---
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- generated_from_trainer
model-index:
- name: sql-code-llama
  results: []
library_name: peft
---

<!-- 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. -->

# sql-code-llama

This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4577

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.1953        | 0.0465 | 20   | 2.0335          |
| 1.1292        | 0.0931 | 40   | 0.8342          |
| 0.8133        | 0.1396 | 60   | 0.6552          |
| 0.5873        | 0.1862 | 80   | 0.5861          |
| 0.4095        | 0.2327 | 100  | 0.5589          |
| 0.5731        | 0.2792 | 120  | 0.5159          |
| 0.4221        | 0.3258 | 140  | 0.5039          |
| 0.6365        | 0.3723 | 160  | 0.5159          |
| 0.4779        | 0.4188 | 180  | 0.4867          |
| 0.3584        | 0.4654 | 200  | 0.5007          |
| 0.5325        | 0.5119 | 220  | 0.4802          |
| 0.3998        | 0.5585 | 240  | 0.4767          |
| 0.5952        | 0.6050 | 260  | 0.4777          |
| 0.4649        | 0.6515 | 280  | 0.4671          |
| 0.3394        | 0.6981 | 300  | 0.4752          |
| 0.5084        | 0.7446 | 320  | 0.4669          |
| 0.3934        | 0.7912 | 340  | 0.4613          |
| 0.5762        | 0.8377 | 360  | 0.4617          |
| 0.4563        | 0.8842 | 380  | 0.4586          |
| 0.345         | 0.9308 | 400  | 0.4577          |


### 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