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--- |
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base_model: HuggingFaceTB/SmolLM2-135M |
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library_name: transformers |
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model_name: SmolLM2-FT-Arlind |
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tags: |
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- generated_from_trainer |
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- smol-course |
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- module_1 |
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- smoltalk |
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- trl |
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- sft |
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licence: license |
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--- |
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# Model Card for SmolLM2-FT-Arlind |
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This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M). |
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It has been trained using [TRL](https://github.com/huggingface/trl). |
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## Quick start |
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```python |
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from transformers import pipeline |
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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?" |
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generator = pipeline("text-generation", model="arlindkadra/SmolLM2-FT-Arlind", device="cuda") |
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] |
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print(output["generated_text"]) |
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``` |
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## Training procedure |
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This model was trained with SFT. |
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### Framework versions |
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- TRL: 0.12.1 |
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- Transformers: 4.46.3 |
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- Pytorch: 2.5.1+cu124 |
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- Datasets: 3.1.0 |
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- Tokenizers: 0.20.3 |
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## Citations |
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Cite TRL as: |
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```bibtex |
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@misc{vonwerra2022trl, |
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title = {{TRL: Transformer Reinforcement Learning}}, |
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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}, |
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year = 2020, |
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journal = {GitHub repository}, |
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publisher = {GitHub}, |
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howpublished = {\url{https://github.com/huggingface/trl}} |
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} |
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``` |
