68 lines
2.3 KiB
Markdown
68 lines
2.3 KiB
Markdown
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---
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base_model: Qwen/Qwen3-4B-Base
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datasets: stanfordnlp/imdb
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library_name: transformers
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model_name: vanilla-imdb-ppo-prop0.2-alpha1.0-seed42-mean_kl0.1-Qwen-Qwen3-4B-Base
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tags:
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- generated_from_trainer
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- ppo
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- trl
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licence: license
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---
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# Model Card for vanilla-imdb-ppo-prop0.2-alpha1.0-seed42-mean_kl0.1-Qwen-Qwen3-4B-Base
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This model is a fine-tuned version of [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) on the [stanfordnlp/imdb](https://huggingface.co/datasets/stanfordnlp/imdb) 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="mxcui/vanilla-imdb-ppo-prop0.2-alpha1.0-seed42-mean_kl0.1-Qwen-Qwen3-4B-Base", 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/mxcui-none/huggingface/runs/esub6q9d)
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This model was trained with PPO, a method introduced in [Fine-Tuning Language Models from Human Preferences](https://huggingface.co/papers/1909.08593).
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### Framework versions
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- TRL: 1.4.0
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- Transformers: 5.9.0
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- Pytorch: 2.11.0+cu128
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- Datasets: 4.8.6.dev0
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- Tokenizers: 0.22.2
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## Citations
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Cite PPO as:
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```bibtex
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@article{mziegler2019fine-tuning,
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title = {{Fine-Tuning Language Models from Human Preferences}},
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author = {Daniel M. Ziegler and Nisan Stiennon and Jeffrey Wu and Tom B. Brown and Alec Radford and Dario Amodei and Paul F. Christiano and Geoffrey Irving},
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year = 2019,
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eprint = {arXiv:1909.08593}
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}
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```
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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