How to use from
SGLang
Install from pip and serve model
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TongjiFinLab/CFGPT1-pt-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TongjiFinLab/CFGPT1-pt-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker images
docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TongjiFinLab/CFGPT1-pt-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TongjiFinLab/CFGPT1-pt-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Quick Links

📈 CFGPT: Chinese Financial Assistant with Large Language Model (CFGPT1-pt-7b)

Introduction

We introduce CFGPT, an open-source language model trained by firstly further pretraining general LLMs on collected and cleaned Chinese finance text data (CFData-pt), including financial domain-specific data (announcement, finance articles, finance exams, finance news, finance research papers) and general data (Wikipedia), and secondly fine-tuning with knowledge-intensive instruction tuning data (CFData-sft). As for preliminary evaluation, we use CFBenchmark-Basic. CFGPT outperforms the baselines on objective and subjective tasks compared to several baseline models with similar parameters.

In this repository, we will share the further pretrained model.

  • Pretrained Model: Full model weights after further pretraining with the chinese finance text corpus to comply with the InternLM model license.

How to Use

The CFGPT1-pt-7b is a pre-trained model, which has not undergone supervised fine-tuning with a instruction data. Therefore, it is not advisable to use this model for financial tasks. Please refer to CFGPT Github repo for further usage.

简介

CFGPT是一个开源的语言模型,首先通过在收集和清理的中国金融文本数据(CFData-pt)上进行继续预训练,包括金融领域特定数据(公告、金融文章、金融考试、金融新闻、金融研究论文)和通用数据(维基百科),然后使用知识密集的指导调整数据(CFData-sft)进行微调。 我们使用CFBenchmark-Basic进行初步评估。与几个具有相似参数的基线模型相比,CFGPT在识别,分类和生成任务上表现优越。

在这个仓库中,我们将分享以下继续预训练的模型。

  • Pretrained Model: 在中国金融文本语料库上进行进一步预训练且符合InternLM模型许可的完整模型权重。

如何使用

这个模型是一个预训练的模型,还没有经历过指令数据库的有监督微调,因此不建议使用该模型执行相关金融任务。 具体使用,请参考CFGPT的Github仓库。

Downloads last month
19
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support