---
base_model: meta-llama/Llama-2-70b-hf
tags:
- generated_from_trainer
model-index:
- name: qlora-llama70-ft-full-dataset
  results: []
library_name: peft
---

<!-- 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. -->

# qlora-llama70-ft-full-dataset

This model is a fine-tuned version of [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4237

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0025
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5952        | 0.16  | 5    | 1.5179          |
| 1.6321        | 0.32  | 10   | 1.5725          |
| 1.511         | 0.48  | 15   | 1.4764          |
| 1.4807        | 0.65  | 20   | 1.4482          |
| 1.439         | 0.81  | 25   | 1.4329          |
| 1.4266        | 0.97  | 30   | 1.4245          |


### Framework versions

- PEFT 0.5.0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.1
- Tokenizers 0.15.0