End of training

README.md

@@ -1,57 +1,68 @@
 ---
-library_name: peft
-license: apache-2.0
 base_model: unsloth/mistral-7b-v0.3-bnb-4bit
+library_name: transformers
+model_name: kto_simplification_imbalanced
 tags:
+- generated_from_trainer
 - unsloth
 - trl
 - kto
-- generated_from_trainer
-model-index:
-- name: kto_simplification_imbalanced
-  results: []
+licence: license
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Model Card for kto_simplification_imbalanced
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ioakeime-aristotle-university-of-thessaloniki/kto_smiplification_imbalanced/runs/jo2gfavb)
-# kto_simplification_imbalanced
+This model is a fine-tuned version of [unsloth/mistral-7b-v0.3-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-v0.3-bnb-4bit).
+It has been trained using [TRL](https://github.com/huggingface/trl).
 
-This model is a fine-tuned version of [unsloth/mistral-7b-v0.3-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-v0.3-bnb-4bit) on an unknown dataset.
+## Quick start
 
-## Model description
+```python
+from transformers import pipeline
 
-More information needed
+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="IoakeimE/kto_simplification_imbalanced", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
 
-## Intended uses & limitations
+## Training procedure
 
-More information needed
+[<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/ioakeime-aristotle-university-of-thessaloniki/kto_smiplification_imbalanced/runs/jo2gfavb) 
 
-## Training and evaluation data
 
-More information needed
+This model was trained with KTO, a method introduced in [KTO: Model Alignment as Prospect Theoretic Optimization](https://huggingface.co/papers/2402.01306).
 
-## Training procedure
+### Framework versions
 
-### Training hyperparameters
+- TRL: 0.19.0
+- Transformers: 4.53.0
+- Pytorch: 2.7.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.2
 
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 8
-- eval_batch_size: 4
-- seed: 3407
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 128
-- optimizer: Use paged_adamw_32bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 6
+## Citations
 
-### Framework versions
+Cite KTO as:
+
+```bibtex
+@article{ethayarajh2024kto,
+    title        = {{KTO: Model Alignment as Prospect Theoretic Optimization}},
+    author       = {Kawin Ethayarajh and Winnie Xu and Niklas Muennighoff and Dan Jurafsky and Douwe Kiela},
+    year         = 2024,
+    eprint       = {arXiv:2402.01306},
+}
+```
 
-- PEFT 0.15.2
-- Transformers 4.53.0
-- Pytorch 2.7.0+cu126
-- Datasets 3.6.0
-- Tokenizers 0.21.2
+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}}
+}
+```