---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
library_name: peft
license: llama3.1
tags:
- generated_from_trainer
model-index:
- name: finetune/output/medical-2day
  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: chatml
datasets:
  - path: Howard881010/medical-2day
    type: alpaca
    train_on_split: train
dataset_prepared_path:
output_dir: ./finetune/output/medical-2day

test_datasets:
  - path: Howard881010/medical-2day
    split: valid
    type: alpaca

adapter: lora
lora_model_dir:

sequence_len: 2100
sample_packing: false
pad_to_sequence_len: true

lora_r: 8
lora_alpha: 32
lora_dropout: 0.1
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: finetune
wandb_entity:
wandb_watch:
wandb_name: medical-2day
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_hf
learning_rate: 0.00002
max_grad_norm: 1.0

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

gradient_checkpointing: true
logging_steps: 1
xformers_attention: 
flash_attention: true
eval_sample_packing: False

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
seed: 0
special_tokens:
  pad_token: "<|end_of_text|>"

```

</details><br>

# finetune/output/medical-2day

This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8085

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- 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: 10
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4523        | 0.0003 | 1    | 1.2391          |
| 0.9863        | 0.2503 | 741  | 0.8352          |
| 0.9459        | 0.5005 | 1482 | 0.8176          |
| 0.908         | 0.7508 | 2223 | 0.8085          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1