Update README.md

README.md

@@ -1,80 +1,13 @@
 ---
 license: gemma
-library_name: transformers
+base_model: AALF/gemma-2-27b-it-SimPO-37K
+base_model_relation: quantized
 pipeline_tag: text-generation
-base_model: google/gemma-2-27b-it
 tags:
 - alignment-handbook
 - generated_from_trainer
 ---
 
-# gemma-2-27b-it-SimPO-37K Model Card
+fits into 24gb with 24576 ctx (q4)
 
-## Implementation Details
-We first followed the [SimPO](https://github.com/princeton-nlp/SimPO) framework to apply [On-Policy Preference Data Generation](https://github.com/princeton-nlp/SimPO/tree/main/on_policy_data_gen) on the [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) dataset using the [google/gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it) model. We then selected prompts where the chosen reward was at least 0.01 higher than the rejected reward, resulting in 37,040 training data points.
-
-Model training was conducted using 8x80G A800 GPUs, leveraging the [alignment-handbook](https://github.com/huggingface/alignment-handbook) library. We used `deepspeed_zero_stage3` with optimizer offloading to the CPU. The `SimPOTrainer` arguments were as follows:
-
-```bash
-# SimPOTrainer arguments
-bf16: true
-beta: 10
-gamma_beta_ratio: 0.5
-gradient_accumulation_steps: 8
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: true
-hub_model_id: simpo-exps
-learning_rate: 8.0e-7
-log_level: info
-logging_steps: 1
-lr_scheduler_type: cosine
-max_length: 2048
-max_prompt_length: 1800
-num_train_epochs: 1
-optim: adamw_torch
-output_dir: outputs/gemma-2-27b-it-SimPO
-run_name: gemma-2-27b-it-SimPO
-per_device_train_batch_size: 2
-push_to_hub: false
-save_strategy: "steps"
-save_steps: 100
-save_total_limit: 20
-seed: 42
-warmup_ratio: 0.1
-save_only_model: true
-```
-
-## Citation
-
-gemma model:
-```
-@article{gemma_2024,
-    title={Gemma},
-    url={https://www.kaggle.com/m/3301},
-    DOI={10.34740/KAGGLE/M/3301},
-    publisher={Kaggle},
-    author={Gemma Team},
-    year={2024}
-}
-```
-
-SimPO paper:
-```
-@article{meng2024simpo,
-  title={{SimPO}: Simple preference optimization with a reference-free reward},
-  author={Meng, Yu and Xia, Mengzhou and Chen, Danqi},
-  journal={arXiv preprint arXiv:2405.14734},
-  year={2024}
-}
-```
-
-UltraFeedback paper:
-```
-@article{cui2023ultrafeedback,
-  title={{UltraFeedback}: Boosting language models with high-quality feedback},
-  author={Cui, Ganqu and Yuan, Lifan and Ding, Ning and Yao, Guanming and Zhu, Wei and Ni, Yuan and Xie, Guotong and Liu, Zhiyuan and Sun, Maosong},
-  journal={arXiv preprint arXiv:2310.01377},
-  year={2023}
-}
-```
+set rope_alpha to 3.75