Instructions to use qgallouedec/tiny-aya-global-tool-calling-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qgallouedec/tiny-aya-global-tool-calling-SFT with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("qgallouedec/tiny-aya-global-tool-calling-SFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from qgallouedec/tiny-aya-global-tool-calling-SFT: direct link, hf CLI and curl.
- Browser
- Download file 2.1 kB
-
https://huggingface.co/qgallouedec/tiny-aya-global-tool-calling-SFT/resolve/main/README.md
- Command line
-
hf download hf://qgallouedec/tiny-aya-global-tool-calling-SFT/README.md
-
curl -L -o README.md https://huggingface.co/qgallouedec/tiny-aya-global-tool-calling-SFT/resolve/main/README.md
2.1 kB
metadata
base_model: CohereLabs/tiny-aya-global
datasets: bebechien/SimpleToolCalling
library_name: transformers
model_name: tiny-aya-global-tool-calling-SFT
tags:
- generated_from_trainer
- >-
trackio:https://qgallouedec-tiny-aya-global-tool-calling-SFT.hf.space?project=huggingface&runs=qgallouedec-1771431695&sidebar=collapsed
- sft
- trackio
- trl
licence: license
Model Card for tiny-aya-global-tool-calling-SFT
This model is a fine-tuned version of CohereLabs/tiny-aya-global on the bebechien/SimpleToolCalling 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="qgallouedec/tiny-aya-global-tool-calling-SFT", 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: 0.28.0.dev0
- Transformers: 5.2.0.dev0
- Pytorch: 2.10.0
- Datasets: 4.5.0
- 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}
}