|
--- |
|
license: apache-2.0 |
|
base_model: mistralai/Mistral-7B-Instruct-v0.3 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: Mistral-7B-Instruct-v0.3-lora-commonsense |
|
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. --> |
|
|
|
[<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/yspkm/PrunePath-LoRA/runs/9c3s4ewl) |
|
# Mistral-7B-Instruct-v0.3-lora-commonsense |
|
|
|
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6863 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 0.0001 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 8 |
|
- total_train_batch_size: 128 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_steps: 100 |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:------:|:----:|:---------------:| |
|
| 0.8738 | 0.1503 | 200 | 0.8158 | |
|
| 0.8589 | 0.3006 | 400 | 0.7939 | |
|
| 0.8589 | 0.4510 | 600 | 0.7800 | |
|
| 0.8589 | 0.6013 | 800 | 0.7725 | |
|
| 0.8305 | 0.7516 | 1000 | 0.7650 | |
|
| 0.8331 | 0.9019 | 1200 | 0.7506 | |
|
| 0.7808 | 1.0522 | 1400 | 0.7438 | |
|
| 0.7781 | 1.2026 | 1600 | 0.7350 | |
|
| 0.7647 | 1.3529 | 1800 | 0.7252 | |
|
| 0.7651 | 1.5032 | 2000 | 0.7228 | |
|
| 0.7522 | 1.6535 | 2200 | 0.7099 | |
|
| 0.7587 | 1.8038 | 2400 | 0.6997 | |
|
| 0.7383 | 1.9542 | 2600 | 0.6932 | |
|
| 0.7071 | 2.1045 | 2800 | 0.6949 | |
|
| 0.6919 | 2.2548 | 3000 | 0.6899 | |
|
| 0.7136 | 2.4051 | 3200 | 0.6884 | |
|
| 0.6912 | 2.5554 | 3400 | 0.6878 | |
|
| 0.6889 | 2.7057 | 3600 | 0.6867 | |
|
| 0.6862 | 2.8561 | 3800 | 0.6863 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.42.3 |
|
- Pytorch 2.3.1+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
