metadata

license: cc-by-nc-4.0

QLoRA Instruction Tuned Models

| Paper | Code |

The LLaMA-2 QLoRA OpenOrca are open-source models obtained through 4-bit QLoRA tuning of LLaMA-2 base models 240k exmaples of OpenOrca.

⚠️ These models are purely intended for research purposes and could produce problematic outputs.

What are QLoRA Instruction Tuned Models and why use them?

Strong performance on MMLU following the QLoRA instruction tuning.
Replicable and efficient instruction tuning procedure that can be extended to new use cases. QLoRA training scripts are available in the QLoRA repo.
Rigorous comparison to 16-bit methods (both 16-bit full-finetuning and LoRA) in our paper demonstrates the effectiveness of 4-bit QLoRA finetuning.
Lightweight checkpoints which only contain adapter weights.

License and Intended Use

Note the use of these adapter weights, requires access to the LLaMA-2 model weighs and therefore should be used according to the LLaMA-2 license.

Usage

Here is an example of how you would load the model 4-bits:

import torch
from peft import PeftModel    
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_name = "meta-llama/Llama-2-70b-hf"
adapters_name = 'uwnlp/llama-2-70b-qlora-openorca'

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    load_in_4bit=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type='nf4'
    ),
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Inference can then be performed as usual with HF models as follows:

prompt = "Introduce yourself"
formatted_prompt = (
    f"A chat between a curious human and an artificial intelligence assistant."
    f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
    f"### Human: {prompt} ### Assistant:"
)
inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0")
outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Expected output similar to the following:

A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have.

Model Card

Architecture: The models released here are LoRA adapters to be used on top of LLaMA-2 models. They are added to all layers. For all model sizes, we use $r=64$.

Base Model: These models use LLaMA-2 as base model. LLaMA is a causal language model pretrained on a large corpus of text. See LLaMA-2 paper for more details. Note that these models can inherit biases and limitations of the base model.

Finetuning Data: These models are finetuned on 240k examples of the OpenOrca dataset.

Languages: The different datasets cover different languages. We direct to the various papers and resources describing the datasets for more details.

Next, we describe Training and Evaluation details.

Training

QLoRA Instruction Tuned Models are the result of 4-bit QLoRA supervised finetuning on different instruction tuning datasets.

All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B/70B models. For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer.

Training hyperparameters

Parameters	Dataset	Batch size	LR	Steps	Source Length	Target Length
7B	All	16	2e-4	10000	384	128
13B	All	16	2e-4	10000	384	128
70B	All	64	1e-4	2500	384	128

Evaluation

We use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy.

Dataset	7B	13B	33B	65B
LLaMA-1 no tuning	35.1	46.9	57.8	63.4
Self-Instruct	36.4	33.3	53.0	56.7
Longform	32.1	43.2	56.6	59.7
Chip2	34.5	41.6	53.6	59.8
HH-RLHF	34.9	44.6	55.8	60.1
Unnatural Instruct	41.9	48.1	57.3	61.3
OASST1 (Guanaco)	36.6	46.4	57.0	62.2
Alpaca	38.8	47.8	57.3	62.5
FLAN v2	44.5	51.4	59.2	63.9

Dataset	7B	13B	34B	70B
LLaMA-2 no tuning	45.3	54.8	62.6	68.9
OpenOrca	45.0			69.0

Citation

@article{dettmers2023qlora,
  title={QLoRA: Efficient Finetuning of Quantized LLMs},
  author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
  journal={arXiv preprint arXiv:2305.14314},
  year={2023}
}