metadata
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Llama-3.2-1B-binary-citation-classifier
results: []
Llama-3.2-1B-binary-citation-classifier
This model is a fine-tuned version of meta-llama/Llama-3.2-1B on a dataset of scientific abstracts and citation counts. Its aim is to predict, based on an article abstract, if an article will be cited within five years or not. It achieves the following results on the evaluation set:
- Loss: 0.5450
- Accuracy: 0.746
- F1: 0.7460
- Precision: 0.7460
- Recall: 0.746
Model description
Llama-3.2-1B architecture, modified with a rank 8 LORA adapter.
Intended uses & limitations
Intended use is binary classification. The training set consists of PubMed indexed neuroscience-related articles exclusively.
Training and evaluation data
Training procedure
Pre-training following Meta's procedures. LORA fine tuning with PEFT on 16k abstracts (8k cited, 8k uncited)
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.6249 | 1.0 | 500 | 0.5853 | 0.716 | 0.7160 | 0.7161 | 0.716 |
| 0.5585 | 2.0 | 1000 | 0.5523 | 0.748 | 0.7478 | 0.7487 | 0.748 |
| 0.6066 | 3.0 | 1500 | 0.5303 | 0.7535 | 0.7535 | 0.7535 | 0.7535 |
| 0.5447 | 4.0 | 2000 | 0.5202 | 0.761 | 0.7609 | 0.7615 | 0.761 |
| 0.4709 | 5.0 | 2500 | 0.5168 | 0.7645 | 0.7645 | 0.7645 | 0.7645 |
| 0.5002 | 6.0 | 3000 | 0.5137 | 0.7695 | 0.7695 | 0.7696 | 0.7695 |
Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2