Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
trl
sft
text-embeddings-inference
Instructions to use hayato92001/Funky with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hayato92001/Funky with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hayato92001/Funky")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hayato92001/Funky") model = AutoModelForSequenceClassification.from_pretrained("hayato92001/Funky", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for Funky
This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct. 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="hayato92001/Funky", 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: 1.1.0
- Transformers: 5.5.4
- Pytorch: 2.10.0+cu128
- Datasets: 4.8.4
- Tokenizers: 0.22.2
Citations
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}
}
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Base model
meta-llama/Llama-3.2-1B-Instruct