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Training in progress, step 500

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README.md ADDED
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+ ---
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+ base_model: HuggingFaceTB/SmolLM2-360M-instruct
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+ library_name: transformers
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+ model_name: Smollm2-360m-instruct-valheim
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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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+ licence: license
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+ ---
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+
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+ # Model Card for Smollm2-360m-instruct-valheim
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+
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+ This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M-instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-instruct).
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+ It has been trained using [TRL](https://github.com/huggingface/trl).
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+
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+ ## Quick start
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+
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+ ```python
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+ from transformers import pipeline
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+
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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="Egrigor/Smollm2-360m-instruct-valheim", 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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+
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+ ## Training procedure
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+
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+
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+
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+
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+ This model was trained with SFT.
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+
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+ ### Framework versions
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+
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+ - TRL: 0.26.0
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+ - Transformers: 4.57.3
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+ - Pytorch: 2.9.0+cu126
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+ - Datasets: 4.0.0
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+ - Tokenizers: 0.22.1
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+
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+ ## Citations
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+
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+
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+
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+ Cite TRL as:
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+
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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 Gallou{\'e}dec},
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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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+ ```
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+ {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
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+ You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
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+ ' }}{% endif %}{{'<|im_start|>' + message['role'] + '
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+ ' + message['content'] + '<|im_end|>' + '
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+ ' }}{% endif %}
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