Model save

README.md

@@ -1,7 +1,7 @@
 ---
-base_model: unsloth/mistral-7b-v0.3-bnb-4bit
 library_name: peft
-model_name: kto_simplification_balanced
+license: apache-2.0
+base_model: unsloth/mistral-7b-v0.3-bnb-4bit
 tags:
 - base_model:adapter:unsloth/mistral-7b-v0.3-bnb-4bit
 - kto
@@ -9,64 +9,52 @@ tags:
 - transformers
 - trl
 - unsloth
-licence: license
 pipeline_tag: text-generation
+model-index:
+- name: kto_simplification_balanced
+  results: []
 ---
 
-# Model Card for kto_simplification_balanced
+<!-- 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. -->
 
-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).
+[<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_balanced/runs/bpfxv7y5)
+# kto_simplification_balanced
 
-## Quick start
+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.
 
-```python
-from transformers import pipeline
+## Model description
 
-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_balanced", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+More information needed
 
-## Training procedure
+## Intended uses & limitations
 
-[<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_balanced/runs/bpfxv7y5) 
+More information needed
 
+## Training and evaluation data
 
-This model was trained with KTO, a method introduced in [KTO: Model Alignment as Prospect Theoretic Optimization](https://huggingface.co/papers/2402.01306).
-
-### Framework versions
+More information needed
 
-- PEFT 0.18.0
-- TRL: 0.24.0
-- Transformers: 4.57.3
-- Pytorch: 2.9.0
-- Datasets: 4.3.0
-- Tokenizers: 0.22.1
+## Training procedure
 
-## Citations
+### Training hyperparameters
 
-Cite KTO as:
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 2
+- eval_batch_size: 4
+- seed: 3407
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 32
+- 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: 3
 
-```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},
-}
-```
+### Framework versions
 
-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}}
-}
-```
+- PEFT 0.18.0
+- Transformers 4.57.3
+- Pytorch 2.9.0+cu128
+- Datasets 4.3.0
+- Tokenizers 0.22.1