|
--- |
|
base_model: meta-llama/Llama-3.2-1B-Instruct |
|
library_name: peft |
|
license: llama3.2 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: Llama-3.2-1B-Instruct_v2 |
|
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. --> |
|
|
|
# Llama-3.2-1B-Instruct_v2 |
|
|
|
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2356 |
|
|
|
## 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: 0.0005 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 4 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 16 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_steps: 30 |
|
- training_steps: 3000 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:------:|:----:|:---------------:| |
|
| 0.4565 | 0.0333 | 100 | 0.4058 | |
|
| 0.3313 | 0.0667 | 200 | 0.3428 | |
|
| 0.3139 | 0.1 | 300 | 0.3192 | |
|
| 0.2903 | 0.1333 | 400 | 0.3034 | |
|
| 0.2639 | 0.1667 | 500 | 0.2944 | |
|
| 0.2688 | 0.2 | 600 | 0.2869 | |
|
| 0.3097 | 0.2333 | 700 | 0.2791 | |
|
| 0.2462 | 0.2667 | 800 | 0.2735 | |
|
| 0.3257 | 0.3 | 900 | 0.2684 | |
|
| 0.2738 | 0.3333 | 1000 | 0.2638 | |
|
| 0.2572 | 0.3667 | 1100 | 0.2598 | |
|
| 0.234 | 0.4 | 1200 | 0.2566 | |
|
| 0.2233 | 0.4333 | 1300 | 0.2537 | |
|
| 0.2996 | 0.4667 | 1400 | 0.2515 | |
|
| 0.2178 | 0.5 | 1500 | 0.2490 | |
|
| 0.2251 | 0.5333 | 1600 | 0.2470 | |
|
| 0.262 | 0.5667 | 1700 | 0.2450 | |
|
| 0.2683 | 0.6 | 1800 | 0.2430 | |
|
| 0.1966 | 0.6333 | 1900 | 0.2416 | |
|
| 0.2451 | 0.6667 | 2000 | 0.2403 | |
|
| 0.2247 | 0.7 | 2100 | 0.2393 | |
|
| 0.1865 | 0.7333 | 2200 | 0.2384 | |
|
| 0.2837 | 0.7667 | 2300 | 0.2378 | |
|
| 0.2312 | 0.8 | 2400 | 0.2371 | |
|
| 0.239 | 0.8333 | 2500 | 0.2365 | |
|
| 0.2064 | 0.8667 | 2600 | 0.2362 | |
|
| 0.208 | 0.9 | 2700 | 0.2358 | |
|
| 0.2588 | 0.9333 | 2800 | 0.2356 | |
|
| 0.2029 | 0.9667 | 2900 | 0.2356 | |
|
| 0.2404 | 1.0 | 3000 | 0.2356 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.13.2 |
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.1+cu121 |
|
- Datasets 3.0.1 |
|
- Tokenizers 0.19.1 |
