|
--- |
|
base_model: meta-llama/Llama-2-7b-hf-adapter |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: Llama-2-7b-hf-IDMGSP |
|
results: [] |
|
license: mit |
|
datasets: |
|
- tum-nlp/IDMGSP |
|
language: |
|
- da |
|
library_name: transformers |
|
--- |
|
|
|
<!-- 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-2-7b-hf-IDMGSP |
|
|
|
This model is a LoRA adapter of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the [tum-nlp/IDMGSP](https://huggingface.co/datasets/tum-nlp/IDMGSP) dataset. |
|
It achieves the following results on the evaluation split: |
|
- Loss: 0.1450 |
|
- Accuracy: {'accuracy': 0.9759036144578314} |
|
- F1: {'f1': 0.9758125472411187} |
|
|
|
## Model description |
|
|
|
Model loaded fine-tuned in 4bit quantization mode using LoRA. |
|
|
|
## Intended uses & limitations |
|
Labels: `0` non-AI generated, `1` AI generated. |
|
|
|
For classifying AI generated text. Code to run the inference |
|
|
|
```python |
|
import transformers |
|
import torch |
|
import datasets |
|
import numpy as np |
|
import torch |
|
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, PeftModel, AutoPeftModelForCausalLM, TaskType |
|
import bitsandbytes as bnb |
|
|
|
class Model(): |
|
def __init__(self, name) -> None: |
|
# Tokenizer |
|
self.tokenizer = transformers.LlamaTokenizer.from_pretrained(self.name) |
|
self.tokenizer.pad_token = self.tokenizer.eos_token |
|
print(f"Tokenizer: {self.tokenizer.eos_token}; Pad {self.tokenizer.pad_token}") |
|
|
|
# Model |
|
bnb_config = transformers.BitsAndBytesConfig( |
|
load_in_4bit = True, |
|
bnb_4bit_use_double_quant = True, |
|
bnb_4bit_quant_type = "nf4", |
|
bnb_4bit_compute_dtype = "bfloat16", |
|
) |
|
self.peft_config = LoraConfig( |
|
task_type=TaskType.SEQ_CLS, r=8, lora_alpha=16, lora_dropout=0.05, bias="none" |
|
) |
|
self.model = transformers.LlamaForSequenceClassification.from_pretrained(self.name, |
|
num_labels=2, |
|
quantization_config = bnb_config, |
|
device_map = "auto" |
|
) |
|
self.model.config.pad_token_id = self.model.config.eos_token_id |
|
|
|
def predict(self, text): |
|
inputs = self.tokenize(text) |
|
outputs = self.model(**inputs) |
|
logits = outputs.logits |
|
predictions = torch.argmax(logits, dim=-1) |
|
return id2label[predictions.item()] |
|
``` |
|
|
|
|
|
## Training and evaluation data |
|
|
|
[tum-nlp/IDMGSP](https://huggingface.co/datasets/tum-nlp/IDMGSP) dataset, `classifier_input` subsplit. |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
BitsAndBytes and LoRA config parameters: |
|
|
|
![image/png](https://cdn-uploads.huggingface.co/production/uploads/638f0f9ab0525fa370479467/XI1imFyXmzFjCGCkBYClc.png) |
|
|
|
GPU VRAM Consumption during fine-tuning: 30.6gb |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 0.0001 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- lr_scheduler_warmup_steps: 500 |
|
- num_epochs: 5 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:| |
|
| 0.0766 | 1.0 | 498 | 0.1165 | {'accuracy': 0.9614708835341366} | {'f1': 0.9612813721780804} | |
|
| 0.182 | 2.0 | 996 | 0.0934 | {'accuracy': 0.9657379518072289} | {'f1': 0.9648059816939539} | |
|
| 0.037 | 3.0 | 1494 | 0.1190 | {'accuracy': 0.9716365461847389} | {'f1': 0.9710182097973841} | |
|
| 0.0349 | 4.0 | 1992 | 0.1884 | {'accuracy': 0.96875} | {'f1': 0.9692326702088224} | |
|
| 0.0046 | 5.0 | 2490 | 0.1450 | {'accuracy': 0.9759036144578314} | {'f1': 0.9758125472411187} | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.0 |
|
- Pytorch 2.0.1 |
|
- Datasets 2.14.6 |
|
- Tokenizers 0.14.1 |