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import nbformat as nbf
def create_hf_card(cells, name, base_model_name, base_model_version, dataset_name, output_dir, report_to):
text = f"""
card = '''
---
license: apache-2.0
tags:
- generated_from_trainer
- {base_model_name}
- PyTorch
- transformers
- trl
- peft
- {report_to}
base_model: {base_model_name}-{base_model_version}
widget:
- example_title: Pirate!
messages:
- role: system
content: You are a pirate chatbot who always responds with Arr!
- role: user
content: "There's a llama on my lawn, how can I get rid of him?"
output:
text: >-
Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare
sight, but I've got a plan that might help ye get rid of 'im. Ye'll need
to gather some carrots and hay, and then lure the llama away with the
promise of a tasty treat. Once he's gone, ye can clean up yer lawn and
enjoy the peace and quiet once again. But beware, me hearty, for there
may be more llamas where that one came from! Arr!
model-index:
- name: {name}
results: []
datasets:
- {dataset_name}
language:
- en
pipeline_tag: text-generation
---
# Model Card for {name}:
**{name}** is a language model that is trained to act as helpful assistant. It is a finetuned version of [{base_model_name}-{base_model_version}](https://huggingface.co/{base_model_name}-{base_model_version}) that was trained using `SFTTrainer` on publicly available dataset [
{dataset_name}](https://huggingface.co/datasets/{dataset_name}).
## Training Procedure:
The training code used to create this model was generated by [Menouar/LLM-FineTuning-Notebook-Generator](https://huggingface.co/spaces/Menouar/LLM-FineTuning-Notebook-Generator).
## Training hyperparameters
The following hyperparameters were used during the training:
'''
with open("{output_dir}/README.md", "w") as f:
f.write(card)
args_dict = vars(args)
with open("{output_dir}/README.md", "a") as f:
for k, v in args_dict.items():
f.write(f"- {{k}}: {{v}}")
f.write("\\n \\n")
"""
title = """### Generating a model card (README.md)"""
cells.append(nbf.v4.new_markdown_cell(title))
code_cell = nbf.v4.new_code_cell(text)
cells.append(code_cell)
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