File size: 2,075 Bytes
a6e582d b9eded9 a6e582d 4bfd998 a6e582d b9eded9 a6e582d 4bfd998 b9eded9 a6e582d |
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 |
---
library_name: transformers
license: mit
datasets:
- heegyu/open-korean-instructions
language:
- ko
tags:
- Llama-2-7b-hf
- LoRA
---
# Llama-2 model fine tuning (TREX-Lab at Seoul Cyber University)
<!-- Provide a quick summary of what the model is/does. -->
## Summary
- Base Model : meta-llama/Llama-2-7b-hf
- Dataset : heegyu/open-korean-instructions (100%)
- Tuning Method
- PEFT(Parameter Efficient Fine-Tuning)
- LoRA(Low-Rank Adaptation of Large Language Models)
- Related Articles : https://arxiv.org/abs/2106.09685
- Fine-tuning the Llama2 model with a random 100% of Korean chatbot data (open Korean instructions)
- Test whether fine tuning of a large language model is possible on A30 GPU*1 (successful)
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [TREX-Lab at Seoul Cyber University]
- **Language(s) (NLP):** [Korean]
- **Finetuned from model :** [meta-llama/Llama-2-7b-hf]
## Fine Tuning Detail
- alpha value 16
- r value 64 (it seems a bit big...@@)
```
peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=64,
bias='none',
task_type='CAUSAL_LM'
)
```
- Mixed precision : 4bit (bnb_4bit_use_double_quant)
```
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_compute_dtype='float16',
)
```
- Use SFT trainer (https://huggingface.co/docs/trl/sft_trainer)
```
trainer = SFTTrainer(
model=peft_model,
train_dataset=dataset,
dataset_text_field='text',
max_seq_length=min(tokenizer.model_max_length, 2048),
tokenizer=tokenizer,
packing=True,
args=training_args
)
```
### Train Result
```
time taken : executed in 2d 0h 17m
```
```
TrainOutput(global_step=2001,
training_loss=0.6940358212922347,
metrics={
'train_runtime': 173852.2333,
'train_samples_per_second': 0.092,
'train_steps_per_second': 0.012,
'train_loss': 0.6940358212922347,
'epoch': 3.0})
```
|