---
license: llama3
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model-index:
- name: test-file-system/axolotl/test-file-system/axolotl/lora-llama3-8b-chat
  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: meta-llama/Meta-Llama-3-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast

load_in_8bit: false
load_in_4bit: true 
strict: false

datasets:
  - path: /test-file-system/axolotl/test-file-system/axolotl/ft_data_sharegpt.jsonl 
    type: sharegpt
    conversation: chatml 
    #field_human: user
    #field_model: assistant
    #roles:
    #   input:
    #    - user
    #    - system
    #  output:
    #    - assistant
dataset_prepared_path:
val_set_size: 0.05
output_dir: /test-file-system/axolotl/test-file-system/axolotl/lora-llama3-8b-chat

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: false 
pad_to_sequence_len: 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:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

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

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

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
   pad_token: <|end_of_text|>

```

</details><br>

# test-file-system/axolotl/test-file-system/axolotl/lora-llama3-8b-chat

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002

## 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: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8618        | 0.0280 | 1    | 1.8569          |
| 0.0185        | 0.2517 | 9    | 0.0596          |
| 0.0056        | 0.5035 | 18   | 0.0202          |
| 0.0008        | 0.7552 | 27   | 0.0005          |
| 0.0006        | 1.0070 | 36   | 0.0002          |
| 0.0001        | 1.2587 | 45   | 0.0000          |
| 0.0004        | 1.5105 | 54   | 0.0004          |
| 0.0007        | 1.7622 | 63   | 0.0002          |
| 0.0001        | 2.0140 | 72   | 0.0001          |
| 0.0001        | 2.2657 | 81   | 0.0002          |
| 0.0006        | 2.5175 | 90   | 0.0004          |
| 0.0006        | 2.7692 | 99   | 0.0004          |
| 0.0005        | 3.0210 | 108  | 0.0003          |
| 0.0003        | 3.2727 | 117  | 0.0002          |
| 0.0004        | 3.5245 | 126  | 0.0002          |
| 0.0006        | 3.7762 | 135  | 0.0002          |


### Framework versions

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