sql-code-llama-alan

This model is a fine-tuned version of codellama/CodeLlama-7b-hf on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4576

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: bitsandbytes
• _load_in_8bit: True
• _load_in_4bit: False
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: fp4
• bnb_4bit_use_double_quant: False
• bnb_4bit_compute_dtype: float32
• bnb_4bit_quant_storage: uint8
• load_in_4bit: False
• load_in_8bit: True

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• training_steps: 400
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.1992	0.0465	20	2.0335
1.14	0.0931	40	0.8371
0.8045	0.1396	60	0.6549
0.584	0.1862	80	0.5715
0.3807	0.2327	100	0.5561
0.5723	0.2792	120	0.5147
0.4262	0.3258	140	0.5056
0.6375	0.3723	160	0.5191
0.4839	0.4188	180	0.4865
0.3596	0.4654	200	0.4994
0.5285	0.5119	220	0.4803
0.4035	0.5585	240	0.4753
0.6019	0.6050	260	0.4772
0.4663	0.6515	280	0.4670
0.345	0.6981	300	0.4746
0.509	0.7446	320	0.4652
0.3946	0.7912	340	0.4614
0.5714	0.8377	360	0.4614
0.4525	0.8842	380	0.4585
0.3432	0.9308	400	0.4576

Framework versions

• PEFT 0.6.0.dev0
• Transformers 4.44.0.dev0
• Pytorch 2.2.2+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1