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--- |
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base_model: HuggingFaceTB/SmolLM2-360M |
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library_name: transformers |
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model_name: SmolLM2-360M-tldr-sft-2025-02-12_15-13 |
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tags: |
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- generated_from_trainer |
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- trl |
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- sft |
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license: mit |
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--- |
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# Smol-Hub-tldr |
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<div style="float: right; margin-left: 1em;"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/60107b385ac3e86b3ea4fc34/dD9vx3VOPB0Tf6C_ZjJT2.png" alt="Model visualization" width="200"/> |
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</div> |
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This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M). The model is focused on generating concise, one-sentence summaries of model and dataset cards from the Hugging Face Hub. These summaries are intended to be used for: |
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- creating useful tl;dr descriptions that can give you a quick sense of what a dataset or model is for |
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- as input text for creating embeddings for semantic search. You can see a demo of this in [librarian-bots/huggingface-datasets-semantic-search](https://huggingface.co/spaces/librarian-bots/huggingface-datasets-semantic-search). |
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The model was trained using supervised fine-tuning (SFT) with [TRL](https://github.com/huggingface/trl). |
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## Intended Use |
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The model is designed to generate brief, informative summaries of: |
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- Model cards: Focusing on key capabilities and characteristics |
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- Dataset cards: Capturing essential dataset characteristics and purposes |
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## Training Data |
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The model was trained on: |
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- Model card summaries generated by Llama 3.3 70B |
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- Dataset card summaries generated by Llama 3.3 70B |
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## Usage |
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```python |
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from transformers import pipeline |
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generator = pipeline("text-generation", model="davanstrien/SmolLM2-360M-tldr-sft-2025-02-12_15-13", device="cuda") |
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output = generator(input_text, max_new_tokens=128, return_full_text=False)[0] |
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``` |
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## Framework Versions |
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- TRL 0.14.0 |
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- Transformers 4.48.3 |
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- PyTorch 2.6.0 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
