---
base_model: NousResearch/Meta-Llama-3-8B-Instruct
library_name: peft
license: other
tags:
- generated_from_trainer
model-index:
- name: workspace/axolotl/vinh/NousResearch_Meta-Llama-3-8B-Instruct-lora-2024-07-01-14-28-39
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: NousResearch/Meta-Llama-3-8B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false
 
datasets:
  - path: /workspace/axolotl/vinh/PAL/input_output_llama3.json
    type: input_output
dataset_prepared_path:
val_set_size: 0.05
eval_sample_packing: false
output_dir: /workspace/axolotl/vinh/NousResearch_Meta-Llama-3-8B-Instruct-lora-2024-07-01-14-28-39

sequence_len: 2048
sample_packing: false
pad_to_sequence_len: false

adapter: lora
lora_model_dir: 
lora_r: 64
lora_alpha: 128
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 128
micro_batch_size: 1
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-4

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention: 
flash_attention: true
s2_attention:

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10
evals_per_epoch: 10
eval_table_size:
eval_max_new_tokens: 512
saves_per_epoch: 2
save_total_limit: 20
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
   pad_token: <|end_of_text|>

```

</details><br>

# workspace/axolotl/vinh/NousResearch_Meta-Llama-3-8B-Instruct-lora-2024-07-01-14-28-39

This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0392

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5339        | 0.0095 | 1    | 0.5036          |
| 0.0879        | 0.1043 | 11   | 0.0813          |
| 0.0582        | 0.2086 | 22   | 0.0629          |
| 0.06          | 0.3129 | 33   | 0.0566          |
| 0.0593        | 0.4172 | 44   | 0.0514          |
| 0.054         | 0.5214 | 55   | 0.0483          |
| 0.0459        | 0.6257 | 66   | 0.0469          |
| 0.0397        | 0.7300 | 77   | 0.0460          |
| 0.0453        | 0.8343 | 88   | 0.0449          |
| 0.04          | 0.9386 | 99   | 0.0429          |
| 0.0338        | 1.0429 | 110  | 0.0418          |
| 0.0322        | 1.1472 | 121  | 0.0422          |
| 0.0275        | 1.2515 | 132  | 0.0416          |
| 0.0322        | 1.3558 | 143  | 0.0416          |
| 0.0266        | 1.4600 | 154  | 0.0404          |
| 0.0249        | 1.5643 | 165  | 0.0397          |
| 0.0292        | 1.6686 | 176  | 0.0393          |
| 0.031         | 1.7729 | 187  | 0.0385          |
| 0.0265        | 1.8772 | 198  | 0.0375          |
| 0.0273        | 1.9815 | 209  | 0.0375          |
| 0.0175        | 2.0858 | 220  | 0.0377          |
| 0.0168        | 2.1901 | 231  | 0.0396          |
| 0.0182        | 2.2943 | 242  | 0.0403          |
| 0.0201        | 2.3986 | 253  | 0.0397          |
| 0.0138        | 2.5029 | 264  | 0.0393          |
| 0.0173        | 2.6072 | 275  | 0.0392          |
| 0.0186        | 2.7115 | 286  | 0.0392          |
| 0.0209        | 2.8158 | 297  | 0.0392          |
| 0.0185        | 2.9201 | 308  | 0.0392          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1