---
license: llama2
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: codellama/CodeLlama-7b-hf
model-index:
- name: EvolCodeLlama-7b
results: []
---
[
](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config
axolotl version: `0.4.0`
```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: FTCodeLlama-2
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 4
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: ""
eos_token: ""
unk_token: ""
```
# 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: 0.3754
## 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: 4
- seed: 42
- gradient_accumulation_steps: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3686 | 0.01 | 1 | 0.5015 |
| 0.4397 | 0.03 | 3 | 0.5013 |
| 0.4919 | 0.06 | 6 | 0.5013 |
| 0.3191 | 0.09 | 9 | 0.5011 |
| 0.2514 | 0.12 | 12 | 0.5003 |
| 0.3379 | 0.15 | 15 | 0.4992 |
| 0.4712 | 0.19 | 18 | 0.4969 |
| 0.3801 | 0.22 | 21 | 0.4922 |
| 0.3482 | 0.25 | 24 | 0.4833 |
| 0.4113 | 0.28 | 27 | 0.4702 |
| 0.2524 | 0.31 | 30 | 0.4552 |
| 0.2641 | 0.34 | 33 | 0.4415 |
| 0.3554 | 0.37 | 36 | 0.4302 |
| 0.2384 | 0.4 | 39 | 0.4213 |
| 0.2131 | 0.43 | 42 | 0.4153 |
| 0.2308 | 0.46 | 45 | 0.4105 |
| 0.3478 | 0.49 | 48 | 0.4053 |
| 0.2913 | 0.53 | 51 | 0.4003 |
| 0.2909 | 0.56 | 54 | 0.3956 |
| 0.2032 | 0.59 | 57 | 0.3928 |
| 0.2479 | 0.62 | 60 | 0.3906 |
| 0.2145 | 0.65 | 63 | 0.3890 |
| 0.2447 | 0.68 | 66 | 0.3882 |
| 0.2928 | 0.71 | 69 | 0.3876 |
| 0.384 | 0.74 | 72 | 0.3854 |
| 0.1751 | 0.77 | 75 | 0.3835 |
| 0.352 | 0.8 | 78 | 0.3818 |
| 0.2443 | 0.84 | 81 | 0.3808 |
| 0.3211 | 0.87 | 84 | 0.3798 |
| 0.3026 | 0.9 | 87 | 0.3788 |
| 0.2357 | 0.93 | 90 | 0.3776 |
| 0.2661 | 0.96 | 93 | 0.3755 |
| 0.3314 | 0.99 | 96 | 0.3751 |
| 0.2789 | 1.02 | 99 | 0.3742 |
| 0.1734 | 1.03 | 102 | 0.3744 |
| 0.1928 | 1.06 | 105 | 0.3761 |
| 0.2681 | 1.09 | 108 | 0.3753 |
| 0.4148 | 1.12 | 111 | 0.3750 |
| 0.1977 | 1.15 | 114 | 0.3744 |
| 0.1977 | 1.19 | 117 | 0.3740 |
| 0.2499 | 1.22 | 120 | 0.3742 |
| 0.2192 | 1.25 | 123 | 0.3730 |
| 0.2207 | 1.28 | 126 | 0.3723 |
| 0.2179 | 1.31 | 129 | 0.3718 |
| 0.2843 | 1.34 | 132 | 0.3734 |
| 0.2614 | 1.37 | 135 | 0.3721 |
| 0.2033 | 1.4 | 138 | 0.3705 |
| 0.212 | 1.43 | 141 | 0.3705 |
| 0.2307 | 1.46 | 144 | 0.3712 |
| 0.3182 | 1.49 | 147 | 0.3698 |
| 0.2467 | 1.53 | 150 | 0.3664 |
| 0.1909 | 1.56 | 153 | 0.3665 |
| 0.3286 | 1.59 | 156 | 0.3655 |
| 0.2195 | 1.62 | 159 | 0.3648 |
| 0.3231 | 1.65 | 162 | 0.3650 |
| 0.2922 | 1.68 | 165 | 0.3631 |
| 0.1956 | 1.71 | 168 | 0.3627 |
| 0.2299 | 1.74 | 171 | 0.3639 |
| 0.1585 | 1.77 | 174 | 0.3649 |
| 0.2289 | 1.8 | 177 | 0.3650 |
| 0.189 | 1.84 | 180 | 0.3643 |
| 0.2736 | 1.87 | 183 | 0.3628 |
| 0.3591 | 1.9 | 186 | 0.3614 |
| 0.3181 | 1.93 | 189 | 0.3612 |
| 0.1994 | 1.96 | 192 | 0.3612 |
| 0.2499 | 1.99 | 195 | 0.3618 |
| 0.1659 | 2.01 | 198 | 0.3627 |
| 0.231 | 2.04 | 201 | 0.3665 |
| 0.169 | 2.07 | 204 | 0.3744 |
| 0.2082 | 2.1 | 207 | 0.3800 |
| 0.1755 | 2.13 | 210 | 0.3770 |
| 0.1959 | 2.16 | 213 | 0.3721 |
| 0.1933 | 2.19 | 216 | 0.3705 |
| 0.1213 | 2.22 | 219 | 0.3712 |
| 0.237 | 2.25 | 222 | 0.3738 |
| 0.2277 | 2.28 | 225 | 0.3771 |
| 0.2832 | 2.31 | 228 | 0.3789 |
| 0.2039 | 2.35 | 231 | 0.3783 |
| 0.2302 | 2.38 | 234 | 0.3764 |
| 0.1562 | 2.41 | 237 | 0.3750 |
| 0.1688 | 2.44 | 240 | 0.3742 |
| 0.126 | 2.47 | 243 | 0.3741 |
| 0.1846 | 2.5 | 246 | 0.3746 |
| 0.2195 | 2.53 | 249 | 0.3745 |
| 0.2335 | 2.56 | 252 | 0.3749 |
| 0.1542 | 2.59 | 255 | 0.3750 |
| 0.1783 | 2.62 | 258 | 0.3755 |
| 0.2409 | 2.65 | 261 | 0.3762 |
| 0.1777 | 2.69 | 264 | 0.3762 |
| 0.2591 | 2.72 | 267 | 0.3761 |
| 0.2092 | 2.75 | 270 | 0.3757 |
| 0.2256 | 2.78 | 273 | 0.3757 |
| 0.1923 | 2.81 | 276 | 0.3756 |
| 0.156 | 2.84 | 279 | 0.3755 |
| 0.2036 | 2.87 | 282 | 0.3754 |
| 0.2254 | 2.9 | 285 | 0.3753 |
| 0.1683 | 2.93 | 288 | 0.3753 |
| 0.1528 | 2.96 | 291 | 0.3754 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0