models: add models/planner-3B-orpo-iter1

.gitattributes

@@ -73,3 +73,4 @@ data/rollouts/iter2_bird_train_3stage_K8.jsonl filter=lfs diff=lfs merge=lfs -te
 data/rollouts/bird_train_3stage_K4.jsonl filter=lfs diff=lfs merge=lfs -text
 data/rollouts/scaleup_bird_train_2stage_K4.jsonl filter=lfs diff=lfs merge=lfs -text
 models/planner-3B-sft/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+models/planner-3B-orpo-iter1/tokenizer.json filter=lfs diff=lfs merge=lfs -text

models/planner-3B-orpo-iter1/README.md

@@ -0,0 +1,65 @@
+---
+datasets: /weka/s225250685/mats-tist/data/hf_orpo_planner_iter1
+library_name: transformers
+tags:
+- generated_from_trainer
+- alignment-handbook
+licence: license
+---
+
+# Model Card for None
+
+This model is a fine-tuned version of [None](https://huggingface.co/None) on the [/weka/s225250685/mats-tist/data/hf_orpo_planner_iter1](https://huggingface.co/datasets//weka/s225250685/mats-tist/data/hf_orpo_planner_iter1) dataset.
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+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?"
+generator = pipeline("text-generation", model="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+ 
+
+
+This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).
+
+### Framework versions
+
+- TRL: 0.13.0
+- Transformers: 4.55.0
+- Pytorch: 2.7.1
+- Datasets: 4.8.5
+- Tokenizers: 0.21.4
+
+## Citations
+
+Cite ORPO as:
+
+```bibtex
+@article{hong2024orpo,
+    title        = {{ORPO: Monolithic Preference Optimization without Reference Model}},
+    author       = {Jiwoo Hong and Noah Lee and James Thorne},
+    year         = 2024,
+    eprint       = {arXiv:2403.07691}
+}
+```
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	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},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

models/planner-3B-orpo-iter1/added_tokens.json

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models/planner-3B-orpo-iter1/all_results.json

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+}

models/planner-3B-orpo-iter1/chat_template.jinja

@@ -0,0 +1,5 @@
+{{'<|im_start|>user
+' + messages['prompt'] + '<|im_end|>
+'}}{{'<|im_start|>assistant
+' + messages['completion'] + '<|im_end|>
+'}}

models/planner-3B-orpo-iter1/config.json

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models/planner-3B-orpo-iter1/eval_results.json

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models/planner-3B-orpo-iter1/generation_config.json

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