---
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: `0.3.0`
```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: 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: 0.3797
## 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: bitsandbytes
- 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
### 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3472 | 0.01 | 1 | 0.4986 |
| 0.3139 | 0.03 | 4 | 0.4985 |
| 0.2981 | 0.07 | 8 | 0.4983 |
| 0.4311 | 0.1 | 12 | 0.4979 |
| 0.3958 | 0.14 | 16 | 0.4960 |
| 0.335 | 0.17 | 20 | 0.4915 |
| 0.4286 | 0.2 | 24 | 0.4808 |
| 0.4011 | 0.24 | 28 | 0.4629 |
| 0.3269 | 0.27 | 32 | 0.4445 |
| 0.2559 | 0.31 | 36 | 0.4284 |
| 0.3786 | 0.34 | 40 | 0.4174 |
| 0.2967 | 0.37 | 44 | 0.4107 |
| 0.2677 | 0.41 | 48 | 0.4027 |
| 0.2455 | 0.44 | 52 | 0.3959 |
| 0.3267 | 0.47 | 56 | 0.3916 |
| 0.2902 | 0.51 | 60 | 0.3882 |
| 0.1845 | 0.54 | 64 | 0.3878 |
| 0.2593 | 0.58 | 68 | 0.3869 |
| 0.3104 | 0.61 | 72 | 0.3836 |
| 0.3799 | 0.64 | 76 | 0.3819 |
| 0.2059 | 0.68 | 80 | 0.3794 |
| 0.3177 | 0.71 | 84 | 0.3792 |
| 0.2307 | 0.75 | 88 | 0.3768 |
| 0.282 | 0.78 | 92 | 0.3749 |
| 0.2713 | 0.81 | 96 | 0.3738 |
| 0.2948 | 0.85 | 100 | 0.3725 |
| 0.2311 | 0.88 | 104 | 0.3713 |
| 0.2516 | 0.92 | 108 | 0.3716 |
| 0.2462 | 0.95 | 112 | 0.3715 |
| 0.2035 | 0.98 | 116 | 0.3711 |
| 0.2638 | 1.02 | 120 | 0.3712 |
| 0.2477 | 1.05 | 124 | 0.3726 |
| 0.1986 | 1.08 | 128 | 0.3682 |
| 0.2292 | 1.12 | 132 | 0.3671 |
| 0.1549 | 1.15 | 136 | 0.3680 |
| 0.1953 | 1.19 | 140 | 0.3683 |
| 0.224 | 1.22 | 144 | 0.3671 |
| 0.1941 | 1.25 | 148 | 0.3687 |
| 0.2234 | 1.29 | 152 | 0.3709 |
| 0.2659 | 1.32 | 156 | 0.3700 |
| 0.2535 | 1.36 | 160 | 0.3689 |
| 0.2115 | 1.39 | 164 | 0.3683 |
| 0.2481 | 1.42 | 168 | 0.3693 |
| 0.2101 | 1.46 | 172 | 0.3699 |
| 0.228 | 1.49 | 176 | 0.3697 |
| 0.3159 | 1.53 | 180 | 0.3680 |
| 0.2257 | 1.56 | 184 | 0.3664 |
| 0.1684 | 1.59 | 188 | 0.3670 |
| 0.2277 | 1.63 | 192 | 0.3663 |
| 0.2787 | 1.66 | 196 | 0.3668 |
| 0.2284 | 1.69 | 200 | 0.3654 |
| 0.2789 | 1.73 | 204 | 0.3640 |
| 0.2089 | 1.76 | 208 | 0.3632 |
| 0.3387 | 1.8 | 212 | 0.3633 |
| 0.2677 | 1.83 | 216 | 0.3610 |
| 0.2684 | 1.86 | 220 | 0.3609 |
| 0.2458 | 1.9 | 224 | 0.3610 |
| 0.2808 | 1.93 | 228 | 0.3602 |
| 0.2895 | 1.97 | 232 | 0.3596 |
| 0.323 | 2.0 | 236 | 0.3591 |
| 0.2105 | 2.03 | 240 | 0.3623 |
| 0.1911 | 2.07 | 244 | 0.3720 |
| 0.2888 | 2.1 | 248 | 0.3802 |
| 0.1958 | 2.13 | 252 | 0.3748 |
| 0.1785 | 2.17 | 256 | 0.3701 |
| 0.2604 | 2.2 | 260 | 0.3709 |
| 0.2212 | 2.24 | 264 | 0.3737 |
| 0.1996 | 2.27 | 268 | 0.3772 |
| 0.1567 | 2.3 | 272 | 0.3778 |
| 0.1777 | 2.34 | 276 | 0.3778 |
| 0.2642 | 2.37 | 280 | 0.3785 |
| 0.1907 | 2.4 | 284 | 0.3796 |
| 0.1637 | 2.44 | 288 | 0.3785 |
| 0.1778 | 2.47 | 292 | 0.3785 |
| 0.144 | 2.51 | 296 | 0.3789 |
| 0.1758 | 2.54 | 300 | 0.3788 |
| 0.2018 | 2.57 | 304 | 0.3784 |
| 0.3126 | 2.61 | 308 | 0.3783 |
| 0.1623 | 2.64 | 312 | 0.3790 |
| 0.223 | 2.68 | 316 | 0.3798 |
| 0.2109 | 2.71 | 320 | 0.3797 |
| 0.1606 | 2.74 | 324 | 0.3797 |
| 0.2226 | 2.78 | 328 | 0.3796 |
| 0.2068 | 2.81 | 332 | 0.3798 |
| 0.1547 | 2.85 | 336 | 0.3797 |
| 0.2513 | 2.88 | 340 | 0.3796 |
| 0.2688 | 2.91 | 344 | 0.3797 |
| 0.1481 | 2.95 | 348 | 0.3796 |
| 0.1443 | 2.98 | 352 | 0.3797 |
### Framework versions
- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0