HumanLLMs/Human-Like-DPO-Dataset
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How to use Amir337/trainer_output with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Amir337/trainer_output") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Amir337/trainer_output")
model = AutoModelForSequenceClassification.from_pretrained("Amir337/trainer_output")This model is a fine-tuned version of HuggingFaceTB/SmolLM-135M-Instruct on the HumanLLMs/Human-Like-DPO-Dataset dataset. It has been trained using TRL.
from transformers import pipeline
text = "The capital of France is Paris."
rewarder = pipeline(model="Amir337/trainer_output", device="cuda")
output = rewarder(text)[0]
print(output["score"])
This model was trained with Reward.
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}}
}
Base model
HuggingFaceTB/SmolLM-135M