đ reranker_continuous_train
This project provides a fine - tuned model for text ranking. It is based on the Qwen/Qwen2.5 - 0.5B - Instruct model and fine - tuned on the reranker_continuous_train dataset, achieving a loss of 0.3195 on the evaluation set.
đ Quick Start
The README does not provide specific quick - start steps. You can refer to the official documentation of the transformers library and the original model for more information.
⨠Features
- Fine - Tuned Model: Based on the Qwen/Qwen2.5 - 0.5B - Instruct model, fine - tuned on the reranker_continuous_train dataset.
- Quantization Support: Multiple quantization methods are available, with different sizes to meet various needs.
đĻ Installation
The README does not provide specific installation steps. You can install the necessary dependencies according to the official requirements of the transformers library.
đģ Usage Examples
The README does not provide specific code examples. You can refer to the official documentation of the transformers library for model usage.
đ Documentation
Model Information
- Pipeline Tag: text - ranking
- Quantization: Made by Richard Erkhov.
Reranker_0.5_cont - GGUF
Original Model Description
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: reranker_continuous_train
results: []
Model Details
This model is a fine - tuned version of Qwen/Qwen2.5 - 0.5B - Instruct on the reranker_continuous_train dataset. It achieves the following results on the evaluation set:
Training and Evaluation
Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e - 05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi - GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.0
Training Results
| Training Loss |
Epoch |
Step |
Validation Loss |
| 0.4242 |
0.1 |
2156 |
0.3844 |
| 0.236 |
0.2 |
4312 |
0.3643 |
| 0.6602 |
0.3 |
6468 |
0.3521 |
| 0.3464 |
0.4 |
8624 |
0.3472 |
| 0.3598 |
0.5 |
10780 |
0.3412 |
| 0.3377 |
0.6 |
12936 |
0.3341 |
| 0.4547 |
0.7 |
15092 |
0.3258 |
| 0.2282 |
0.8 |
17248 |
0.3228 |
| 0.2692 |
0.9 |
19404 |
0.3195 |
| 0.2059 |
1.0 |
21560 |
0.3195 |
Framework Versions
- Transformers 4.46.1
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
đ§ Technical Details
The README does not provide detailed technical implementation details.
đ License
The license of this model is "other". For more information, please refer to the relevant documentation.
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Transformers
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