---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: peft
license: llama3.1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: EvolCodeLlama-3.1-8B-Instruct
results: []
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.4.1`
```yaml
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: true
hub_model_id: EvolCodeLlama-3.1-8B-Instruct
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:
pad_token: "<|end_of_text|>"
```
# EvolCodeLlama-3.1-8B-Instruct
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4057
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.388 | 0.0120 | 1 | 0.4443 |
| 0.3646 | 0.0359 | 3 | 0.4441 |
| 0.3216 | 0.0719 | 6 | 0.4439 |
| 0.3628 | 0.1078 | 9 | 0.4435 |
| 0.2506 | 0.1437 | 12 | 0.4417 |
| 0.2855 | 0.1796 | 15 | 0.4379 |
| 0.2472 | 0.2156 | 18 | 0.4310 |
| 0.3146 | 0.2515 | 21 | 0.4243 |
| 0.2829 | 0.2874 | 24 | 0.4185 |
| 0.2926 | 0.3234 | 27 | 0.4139 |
| 0.3832 | 0.3593 | 30 | 0.4099 |
| 0.3 | 0.3952 | 33 | 0.4069 |
| 0.2759 | 0.4311 | 36 | 0.4051 |
| 0.341 | 0.4671 | 39 | 0.4017 |
| 0.2268 | 0.5030 | 42 | 0.3989 |
| 0.3938 | 0.5389 | 45 | 0.3971 |
| 0.3478 | 0.5749 | 48 | 0.3951 |
| 0.2745 | 0.6108 | 51 | 0.3935 |
| 0.2623 | 0.6467 | 54 | 0.3920 |
| 0.3743 | 0.6826 | 57 | 0.3903 |
| 0.3205 | 0.7186 | 60 | 0.3898 |
| 0.332 | 0.7545 | 63 | 0.3897 |
| 0.268 | 0.7904 | 66 | 0.3876 |
| 0.2842 | 0.8263 | 69 | 0.3873 |
| 0.3677 | 0.8623 | 72 | 0.3868 |
| 0.212 | 0.8982 | 75 | 0.3857 |
| 0.2656 | 0.9341 | 78 | 0.3854 |
| 0.2499 | 0.9701 | 81 | 0.3844 |
| 0.3512 | 1.0060 | 84 | 0.3850 |
| 0.3069 | 1.0269 | 87 | 0.3848 |
| 0.3037 | 1.0629 | 90 | 0.3856 |
| 0.2785 | 1.0988 | 93 | 0.3864 |
| 0.206 | 1.1347 | 96 | 0.3873 |
| 0.3354 | 1.1707 | 99 | 0.3912 |
| 0.3281 | 1.2066 | 102 | 0.3882 |
| 0.3452 | 1.2425 | 105 | 0.3849 |
| 0.3153 | 1.2784 | 108 | 0.3851 |
| 0.3846 | 1.3144 | 111 | 0.3851 |
| 0.2847 | 1.3503 | 114 | 0.3842 |
| 0.3128 | 1.3862 | 117 | 0.3842 |
| 0.282 | 1.4222 | 120 | 0.3866 |
| 0.2186 | 1.4581 | 123 | 0.3876 |
| 0.2122 | 1.4940 | 126 | 0.3862 |
| 0.2877 | 1.5299 | 129 | 0.3837 |
| 0.2771 | 1.5659 | 132 | 0.3822 |
| 0.3518 | 1.6018 | 135 | 0.3820 |
| 0.302 | 1.6377 | 138 | 0.3829 |
| 0.2653 | 1.6737 | 141 | 0.3833 |
| 0.3281 | 1.7096 | 144 | 0.3832 |
| 0.2933 | 1.7455 | 147 | 0.3821 |
| 0.1959 | 1.7814 | 150 | 0.3824 |
| 0.2013 | 1.8174 | 153 | 0.3830 |
| 0.1909 | 1.8533 | 156 | 0.3824 |
| 0.2321 | 1.8892 | 159 | 0.3812 |
| 0.2695 | 1.9251 | 162 | 0.3798 |
| 0.2516 | 1.9611 | 165 | 0.3796 |
| 0.2148 | 1.9970 | 168 | 0.3796 |
| 0.2233 | 2.0180 | 171 | 0.3802 |
| 0.234 | 2.0539 | 174 | 0.3844 |
| 0.2615 | 2.0898 | 177 | 0.3938 |
| 0.1582 | 2.1257 | 180 | 0.4031 |
| 0.218 | 2.1617 | 183 | 0.4071 |
| 0.2438 | 2.1976 | 186 | 0.4072 |
| 0.1822 | 2.2335 | 189 | 0.4050 |
| 0.2163 | 2.2695 | 192 | 0.4028 |
| 0.1513 | 2.3054 | 195 | 0.4021 |
| 0.1898 | 2.3413 | 198 | 0.4031 |
| 0.1857 | 2.3772 | 201 | 0.4059 |
| 0.1909 | 2.4132 | 204 | 0.4075 |
| 0.1119 | 2.4491 | 207 | 0.4092 |
| 0.1794 | 2.4850 | 210 | 0.4091 |
| 0.1188 | 2.5210 | 213 | 0.4081 |
| 0.1525 | 2.5569 | 216 | 0.4073 |
| 0.1897 | 2.5928 | 219 | 0.4069 |
| 0.1785 | 2.6287 | 222 | 0.4064 |
| 0.169 | 2.6647 | 225 | 0.4064 |
| 0.1518 | 2.7006 | 228 | 0.4060 |
| 0.1896 | 2.7365 | 231 | 0.4052 |
| 0.1675 | 2.7725 | 234 | 0.4055 |
| 0.2193 | 2.8084 | 237 | 0.4055 |
| 0.1887 | 2.8443 | 240 | 0.4057 |
| 0.1639 | 2.8802 | 243 | 0.4055 |
| 0.1701 | 2.9162 | 246 | 0.4058 |
| 0.2019 | 2.9521 | 249 | 0.4057 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1