metadata

base_model: HuggingFaceTB/SmolLM2-135M-Instruct
datasets: Crimsoin/OTC_Medicine_PH_v2
library_name: transformers
model_name: HuggingFaceTB/SmolLM2-135M-Instruct
tags:
  - generated_from_trainer
  - question-answering
  - QA
  - text-generation
  - trl
  - sft
licence: license

Model Card for HuggingFaceTB/SmolLM2-135M-Instruct

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M-Instruct on the Crimsoin/OTC_Medicine_PH_v2 dataset. 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="Erick03/HFTB-SmolLM2-135M-Instruct-OTCMedicinePHv2", 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: 0.12.0
Transformers: 4.46.2
Pytorch: 2.3.0+cu121
Datasets: 3.0.1
Tokenizers: 0.20.1

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