File size: 3,176 Bytes
b94494e 19a512a b94494e 19a512a b94494e 19a512a b94494e 19a512a b94494e 19a512a b94494e 19a512a 40da4a1 19a512a b94494e 19a512a b94494e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
---
license: other
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
model-index:
- name: sft_trained_woaqa_llama3
results: []
datasets:
- jiazhengli/Synthetic_Rationale
- jiazhengli/Rationale_MCTS
language:
- en
metrics:
- accuracy
- f1
---
# Meta-Llama-3-8B-QLoRA-Assessment-Rationale-sft
The model trained with w/o private data from the EMNLP 2024 Paper: Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring.
- **Paper:** [Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring](https://arxiv.org/abs/2406.19949) (EMNLP 2024 Findings)
- **GitHub Repository:** [Thought Tree Assessment Repository](https://github.com/lijiazheng99/thought_tree_assessment)
## Intended uses & limitations
This model offers a valuable resource for research in explainable AI within educational technology. The model is trained with **noisy** response-level rationales. This makes them **unsuitable** for direct application in high-stakes assessments without additional verification.
## Training and evaluation data
We trained and evaluated the model on the [Synthetic Rationale data](https://huggingface.co/datasets/jiazhengli/Synthetic_Rationale), which was generated from the [Rationale MCTS data](https://huggingface.co/datasets/jiazhengli/Rationale_MCTS).
To extract scores from rationales, please use the [jiazhengli/deberta-v3-large-Rationale-to-Score](https://huggingface.co/jiazhengli/deberta-v3-large-Rationale-to-Score).
## Citation
Please cite the following work if you utilize this model:
**BibTeX:**
```bibtex
@misc{li2024calibratingllmspreferenceoptimization,
title={Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring},
author={Jiazheng Li and Hainiu Xu and Zhaoyue Sun and Yuxiang Zhou and David West and Cesare Aloisi and Yulan He},
year={2024},
eprint={2406.19949},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.19949},
}
```
## Training procedure
Please refer to our [paper](https://arxiv.org/abs/2406.19949).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9813 | 0.63 | 100 | 0.9671 |
| 0.9108 | 1.26 | 200 | 0.9250 |
| 0.8976 | 1.9 | 300 | 0.9091 |
| 0.8687 | 2.53 | 400 | 0.9005 |
| 0.8548 | 3.16 | 500 | 0.8958 |
| 0.8468 | 3.79 | 600 | 0.8952 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |