---
license: llama2
library_name: peft
tags:
- generated_from_trainer
base_model: codellama/CodeLlama-13b-Instruct-hf
model-index:
- name: stg-cli13b-t6-cdp-ca.mt.him.cln.inter-b4s1e1-20231220-1052
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# stg-cli13b-t6-cdp-ca.mt.him.cln.inter-b4s1e1-20231220-1052

This model is a fine-tuned version of [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0472

## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3435        | 0.03  | 100  | 0.0703          |
| 0.0654        | 0.07  | 200  | 0.0586          |
| 0.0579        | 0.1   | 300  | 0.0563          |
| 0.0567        | 0.14  | 400  | 0.0562          |
| 0.0551        | 0.17  | 500  | 0.0547          |
| 0.0547        | 0.21  | 600  | 0.0526          |
| 0.0532        | 0.24  | 700  | 0.0516          |
| 0.0534        | 0.28  | 800  | 0.0515          |
| 0.0521        | 0.31  | 900  | 0.0520          |
| 0.0522        | 0.35  | 1000 | 0.0517          |
| 0.0518        | 0.38  | 1100 | 0.0511          |
| 0.051         | 0.42  | 1200 | 0.0502          |
| 0.0517        | 0.45  | 1300 | 0.0494          |
| 0.0506        | 0.49  | 1400 | 0.0499          |
| 0.0511        | 0.52  | 1500 | 0.0496          |
| 0.05          | 0.56  | 1600 | 0.0493          |
| 0.05          | 0.59  | 1700 | 0.0497          |
| 0.049         | 0.63  | 1800 | 0.0485          |
| 0.0487        | 0.66  | 1900 | 0.0484          |
| 0.0492        | 0.7   | 2000 | 0.0483          |
| 0.0493        | 0.73  | 2100 | 0.0481          |
| 0.0483        | 0.77  | 2200 | 0.0478          |
| 0.048         | 0.8   | 2300 | 0.0478          |
| 0.048         | 0.83  | 2400 | 0.0476          |
| 0.0476        | 0.87  | 2500 | 0.0474          |
| 0.0471        | 0.9   | 2600 | 0.0473          |
| 0.0472        | 0.94  | 2700 | 0.0472          |
| 0.0469        | 0.97  | 2800 | 0.0472          |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- 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: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

### Framework versions


- PEFT 0.6.2