gabrielmbmb_-_SmolLM-1.7B-Instruct-IFEval-gguf

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SmolLM-1.7B-Instruct-IFEval - GGUF

• Model creator: https://huggingface.co/gabrielmbmb/
• Original model: https://huggingface.co/gabrielmbmb/SmolLM-1.7B-Instruct-IFEval/

Original model description:

base_model: HuggingFaceTB/SmolLM-1.7B-Instruct datasets: gabrielmbmb/ifeval-trl library_name: transformers model_name: SmolLM-1.7B-Instruct-IFEval tags:

• generated_from_trainer
• trl
• sft licence: license

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Model Card for SmolLM-1.7B-Instruct-IFEval

This model is a fine-tuned version of HuggingFaceTB/SmolLM-1.7B-Instruct on the gabrielmbmb/ifeval-trl 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="gabrielmbmb/SmolLM-1.7B-Instruct-IFEval", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>

This model was trained with SFT.

Framework versions

• TRL: 0.12.0.dev0
• Transformers: 4.45.1
• Pytorch: 2.4.1
• Datasets: 3.0.1
• Tokenizers: 0.20.0

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}}
}