File size: 2,270 Bytes
e989a0a 636127d e989a0a ed00d68 e989a0a 636127d e989a0a ed00d68 c888403 ed00d68 54d69eb ed00d68 e989a0a ed00d68 e989a0a |
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 |
---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets:
- generator
library_name: peft
license: llama3.2
tags:
- trl
- sft
- generated_from_trainer
- lora
model-index:
- name: Llama-3.2-1B-Indonesian
results: []
language:
- id
pipeline_tag: text-generation
---
<!-- 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-Indonesian
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) that has been optimized for Indonesian language understanding and generation.
<br>
<br>
The fine-tuning process utilized Low-Rank Adaptation (LoRA) to efficiently adapt the model while minimizing computational and storage overhead. This approach enables effective fine-tuning for specific tasks or domains, particularly in the Indonesian language context.
## Training and evaluation data
[Ichsan2895/alpaca-gpt4-indonesian](https://huggingface.co/datasets/Ichsan2895/alpaca-gpt4-indonesian)
### Use WIth Transformers
```python
import torch
from transformers import pipeline
model_id = "digo-prayudha/Llama-3.2-1B-Indonesian"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "user", "content": "Tentukan subjek dari kalimat berikut: 'Film tersebut dirilis kemarin'."},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 6
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
![Train Loss]
<img src="./train_loss.svg" width="300" height="200">
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.46.1
- Pytorch 2.4.0+cu121
- Datasets 2.16.1
- Tokenizers 0.20.1 |