Efficient Finetuning of Quantized LLMs for Finance
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
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
bash script/finetune.sh
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 datatypesfp4
(four bit float) andnf4
(normal four bit float). The latter is theoretically optimal for normally distributed weights and we recommend usingnf4
.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
pretrained_model_name_or_path = "bavest/fin-llama-33b-merged"
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
)
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.
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.
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.
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.
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".
You can access the paged optimizer with the argument --optim paged_adamw_32bit
@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}},
}