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qwen1.5-1.8b-lora-insurance-codes
gaolearning/qwen1.5-1.8b-lora-insurance-codes
qwen1.5-1.8b-lora-insurance-codes is a text generation model from gaolearning. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as other.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Generation
- Library
- peft
- License
- other
- Access
- Public
- Created
- Nov 7, 2025
- Updated
- Nov 13, 2025
- SHA
- df74e61f
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
qwen1.5-1.8b-lora-insurance-codes
This model is a fine-tuned version of Qwen/Qwen1.5-1.8B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0295
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3397 | 1.7111 | 20 | 0.3000 |
| 0.042 | 3.3556 | 40 | 0.0413 |
| 0.0013 | 5.0 | 60 | 0.0311 |
| 0.0001 | 6.7111 | 80 | 0.0470 |
| 0.0 | 8.3556 | 100 | 0.0313 |
| 0.0 | 10.0 | 120 | 0.0295 |
Framework versions
- PEFT 0.17.1
- Transformers 4.57.1
- Pytorch 2.9.0
- Datasets 3.6.0
- Tokenizers 0.22.1
- Task
- Text Generation
- Library
- peft
- License
- other
- Created
- Nov 7, 2025
- Updated
- Nov 13, 2025