---
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: ptoro/Evol-Instruct-Python-1k-testing
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: ""
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: 0.3828
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3627 | 0.01 | 1 | 0.5027 |
| 0.3412 | 0.03 | 4 | 0.5026 |
| 0.3806 | 0.07 | 8 | 0.5023 |
| 0.392 | 0.1 | 12 | 0.5018 |
| 0.4141 | 0.14 | 16 | 0.4999 |
| 0.3433 | 0.17 | 20 | 0.4954 |
| 0.3702 | 0.21 | 24 | 0.4851 |
| 0.2948 | 0.24 | 28 | 0.4682 |
| 0.3387 | 0.28 | 32 | 0.4499 |
| 0.2437 | 0.31 | 36 | 0.4331 |
| 0.2526 | 0.35 | 40 | 0.4221 |
| 0.2721 | 0.38 | 44 | 0.4146 |
| 0.2292 | 0.42 | 48 | 0.4089 |
| 0.1986 | 0.45 | 52 | 0.4028 |
| 0.3258 | 0.48 | 56 | 0.3983 |
| 0.3509 | 0.52 | 60 | 0.3950 |
| 0.2697 | 0.55 | 64 | 0.3926 |
| 0.2646 | 0.59 | 68 | 0.3907 |
| 0.3979 | 0.62 | 72 | 0.3900 |
| 0.2737 | 0.66 | 76 | 0.3880 |
| 0.2271 | 0.69 | 80 | 0.3865 |
| 0.247 | 0.73 | 84 | 0.3847 |
| 0.3112 | 0.76 | 88 | 0.3824 |
| 0.2724 | 0.8 | 92 | 0.3820 |
| 0.207 | 0.83 | 96 | 0.3814 |
| 0.3492 | 0.87 | 100 | 0.3810 |
| 0.2474 | 0.9 | 104 | 0.3802 |
| 0.4037 | 0.94 | 108 | 0.3785 |
| 0.2295 | 0.97 | 112 | 0.3773 |
| 0.2689 | 1.0 | 116 | 0.3760 |
| 0.2546 | 1.02 | 120 | 0.3753 |
| 0.1916 | 1.05 | 124 | 0.3768 |
| 0.2458 | 1.09 | 128 | 0.3758 |
| 0.2155 | 1.12 | 132 | 0.3768 |
| 0.2341 | 1.16 | 136 | 0.3773 |
| 0.1909 | 1.19 | 140 | 0.3793 |
| 0.1911 | 1.23 | 144 | 0.3759 |
| 0.2096 | 1.26 | 148 | 0.3761 |
| 0.2353 | 1.29 | 152 | 0.3772 |
| 0.2606 | 1.33 | 156 | 0.3773 |
| 0.1485 | 1.36 | 160 | 0.3778 |
| 0.1807 | 1.4 | 164 | 0.3749 |
| 0.2294 | 1.43 | 168 | 0.3770 |
| 0.216 | 1.47 | 172 | 0.3759 |
| 0.1791 | 1.5 | 176 | 0.3727 |
| 0.2605 | 1.54 | 180 | 0.3733 |
| 0.2838 | 1.57 | 184 | 0.3738 |
| 0.2632 | 1.61 | 188 | 0.3694 |
| 0.1839 | 1.64 | 192 | 0.3686 |
| 0.1939 | 1.68 | 196 | 0.3690 |
| 0.2413 | 1.71 | 200 | 0.3699 |
| 0.1494 | 1.74 | 204 | 0.3689 |
| 0.2782 | 1.78 | 208 | 0.3695 |
| 0.2314 | 1.81 | 212 | 0.3696 |
| 0.2499 | 1.85 | 216 | 0.3691 |
| 0.1976 | 1.88 | 220 | 0.3672 |
| 0.2587 | 1.92 | 224 | 0.3660 |
| 0.2598 | 1.95 | 228 | 0.3658 |
| 0.2686 | 1.99 | 232 | 0.3666 |
| 0.216 | 2.01 | 236 | 0.3673 |
| 0.1261 | 2.04 | 240 | 0.3723 |
| 0.1938 | 2.08 | 244 | 0.3811 |
| 0.1906 | 2.11 | 248 | 0.3869 |
| 0.1375 | 2.15 | 252 | 0.3829 |
| 0.228 | 2.18 | 256 | 0.3796 |
| 0.2524 | 2.22 | 260 | 0.3789 |
| 0.118 | 2.25 | 264 | 0.3809 |
| 0.2224 | 2.29 | 268 | 0.3834 |
| 0.1477 | 2.32 | 272 | 0.3847 |
| 0.2095 | 2.35 | 276 | 0.3849 |
| 0.1919 | 2.39 | 280 | 0.3820 |
| 0.1916 | 2.42 | 284 | 0.3804 |
| 0.1625 | 2.46 | 288 | 0.3788 |
| 0.2054 | 2.49 | 292 | 0.3794 |
| 0.1605 | 2.53 | 296 | 0.3810 |
| 0.1564 | 2.56 | 300 | 0.3819 |
| 0.196 | 2.6 | 304 | 0.3822 |
| 0.1975 | 2.63 | 308 | 0.3830 |
| 0.1406 | 2.67 | 312 | 0.3833 |
| 0.2754 | 2.7 | 316 | 0.3830 |
| 0.1544 | 2.74 | 320 | 0.3829 |
| 0.1733 | 2.77 | 324 | 0.3830 |
| 0.1862 | 2.81 | 328 | 0.3832 |
| 0.1634 | 2.84 | 332 | 0.3829 |
| 0.1966 | 2.87 | 336 | 0.3830 |
| 0.1306 | 2.91 | 340 | 0.3831 |
| 0.1444 | 2.94 | 344 | 0.3828 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0