To load models in 4bits with transformers and bitsandbytes, you have to install accelerate and transformers from source and make sure you have the latest version of the bitsandbytes library (0.39.0).

pip3 install -r requirements.txt

Other dependencies

If you want to finetune the model on a new instance. You could run the setup.sh to install the python and cuda package.

bash scripts/setup.sh

Finetuning

bash script/finetune.sh

Usage

Quantization parameters are controlled from the BitsandbytesConfig

Loading in 4 bits is activated through load_in_4bit
The datatype used for the linear layer computations with bnb_4bit_compute_dtype
Nested quantization is activated through bnb_4bit_use_double_quant
The datatype used for qunatization is specified with bnb_4bit_quant_type. Note that there are two supported quantization datatypes fp4 (four bit float) and nf4 (normal four bit float). The latter is theoretically optimal for normally distributed weights and we recommend using nf4.

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

pretrained_model_name_or_path = "bavest/fin-llama-33b-merge"
model = AutoModelForCausalLM.from_pretrained(
    pretrained_model_name_or_path=pretrained_model_name_or_path,
    load_in_4bit=True,
    device_map='auto',
    torch_dtype=torch.bfloat16,
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type='nf4'
    ),
)

tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)

question = "What is the market cap of apple?"
input = "" # context if needed

prompt = f"""
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's question.
'### Instruction:\n{question}\n\n### Input:{input}\n""\n\n### Response: 
"""

input_ids = tokenizer.encode(prompt, return_tensors="pt").to('cuda:0')

with torch.no_grad():
    generated_ids = model.generate(
        input_ids,
        do_sample=True,
        top_p=0.9,
        temperature=0.8,
        max_length=128
    )

generated_text = tokenizer.decode(
    [el.item() for el in generated_ids[0]], skip_special_tokens=True
)

Dataset for FIN-LLAMA

The dataset is released under bigscience-openrail-m. You can find the dataset used to train FIN-LLAMA models on HF at bavest/fin-llama-dataset.

Known Issues and Limitations

Here a list of known issues and bugs. If your issue is not reported here, please open a new issue and describe the problem. See QLORA for any other limitations.

4-bit inference is slow. Currently, our 4-bit inference implementation is not yet integrated with the 4-bit matrix multiplication
Currently, using bnb_4bit_compute_type='fp16' can lead to instabilities.
Make sure that tokenizer.bos_token_id = 1 to avoid generation issues.

Acknowledgements

We also thank Meta for releasing the LLaMA models without which this work would not have been possible.

This repo builds on the Stanford Alpaca , QLORA, Chinese-Guanaco and LMSYS FastChat repos.

License and Intended Use

We release the resources associated with QLoRA finetuning in this repository under GLP3 license. In addition, we release the FIN-LLAMA model family for base LLaMA model sizes of 7B, 13B, 33B, and 65B. These models are intended for purposes in line with the LLaMA license and require access to the LLaMA models.

Prompts

Act as an Accountant

I want you to act as an accountant and come up with creative ways to manage finances. You'll need to consider budgeting, investment strategies and risk management when creating a financial plan for your client. In some cases, you may also need to provide advice on taxation laws and regulations in order to help them maximize their profits. My first suggestion request is “Create a financial plan for a small business that focuses on cost savings and long-term investments".

Paged Optimizer

You can access the paged optimizer with the argument --optim paged_adamw_32bit

Cite

tex

@misc{Fin-LLAMA,
  author = {William Todt, Ramtin Babaei, Pedram Babaei},
  title = {Fin-LLAMA: Efficient Finetuning of Quantized LLMs for Finance},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Bavest/fin-llama}},
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	51.76
ARC (25-shot)	65.02
HellaSwag (10-shot)	86.2
MMLU (5-shot)	58.73
TruthfulQA (0-shot)	49.75
Winogrande (5-shot)	80.03
GSM8K (5-shot)	16.22
DROP (3-shot)	6.36

bavest/fin-llama-33b-merged

FIN-LLAMA

Installation