Llama-2-7b-hf-IDMGSP

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Llama-2-7b-hf-IDMGSP

This model is a LoRA adapter of meta-llama/Llama-2-7b-hf on the 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

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 dataset, classifier_input subsplit.

Training procedure

Training hyperparameters

BitsAndBytes and LoRA config parameters:

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

Framework versions

• Transformers 4.35.0
• Pytorch 2.0.1
• Datasets 2.14.6
• Tokenizers 0.14.1