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Qwen2.5-1.5B-SFT_d-si_s-20252_dpo_0926-203321_1b-TEST

Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
trl
dpo
conversational
text-generation-inference
Model card Files
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Training in progress, step 32

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  1. .gitattributes +2 -0
  2. README.md +69 -0
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.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ base_model: rshwndsz/Qwen2.5-1.5B-SFT
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+ library_name: transformers
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+ model_name: Qwen2.5-1.5B-SFT_d-si_s-20252_dpo_0926-203321_1b-TEST
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+ tags:
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+ - generated_from_trainer
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+ - trl
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+ - dpo
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+ licence: license
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+ ---
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+
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+ # Model Card for Qwen2.5-1.5B-SFT_d-si_s-20252_dpo_0926-203321_1b-TEST
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+
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+ This model is a fine-tuned version of [rshwndsz/Qwen2.5-1.5B-SFT](https://huggingface.co/rshwndsz/Qwen2.5-1.5B-SFT).
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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="rshwndsz/Qwen2.5-1.5B-SFT_d-si_s-20252_dpo_0926-203321_1b-TEST", 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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+ [<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/rshwndsz/janus/runs/c1fx1stu)
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+
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+
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+ This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
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+
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+ ### Framework versions
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+
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+ - TRL: 0.23.0
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+ - Transformers: 4.56.1
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+ - Pytorch: 2.7.1+cu126
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+ - Datasets: 3.6.0
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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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+ Cite DPO as:
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+
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+ ```bibtex
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+ @inproceedings{rafailov2023direct,
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+ title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
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+ author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
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+ year = 2023,
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+ booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
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+ url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
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+ editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
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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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