Model save

README.md

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+---
+base_model: teknium/OpenHermes-2.5-Mistral-7B
+library_name: transformers
+model_name: mistral-7b-dpo
+tags:
+- generated_from_trainer
+- trl
+- dpo
+licence: license
+---
+
+# Model Card for mistral-7b-dpo
+
+This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B).
+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="Jenbenarye/mistral-7b-dpo", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+[<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/jenbenarye_/adversarial-rlhf/runs/alt53jvz) 
+
+
+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).
+
+### Framework versions
+
+- TRL: 0.24.0
+- Transformers: 4.57.1
+- Pytorch: 2.9.0
+- Datasets: 4.3.0
+- Tokenizers: 0.22.1
+
+## Citations
+
+Cite DPO as:
+
+```bibtex
+@inproceedings{rafailov2023direct,
+    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
+    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
+    year         = 2023,
+    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},
+    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
+    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
+}
+```
+
+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 Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

added_tokens.json

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+{
+  "<|im_end|>": 32000,
+  "<|im_start|>": 32001
+}

all_results.json

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+{
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+    "eval_logps/chosen": -296.33209228515625,
+    "eval_logps/rejected": -291.47021484375,
+    "eval_loss": 0.4926324486732483,
+    "eval_rewards/accuracies": 0.7570000290870667,
+    "eval_rewards/chosen": -1.3098689317703247,
+    "eval_rewards/margins": 1.4436919689178467,
+    "eval_rewards/rejected": -2.753561019897461,
+    "eval_runtime": 1173.4935,
+    "eval_samples": 2000,
+    "eval_samples_per_second": 1.704,
+    "eval_steps_per_second": 0.213,
+    "total_flos": 0.0,
+    "train_loss": 0.5167291775107914,
+    "train_runtime": 38075.0654,
+    "train_samples": 61135,
+    "train_samples_per_second": 1.606,
+    "train_steps_per_second": 0.1
+}

chat_template.jinja

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+{% for message in messages %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

eval_results.json

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+    "eval_logps/chosen": -296.33209228515625,
+    "eval_logps/rejected": -291.47021484375,
+    "eval_loss": 0.4926324486732483,
+    "eval_rewards/accuracies": 0.7570000290870667,
+    "eval_rewards/chosen": -1.3098689317703247,
+    "eval_rewards/margins": 1.4436919689178467,
+    "eval_rewards/rejected": -2.753561019897461,
+    "eval_runtime": 1173.4935,
+    "eval_samples": 2000,
+    "eval_samples_per_second": 1.704,
+    "eval_steps_per_second": 0.213
+}

policy/adapter_config.json

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policy/adapter_model.safetensors

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reference/adapter_config.json

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reference/adapter_model.safetensors

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special_tokens_map.json

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tokenizer.model

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tokenizer_config.json

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+}

train_results.json

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+}

training_args.bin

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