sqlcoder-7b-2_FineTuned_QLORA_Adapter
This model is a fine-tuned version of defog/sqlcoder-7b-2 on 260 SQL examples (Task, Schema and Answer triplets) related to financial/banking domain.
Intended uses & limitations
MS SQL Server - SQL Query Generation
Training
This model was trained using the QLoRA method with the following configurations:
- r = 64,
- lora_alpha = 32
- lora_dropout = 0.05
- bias='none'
- task_type='CAUSAL_LM'
Quantization parameters:
- load_in_4bit=True
- bnb_4bit_quant_type="nf4"
- bnb_4bit_compute_dtype=torch.bfloat16
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 5
- mixed_precision_training: Native AMP
Framework versions
- PEFT 0.13.2
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
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Model tree for CHZY-1/sqlcoder-7b-2_FineTuned_PEFT_QLORA_adapter
Base model
defog/sqlcoder-7b-2