metadata

'[object Object]': null

Model Card for {{ model_name }}

This model is a fine-tuned version of [{{ base_model }}](https://huggingface.co/{{ base_model }}){% if dataset_name %} on the [{{ dataset_name }}](https://huggingface.co/datasets/{{ dataset_name }}) dataset{% endif %}. 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="{{ hub_model_id }}", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

{% if wandb_url %}[]({{ wandb_url }}){% endif %} {% if comet_url %}[]({{ comet_url }}){% endif %}

This model was trained with {{ trainer_name }}{% if paper_id %}, a method introduced in [{{ paper_title }}](https://huggingface.co/papers/{{ paper_id }}){% endif %}.

Framework versions

TRL: {{ trl_version }}
Transformers: {{ transformers_version }}
Pytorch: {{ pytorch_version }}
Datasets: {{ datasets_version }}
Tokenizers: {{ tokenizers_version }}

Citations

{% if trainer_citation %}Cite {{ trainer_name }} as:

{{ trainer_citation }}
```{% endif %}

Cite TRL as:
    
```bibtex
{% raw %}@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}}
}{% endraw %}