Model Card for Model ID
The GlueQwen model is fine-tuned on four distinct tasks from the GLUE benchmark: SST-2 (Sentiment Analysis), MRPC (Paraphrase Detection), CoLA (Linguistic Acceptability), and MNLI (Natural Language Inference). The base model used is Qwen/Qwen2.5-7B, which has 7 billion parameters. Qwen2.5-7B is designed to enhance various natural language understanding tasks through pre-training on diverse datasets, followed by fine-tuning for task-specific improvements.
The fine-tuning of GlueQwen involves optimizing the model for these GLUE tasks, aiming to measure catastrophic forgetting, model learning abilities, and overall training performance across different language tasks. These benchmarks provide insight into how well the model retains previous knowledge while learning new tasks sequentially.
Model Details
Model Description
Benchmark Table for GlueQwen Fine-Tuning Performance
| Model | Parameter Size (B) | Pretrained Performance | Forgetting | Learning | Training Performance |
|---|
| Llama-3.2-1B | 1 | 0.50 | 0.24 | 0.33 | 0.54 |
| Llama-3.2-3B | 3 | 0.56 | 0.225 | 0.36 | 0.61 |
| Llama-3.1-8B | 8 | 0.56 | 0.59 | 0.84 | 0.67 |
| Llama-3-8B | 8 | 0.53 | 0.39 | 0.98 | 0.70 |
| Llama-2-7B | 7 | 0.67 | 0.23 | 0.12 | 0.63 |
| GPT-J-6B | 6 | 0.50 | 0.39 | 0.45 | 0.54 |
| Phi-2 | 2.7 | 0.59 | 0.10 | 0.15 | 0.61 |
| Phi-3.5-mini | 3.82 | 0.69 | 0.02 | 0.30 | 0.76 |
| Orca-2-7b | 7 | 0.76 | 0.185 | 0.33 | 0.81 |
| Qwen2.5-0.5B | 0.5 | 0.52 | 0.23 | 0.56 | 0.61 |
| Qwen2.5-7B | 7 | 0.56 | 0.51 | 1.12 | 0.77 |
| Qwen2.5-14B | 14 | 0.71 | 0.935 | 0.66 | 0.80 |
| GlueQwen | 7 | 0.59 | 0.42 | 0.97 | 0.73 |
Analysis
GlueQwen, fine-tuned on multiple tasks from the GLUE dataset, demonstrates a pre-trained performance of 0.59. Its forgetting rate is moderate at 0.42, reflecting some loss of previously learned information. However, the model exhibits a strong learning capability with a learning score of 0.97. The overall training performance stands at 0.73, positioning GlueQwen as a balanced model that manages forgetting while achieving significant improvements in task-specific learning.
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