60 lines
1.9 KiB
Markdown
60 lines
1.9 KiB
Markdown
---
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base_model: Qwen/Qwen1.5-MoE-A2.7B
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datasets: HectorHe/math7k
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library_name: transformers
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model_name: Qwen1.5-MOE-sft-math7k-sft-2epochs-frozen-router
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tags:
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- generated_from_trainer
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- open-r1
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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 Qwen1.5-MOE-sft-math7k-sft-2epochs-frozen-router
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This model is a fine-tuned version of [Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) on the [HectorHe/math7k](https://huggingface.co/datasets/HectorHe/math7k) dataset.
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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="xd2010/Qwen1.5-MOE-sft-math7k-sft-2epochs-frozen-router", 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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hector_-carnegie-mellon-university/huggingface/runs/ovyzz5sp)
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.16.0.dev0
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- Transformers: 4.51.0
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- Pytorch: 2.6.0
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- Datasets: 4.8.4
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- Tokenizers: 0.21.4
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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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``` |