second_sql_converter_model – AI Model by CHAFIK12 | AlphaNeural AI

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second_sql_converter_model

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codellama/CodeLlama-7b-hf

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fine_tuning_codellama

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: 0.1513

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
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
1.1085	0.1010	20	1.0942
0.729	0.2020	40	0.5856
0.4941	0.3030	60	0.3775
0.2508	0.4040	80	0.2868
0.2139	0.5051	100	0.2528
0.2612	0.6061	120	0.2115
0.154	0.7071	140	0.2073
0.2558	0.8081	160	0.1936
0.1553	0.9091	180	0.1832
0.1831	1.0101	200	0.1872
0.2135	1.1111	220	0.1729
0.1278	1.2121	240	0.1724
0.21	1.3131	260	0.1676
0.1328	1.4141	280	0.1619
0.1731	1.5152	300	0.1651
0.1668	1.6162	320	0.1577
0.1259	1.7172	340	0.1565
0.191	1.8182	360	0.1543
0.129	1.9192	380	0.1515
0.1693	2.0202	400	0.1513

Framework versions

PEFT 0.6.0.dev0
Transformers 4.42.0.dev0
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1