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