Instructions to use hai2131/IELTS-Task1-Assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hai2131/IELTS-Task1-Assistant with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hai2131/IELTS-Task1-Assistant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from hai2131/IELTS-Task1-Assistant: direct link, hf CLI and curl.
- Browser
- Download file 1.52 kB
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https://huggingface.co/hai2131/IELTS-Task1-Assistant/resolve/main/README.md
- Command line
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hf download hf://hai2131/IELTS-Task1-Assistant/README.md
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curl -L -o README.md https://huggingface.co/hai2131/IELTS-Task1-Assistant/resolve/main/README.md
1.52 kB
metadata
base_model: unsloth/qwen2.5-3b-unsloth-bnb-4bit
library_name: transformers
model_name: IELTS-Task1-Assistant
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
Model Card for IELTS-Task1-Assistant
This model is a fine-tuned version of unsloth/qwen2.5-3b-unsloth-bnb-4bit. 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="hai2131/IELTS-Task1-Assistant", 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: 0.15.2
- Transformers: 4.51.1
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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}}
}