HumanLLMs/Human-Like-DPO-Dataset
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How to use SupNek/trainer_output with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="SupNek/trainer_output") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("SupNek/trainer_output")
model = AutoModelForSequenceClassification.from_pretrained("SupNek/trainer_output", device_map="auto")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="SupNek/trainer_output", device="cuda")
output = rewarder(text)[0]
print(output["score"])
This model was trained with Reward.
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}
Base model
HuggingFaceTB/SmolLM-135M