---
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-2024-06-29-17-22-10
  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
  - path: /workspace/axolotl/vinh/INSTRUCT/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-2024-06-29-17-22-10

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-2024-06-29-17-22-10

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: 0.1026

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.6579        | 0.0063 | 1    | 0.6361          |
| 0.1746        | 0.1011 | 16   | 0.1862          |
| 0.1495        | 0.2023 | 32   | 0.1577          |
| 0.1288        | 0.3034 | 48   | 0.1459          |
| 0.1508        | 0.4045 | 64   | 0.1368          |
| 0.1309        | 0.5056 | 80   | 0.1310          |
| 0.1179        | 0.6068 | 96   | 0.1283          |
| 0.1035        | 0.7079 | 112  | 0.1236          |
| 0.1117        | 0.8090 | 128  | 0.1208          |
| 0.1126        | 0.9101 | 144  | 0.1188          |
| 0.0739        | 1.0113 | 160  | 0.1146          |
| 0.0741        | 1.1124 | 176  | 0.1134          |
| 0.0746        | 1.2135 | 192  | 0.1137          |
| 0.0821        | 1.3146 | 208  | 0.1125          |
| 0.0768        | 1.4158 | 224  | 0.1091          |
| 0.0627        | 1.5169 | 240  | 0.1069          |
| 0.0746        | 1.6180 | 256  | 0.1056          |
| 0.0767        | 1.7191 | 272  | 0.1031          |
| 0.0775        | 1.8203 | 288  | 0.0996          |
| 0.0596        | 1.9214 | 304  | 0.0987          |
| 0.0463        | 2.0225 | 320  | 0.0976          |
| 0.036         | 2.1236 | 336  | 0.1062          |
| 0.0401        | 2.2248 | 352  | 0.1029          |
| 0.0462        | 2.3259 | 368  | 0.1039          |
| 0.0476        | 2.4270 | 384  | 0.1034          |
| 0.0372        | 2.5281 | 400  | 0.1026          |
| 0.0377        | 2.6293 | 416  | 0.1026          |
| 0.0358        | 2.7304 | 432  | 0.1026          |
| 0.0392        | 2.8315 | 448  | 0.1027          |
| 0.0384        | 2.9326 | 464  | 0.1026          |


### Framework versions

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