---
license: other
base_model: Undi95/Meta-Llama-3-8B-hf
tags:
- generated_from_trainer
model-index:
- name: lora-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: Undi95/Meta-Llama-3-8B-hf
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: Pbug/bftest
    type: sharegpt
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 10
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
evals_per_epoch: 2
eval_table_size:
eval_sample_packing: False
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>
```

</details><br>

# lora-out

This model is a fine-tuned version of [Undi95/Meta-Llama-3-8B-hf](https://huggingface.co/Undi95/Meta-Llama-3-8B-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5419        | 0.03  | 1    | nan             |
| 2.5492        | 0.51  | 18   | nan             |
| 2.434         | 1.01  | 36   | nan             |
| 2.3504        | 1.5   | 54   | nan             |
| 2.3643        | 2.0   | 72   | nan             |
| 2.2834        | 2.48  | 90   | nan             |
| 2.2383        | 2.98  | 108  | nan             |
| 1.8786        | 3.47  | 126  | nan             |
| 1.7963        | 3.98  | 144  | nan             |
| 2.1853        | 4.47  | 162  | nan             |
| 1.4333        | 4.98  | 180  | nan             |
| 1.2058        | 5.46  | 198  | nan             |
| 1.125         | 5.96  | 216  | nan             |
| 0.809         | 6.44  | 234  | nan             |
| 0.7118        | 6.94  | 252  | nan             |
| 0.7175        | 7.44  | 270  | nan             |
| 0.7341        | 7.94  | 288  | nan             |
| 0.774         | 8.44  | 306  | nan             |
| 0.6379        | 8.94  | 324  | nan             |
| 0.562         | 9.41  | 342  | nan             |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0