README.md

---
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
- BitsAndBytes
- PEFT
- QLoRA
datasets:
- databricks/databricks-dolly-15k
base_model: NousResearch/Llama-2-7b-chat-hf
model-index:
- name: llama2-7-dolly-query 
  results: []
license: mit
language:
- en
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# llama2-7-dolly-query

This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the generator dataset.
Can be used in conjunction with [LukeOLuck/llama2-7-dolly-answer](https://huggingface.co/LukeOLuck/llama2-7-dolly-answer)

## Model description

A Fine-Tuned PEFT Adapter for the llama2 7b chat hf model
Leverages [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://arxiv.org/abs/2205.14135), [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314), and [PEFT](https://huggingface.co/blog/peft)

## Intended uses & limitations

Generate a query based on context and input

## Training and evaluation data

Used SFTTrainer, [checkout the code](https://colab.research.google.com/drive/1sr0mUF8dwYKo6NNR3tkjk0Z-p5FFr1_6?usp=sharing)

## Training procedure

[Checkout the code here](https://colab.research.google.com/drive/1sr0mUF8dwYKo6NNR3tkjk0Z-p5FFr1_6?usp=sharing)

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3

### Training results

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65388a56a5ab055cf2d73676/FJ5p_wutu8o1z789Hd93g.png)

### Framework versions

- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2