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---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- unsloth
- generated_from_trainer
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
model-index:
- name: llama3-QA-ViMMRC-Squad-v1.1
results: []
---
<!-- 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. -->
# llama3-QA-ViMMRC-Squad-v1.1
This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co./unsloth/llama-3-8b-Instruct-bnb-4bit) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6506
## Model description
More information needed
## Intended uses & limitations
- **Prompt 1**: Given the following reference, create a question and a corresponding answer to the question: + [context]
- **Prompt 2**: Given the following reference, create a multiple-choice question and its corresponding answer: + [context]
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8259 | 0.2307 | 320 | 1.8769 |
| 1.611 | 0.4614 | 640 | 1.9125 |
| 1.4266 | 0.6921 | 960 | 1.9795 |
| 1.2355 | 0.9229 | 1280 | 2.0370 |
| 0.9715 | 1.1536 | 1600 | 2.1435 |
| 0.7983 | 1.3843 | 1920 | 2.2154 |
| 0.6768 | 1.6150 | 2240 | 2.3018 |
| 0.5643 | 1.8457 | 2560 | 2.3872 |
| 0.4374 | 2.0764 | 2880 | 2.5030 |
| 0.325 | 2.3071 | 3200 | 2.5655 |
| 0.2927 | 2.5379 | 3520 | 2.6038 |
| 0.2688 | 2.7686 | 3840 | 2.6470 |
| 0.2641 | 2.9993 | 4160 | 2.6506 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1 |