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README.md

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+---
+base_model: google/t5_xxl_true_nli_mixture
+library_name: transformers
+model_name: t5_xxl_true_nli_mixture_aligned_augmented2kv05ep
+tags:
+- generated_from_trainer
+- trl
+- dpo
+licence: license
+---
+
+# Model Card for t5_xxl_true_nli_mixture_aligned_augmented2kv05ep
+
+This model is a fine-tuned version of [google/t5_xxl_true_nli_mixture](https://huggingface.co/google/t5_xxl_true_nli_mixture).
+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 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: 0.23.0
+- Transformers: 4.56.1
+- Pytorch: 2.6.0+cu126
+- Datasets: 4.1.0
+- Tokenizers: 0.22.0
+
+## 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}}
+}
+```

all_results.json

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+{
+    "epoch": 5.0,
+    "eval_logits/chosen": -42.66371536254883,
+    "eval_logits/rejected": -41.95132827758789,
+    "eval_logps/chosen": -65.31195068359375,
+    "eval_logps/rejected": -71.56636810302734,
+    "eval_loss": 0.6810184121131897,
+    "eval_rewards/accuracies": 0.5956858396530151,
+    "eval_rewards/chosen": -0.006560367997735739,
+    "eval_rewards/margins": 0.029038555920124054,
+    "eval_rewards/rejected": -0.03560638427734375,
+    "eval_runtime": 142.4332,
+    "eval_samples_per_second": 12.694,
+    "eval_steps_per_second": 0.793,
+    "total_flos": 0.0,
+    "train_loss": 0.6952488298458149,
+    "train_runtime": 23018.5818,
+    "train_samples": 7233,
+    "train_samples_per_second": 1.571,
+    "train_steps_per_second": 0.049
+}

chat_template.jinja

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+{% for message in messages %}{{ message['role'] + ': ' + message['content'] + '
+'}}{% endfor %}

config.json

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+{
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 65536,
+  "d_kv": 128,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "dtype": "bfloat16",
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
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+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
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+  "num_heads": 128,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "transformers_version": "4.56.1",
+  "use_cache": false,
+  "vocab_size": 32128
+}

eval_results.json

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+    "eval_rewards/margins": 0.029038555920124054,
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+    "eval_runtime": 142.4332,
+    "eval_samples_per_second": 12.694,
+    "eval_steps_per_second": 0.793
+}

generation_config.json

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+  "eos_token_id": [
+    1
+  ],
+  "pad_token_id": 0,
+  "transformers_version": "4.56.1",
+  "use_cache": false
+}

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special_tokens_map.json

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