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---
base_model: codellama/CodeLlama-7b-hf
library_name: peft
license: llama2
tags:
- axolotl
- generated_from_trainer
- autoquant
- gguf
model-index:
- name: EvolCodeLlama-7b
results: []
---
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
base_model: codellama/CodeLlama-7b-hf
base_model_config: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
hub_model_id: EvolCodeLlama-7b
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: mlabonne/Evol-Instruct-Python-1k
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.02
output_dir: ./qlora-out
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
</details><br>
# EvolCodeLlama-7b
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3796
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4828 | 0.0086 | 1 | 0.4975 |
| 0.4056 | 0.0343 | 4 | 0.4976 |
| 0.5046 | 0.0685 | 8 | 0.4973 |
| 0.3969 | 0.1028 | 12 | 0.4966 |
| 0.3404 | 0.1370 | 16 | 0.4947 |
| 0.4645 | 0.1713 | 20 | 0.4896 |
| 0.2892 | 0.2056 | 24 | 0.4789 |
| 0.2616 | 0.2398 | 28 | 0.4616 |
| 0.2586 | 0.2741 | 32 | 0.4430 |
| 0.3147 | 0.3084 | 36 | 0.4267 |
| 0.3686 | 0.3426 | 40 | 0.4158 |
| 0.2935 | 0.3769 | 44 | 0.4084 |
| 0.2419 | 0.4111 | 48 | 0.4026 |
| 0.2791 | 0.4454 | 52 | 0.3970 |
| 0.2381 | 0.4797 | 56 | 0.3922 |
| 0.2407 | 0.5139 | 60 | 0.3888 |
| 0.2686 | 0.5482 | 64 | 0.3872 |
| 0.3673 | 0.5824 | 68 | 0.3880 |
| 0.2665 | 0.6167 | 72 | 0.3848 |
| 0.3259 | 0.6510 | 76 | 0.3830 |
| 0.236 | 0.6852 | 80 | 0.3801 |
| 0.2301 | 0.7195 | 84 | 0.3786 |
| 0.3573 | 0.7537 | 88 | 0.3766 |
| 0.2409 | 0.7880 | 92 | 0.3745 |
| 0.3192 | 0.8223 | 96 | 0.3744 |
| 0.2652 | 0.8565 | 100 | 0.3720 |
| 0.2341 | 0.8908 | 104 | 0.3712 |
| 0.3651 | 0.9251 | 108 | 0.3709 |
| 0.1667 | 0.9593 | 112 | 0.3714 |
| 0.2755 | 0.9936 | 116 | 0.3699 |
| 0.2906 | 1.0254 | 120 | 0.3712 |
| 0.2079 | 1.0593 | 124 | 0.3708 |
| 0.3429 | 1.0932 | 128 | 0.3708 |
| 0.3296 | 1.1271 | 132 | 0.3721 |
| 0.2231 | 1.1610 | 136 | 0.3707 |
| 0.2098 | 1.1949 | 140 | 0.3686 |
| 0.2918 | 1.2288 | 144 | 0.3711 |
| 0.3803 | 1.2627 | 148 | 0.3676 |
| 0.2619 | 1.2966 | 152 | 0.3662 |
| 0.2261 | 1.3305 | 156 | 0.3679 |
| 0.1954 | 1.3644 | 160 | 0.3689 |
| 0.2183 | 1.3983 | 164 | 0.3677 |
| 0.2459 | 1.4322 | 168 | 0.3674 |
| 0.1979 | 1.4661 | 172 | 0.3669 |
| 0.2175 | 1.5 | 176 | 0.3653 |
| 0.26 | 1.5339 | 180 | 0.3652 |
| 0.2195 | 1.5678 | 184 | 0.3645 |
| 0.3344 | 1.6017 | 188 | 0.3645 |
| 0.1769 | 1.6356 | 192 | 0.3643 |
| 0.1829 | 1.6695 | 196 | 0.3639 |
| 0.2343 | 1.7034 | 200 | 0.3649 |
| 0.2568 | 1.7373 | 204 | 0.3650 |
| 0.1749 | 1.7712 | 208 | 0.3640 |
| 0.2118 | 1.8051 | 212 | 0.3628 |
| 0.2252 | 1.8390 | 216 | 0.3611 |
| 0.2301 | 1.8729 | 220 | 0.3602 |
| 0.1884 | 1.9068 | 224 | 0.3602 |
| 0.2023 | 1.9407 | 228 | 0.3600 |
| 0.2428 | 1.9746 | 232 | 0.3587 |
| 0.2413 | 2.0064 | 236 | 0.3583 |
| 0.2015 | 2.0407 | 240 | 0.3620 |
| 0.2131 | 2.0749 | 244 | 0.3728 |
| 0.1768 | 2.1092 | 248 | 0.3834 |
| 0.1615 | 2.1435 | 252 | 0.3810 |
| 0.1598 | 2.1777 | 256 | 0.3775 |
| 0.171 | 2.2120 | 260 | 0.3763 |
| 0.1973 | 2.2463 | 264 | 0.3759 |
| 0.1407 | 2.2805 | 268 | 0.3758 |
| 0.1998 | 2.3148 | 272 | 0.3771 |
| 0.1267 | 2.3490 | 276 | 0.3773 |
| 0.1526 | 2.3833 | 280 | 0.3782 |
| 0.1547 | 2.4176 | 284 | 0.3776 |
| 0.1439 | 2.4518 | 288 | 0.3768 |
| 0.1565 | 2.4861 | 292 | 0.3757 |
| 0.2113 | 2.5203 | 296 | 0.3767 |
| 0.1768 | 2.5546 | 300 | 0.3776 |
| 0.2366 | 2.5889 | 304 | 0.3792 |
| 0.1397 | 2.6231 | 308 | 0.3801 |
| 0.3598 | 2.6574 | 312 | 0.3805 |
| 0.1296 | 2.6916 | 316 | 0.3803 |
| 0.1344 | 2.7259 | 320 | 0.3805 |
| 0.2095 | 2.7602 | 324 | 0.3804 |
| 0.1646 | 2.7944 | 328 | 0.3800 |
| 0.1749 | 2.8287 | 332 | 0.3799 |
| 0.1597 | 2.8630 | 336 | 0.3800 |
| 0.1602 | 2.8972 | 340 | 0.3799 |
| 0.1786 | 2.9315 | 344 | 0.3797 |
| 0.1692 | 2.9657 | 348 | 0.3797 |
| 0.1887 | 3.0 | 352 | 0.3796 |
### Framework versions
- PEFT 0.13.0
- Transformers 4.45.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0 |