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: 0.13.0
• Transformers: 4.47.1
• Pytorch: 2.5.1+cu121
• Datasets: 3.2.0
• Tokenizers: 0.21.0

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