metadata

library_name: peft
tags:
  - PyTorch
  - Transformers
  - trl
  - sft
  - BitsAndBytes
  - PEFT
  - QLoRA
datasets:
  - databricks/databricks-dolly-15k
base_model: meta-llama/Llama-2-7b-chat
model-index:
  - name: llama2-7-dolly-answer
    results: []
license: mit
language:
  - en

llama2-7-dolly-answer

This model is a fine-tuned version of Llama-2-7b-chat-hf on the dolly dataset. Can be used in conjunction with LukeOLuck/llama2-7-dolly-query

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, QLoRA: Efficient Finetuning of Quantized LLMs, and PEFT

Intended uses & limitations

Generate a safe answer based on context and a request

Training and evaluation data

Used SFTTrainer, checkout the code

Training procedure

Checkout the code here

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

Framework versions

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