---
license: other
base_model: meta-llama/Meta-Llama-3-8B
tags:
- generated_from_trainer
model-index:
- name: out
  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: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: taozi555/bagel
    type: sharegpt
#  - path: jondurbin/cinematika-v0.1
#    type: text
  - path: MinervaAI/Aesir-Preview
    type: sharegpt
  - path: Norquinal/claude_multiround_chat_30k
    type: sharegpt
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./out
chat_template: alpaca

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project: waifu-8b
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
eval_steps: 100
eval_table_size:
saves_per_epoch: 
save_steps: 100
save_total_limit: 20
debug:
deepspeed: /workspace/deepspeed.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

```

</details><br>

# out

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7773

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0419        | 0.0   | 1    | 1.1113          |
| 0.9179        | 0.07  | 100  | 0.8886          |
| 1.0123        | 0.14  | 200  | 0.8822          |
| 0.9106        | 0.21  | 300  | 0.8701          |
| 0.8992        | 0.28  | 400  | 0.8637          |
| 0.7915        | 0.35  | 500  | 0.8527          |
| 0.9123        | 0.42  | 600  | 0.8448          |
| 0.7849        | 0.49  | 700  | 0.8381          |
| 0.8381        | 0.56  | 800  | 0.8344          |
| 0.7652        | 0.63  | 900  | 0.8230          |
| 0.9006        | 0.7   | 1000 | 0.8167          |
| 0.8589        | 0.77  | 1100 | 0.8088          |
| 0.7635        | 0.84  | 1200 | 0.8016          |
| 0.7696        | 0.91  | 1300 | 0.7951          |
| 0.8476        | 0.98  | 1400 | 0.7879          |
| 0.6031        | 1.03  | 1500 | 0.8063          |
| 0.5386        | 1.09  | 1600 | 0.8065          |
| 0.5298        | 1.16  | 1700 | 0.8015          |
| 0.5736        | 1.23  | 1800 | 0.7979          |
| 0.5761        | 1.3   | 1900 | 0.7939          |
| 0.5576        | 1.37  | 2000 | 0.7917          |
| 0.4814        | 1.44  | 2100 | 0.7879          |
| 0.5146        | 1.51  | 2200 | 0.7842          |
| 0.4577        | 1.58  | 2300 | 0.7832          |
| 0.4821        | 1.65  | 2400 | 0.7806          |
| 0.6088        | 1.72  | 2500 | 0.7782          |
| 0.5113        | 1.79  | 2600 | 0.7785          |
| 0.5861        | 1.86  | 2700 | 0.7779          |
| 0.4885        | 1.93  | 2800 | 0.7773          |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0