Instructions to use Colin1337X/broken-tutu-24b-unslop-v2.0-lora-limaRP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Colin1337X/broken-tutu-24b-unslop-v2.0-lora-limaRP with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Colin1337X/broken-tutu-24b-unslop-v2.0-lora-limaRP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from Colin1337X/broken-tutu-24b-unslop-v2.0-lora-limaRP: direct link, hf CLI and curl.
- Browser
- Download file 1.5 kB
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https://huggingface.co/Colin1337X/broken-tutu-24b-unslop-v2.0-lora-limaRP/resolve/main/README.md
- Command line
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hf download hf://Colin1337X/broken-tutu-24b-unslop-v2.0-lora-limaRP/README.md
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curl -L -o README.md https://huggingface.co/Colin1337X/broken-tutu-24b-unslop-v2.0-lora-limaRP/resolve/main/README.md
1.5 kB
metadata
base_model: ReadyArt/Broken-Tutu-24B-Unslop-v2.0
library_name: transformers
model_name: trainer_output
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
Model Card for trainer_output
This model is a fine-tuned version of ReadyArt/Broken-Tutu-24B-Unslop-v2.0. 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="None", 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: 0.23.1
- Transformers: 4.57.6
- Pytorch: 2.10.0+cu128
- Datasets: 4.3.0
- Tokenizers: 0.22.2
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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}