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  1. .gitattributes +1 -0
  2. models/validator-sel-1B-orpo-iter1-collab-paper/README.md +65 -0
  3. models/validator-sel-1B-orpo-iter1-collab-paper/all_results.json +25 -0
  4. models/validator-sel-1B-orpo-iter1-collab-paper/chat_template.jinja +5 -0
  5. models/validator-sel-1B-orpo-iter1-collab-paper/config.json +39 -0
  6. models/validator-sel-1B-orpo-iter1-collab-paper/eval_results.json +19 -0
  7. models/validator-sel-1B-orpo-iter1-collab-paper/generation_config.json +12 -0
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  10. models/validator-sel-1B-orpo-iter1-collab-paper/runs/May19_04-36-00_h200l-m-01/events.out.tfevents.1779130083.h200l-m-01 +3 -0
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  12. models/validator-sel-1B-orpo-iter1-collab-paper/tokenizer.json +3 -0
  13. models/validator-sel-1B-orpo-iter1-collab-paper/tokenizer_config.json +2071 -0
  14. models/validator-sel-1B-orpo-iter1-collab-paper/train_results.json +9 -0
  15. models/validator-sel-1B-orpo-iter1-collab-paper/trainer_state.json +839 -0
.gitattributes CHANGED
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  models/planner-3B-orpo-iter1/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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  models/validator-sel-1B-sft-paper/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 
 
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  models/validator-sel-1B-sft-paper/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ models/validator-sel-1B-orpo-iter1-collab-paper/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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1
+ ---
2
+ datasets: /weka/s225250685/mats-tist/data/hf_orpo_val_sel_paper_iter1_collab
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+ library_name: transformers
4
+ tags:
5
+ - generated_from_trainer
6
+ - alignment-handbook
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+ licence: license
8
+ ---
9
+
10
+ # Model Card for None
11
+
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+ This model is a fine-tuned version of [None](https://huggingface.co/None) on the [/weka/s225250685/mats-tist/data/hf_orpo_val_sel_paper_iter1_collab](https://huggingface.co/datasets//weka/s225250685/mats-tist/data/hf_orpo_val_sel_paper_iter1_collab) dataset.
13
+ It has been trained using [TRL](https://github.com/huggingface/trl).
14
+
15
+ ## Quick start
16
+
17
+ ```python
18
+ from transformers import pipeline
19
+
20
+ 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?"
21
+ generator = pipeline("text-generation", model="None", device="cuda")
22
+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
23
+ print(output["generated_text"])
24
+ ```
25
+
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+ ## Training procedure
27
+
28
+
29
+
30
+
31
+ This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).
32
+
33
+ ### Framework versions
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+
35
+ - TRL: 0.13.0
36
+ - Transformers: 4.55.0
37
+ - Pytorch: 2.7.1
38
+ - Datasets: 4.8.5
39
+ - Tokenizers: 0.21.4
40
+
41
+ ## Citations
42
+
43
+ Cite ORPO as:
44
+
45
+ ```bibtex
46
+ @article{hong2024orpo,
47
+ title = {{ORPO: Monolithic Preference Optimization without Reference Model}},
48
+ author = {Jiwoo Hong and Noah Lee and James Thorne},
49
+ year = 2024,
50
+ eprint = {arXiv:2403.07691}
51
+ }
52
+ ```
53
+
54
+ Cite TRL as:
55
+
56
+ ```bibtex
57
+ @misc{vonwerra2022trl,
58
+ title = {{TRL: Transformer Reinforcement Learning}},
59
+ 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},
60
+ year = 2020,
61
+ journal = {GitHub repository},
62
+ publisher = {GitHub},
63
+ howpublished = {\url{https://github.com/huggingface/trl}}
64
+ }
65
+ ```
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