license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen2-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- finance
- text-generation-inference
Golden-Touchstone Benchmark
Golden-Touchstone
Golden Touchstone is a simple, effective, and systematic benchmark for bilingual (Chinese-English) financial large language models, driving the research and implementation of financial large language models, akin to a touchstone. We also have trained and open-sourced Touchstone-GPT as a baseline for subsequent community research.
Introduction
The paper shows the evaluation of the diversity, systematicness and LLM adaptability of each open source benchmark.
By collecting and selecting representative task datasets, we built our own Chinese-English bilingual Touchstone Benchmark, which includes 22 datasets
We extensively evaluated GPT-4o, llama3, qwen2, fingpt and our own trained Touchstone-GPT, analyzed the advantages and disadvantages of these models, and provided direction for subsequent research on financial large language models
Evaluation of Touchstone Benchmark
Please See our github repo Golden-Touchstone
Usage of Touchstone-GPT
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"IDEA-FinAI/TouchstoneGPT-7B-Instruct",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("IDEA-FinAI/TouchstoneGPT-7B-Instruct")
prompt = "What is the sentiment of the following financial post: Positive, Negative, or Neutral?\nsees #Apple at $150/share in a year (+36% from today) on growing services business."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
Citation
@article{gan2023ziya2,
title={Ziya2: Data-centric learning is all llms need},
author={Gan, Ruyi and Wu, Ziwei and Sun, Renliang and Lu, Junyu and Wu, Xiaojun and Zhang, Dixiang and Pan, Kunhao and He, Junqing and Tian, Yuanhe and Yang, Ping and others},
journal={arXiv preprint arXiv:2311.03301},
year={2023}
}