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---
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- generated_from_trainer
model-index:
- name: autofix10k
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. -->
# autofix10k
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: 0.4372
## 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: QuantizationMethod.BITS_AND_BYTES
- _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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7922 | 0.2 | 20 | 0.5237 |
| 0.5053 | 0.4 | 40 | 0.4857 |
| 0.4071 | 0.6 | 60 | 0.4356 |
| 0.4297 | 0.8 | 80 | 0.4154 |
| 0.5313 | 1.0 | 100 | 0.3827 |
| 0.4814 | 1.2 | 120 | 0.3785 |
| 0.3739 | 1.4 | 140 | 0.3774 |
| 0.3279 | 1.6 | 160 | 0.3761 |
| 0.3149 | 1.8 | 180 | 0.3732 |
| 0.4086 | 2.0 | 200 | 0.3658 |
| 0.3724 | 2.2 | 220 | 0.3664 |
| 0.3691 | 2.4 | 240 | 0.3644 |
| 0.3065 | 2.6 | 260 | 0.3679 |
| 0.2688 | 2.8 | 280 | 0.3767 |
| 0.3431 | 3.0 | 300 | 0.3633 |
| 0.333 | 3.2 | 320 | 0.3641 |
| 0.3052 | 3.4 | 340 | 0.3597 |
| 0.2444 | 3.6 | 360 | 0.3779 |
| 0.2455 | 3.8 | 380 | 0.3712 |
| 0.3078 | 4.0 | 400 | 0.3578 |
| 0.2877 | 4.2 | 420 | 0.3650 |
| 0.2659 | 4.4 | 440 | 0.3731 |
| 0.2496 | 4.6 | 460 | 0.3764 |
| 0.218 | 4.8 | 480 | 0.3781 |
| 0.219 | 5.0 | 500 | 0.3742 |
| 0.2119 | 5.2 | 520 | 0.3808 |
| 0.2435 | 5.4 | 540 | 0.3871 |
| 0.2331 | 5.6 | 560 | 0.3818 |
| 0.1738 | 5.8 | 580 | 0.3758 |
| 0.1772 | 6.0 | 600 | 0.3731 |
| 0.1607 | 6.2 | 620 | 0.4121 |
| 0.1942 | 6.4 | 640 | 0.3943 |
| 0.2312 | 6.6 | 660 | 0.3867 |
| 0.1528 | 6.8 | 680 | 0.4160 |
| 0.1155 | 7.0 | 700 | 0.4100 |
| 0.1495 | 7.2 | 720 | 0.4081 |
| 0.1674 | 7.4 | 740 | 0.4015 |
| 0.1849 | 7.6 | 760 | 0.4075 |
| 0.1231 | 7.8 | 780 | 0.4238 |
| 0.0905 | 8.0 | 800 | 0.4128 |
| 0.1156 | 8.2 | 820 | 0.4278 |
| 0.1628 | 8.4 | 840 | 0.4203 |
| 0.1545 | 8.6 | 860 | 0.4219 |
| 0.1236 | 8.8 | 880 | 0.4294 |
| 0.0799 | 9.0 | 900 | 0.4224 |
| 0.0991 | 9.2 | 920 | 0.4399 |
| 0.1176 | 9.4 | 940 | 0.4350 |
| 0.1711 | 9.6 | 960 | 0.4362 |
| 0.1106 | 9.8 | 980 | 0.4414 |
| 0.0582 | 10.0 | 1000 | 0.4372 |
### Framework versions
- PEFT 0.4.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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