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chancefocus avatar

chancefocus/PIXIU

0
View on GitHub↗
868 stars·117 forks·Jupyter Notebook·MIT·15 views

PIXIU

This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).

Features

  • Financial Domain Models - Financial model with specialized instruction-tuning datasets.
  • LLM Financial Tools - Resource for financial LLMs with instruction datasets and evaluation benchmarks.
  • Evaluation Benchmarks - Benchmark for financial understanding and prediction tasks.

Star history

Star history chart for chancefocus/pixiuStar history chart for chancefocus/pixiu

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does chancefocus/pixiu do?

This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).

What are the main features of chancefocus/pixiu?

The main features of chancefocus/pixiu are: Financial Domain Models, LLM Financial Tools, Evaluation Benchmarks.

Which projects share features with chancefocus/pixiu?

Projects with overlapping indexed features include: ssymmetry/bbt-fincuge-applications — 论文链接:https://arxiv.org/abs/2302.09432. salt-nlp/flang — When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain. ai4finance-foundation/fingpt — FinGPT is a suite of specialized financial tools and a framework for adapting large language models to the financial… cbluebenchmark/cblue — [CBLUE1] 中文医疗信息处理基准CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark. dai-shen/laiw — LAiW: A Chinese Legal Large Language Models Benchmark. codefuse-ai/codefuse-devops-eval — Industrial-first evaluation benchmark for LLMs in the DevOps/AIOps domain.

Projects sharing features with PIXIU

These projects share indexed features with PIXIU. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • salt-nlp/flangSALT-NLP avatar

    SALT-NLP/FLANG

    57View on GitHub↗

    When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain

    Python
    View on GitHub↗57
  • ssymmetry/bbt-fincuge-applicationsssymmetry avatar

    ssymmetry/BBT-FinCUGE-Applications

    284View on GitHub↗

    论文链接:https://arxiv.org/abs/2302.09432

    Python
    View on GitHub↗284
  • ai4finance-foundation/fingptAI4Finance-Foundation avatar

    AI4Finance-Foundation/FinGPT

    20,507View on GitHub↗

    FinGPT is a suite of specialized financial tools and a framework for adapting large language models to the financial domain. It provides a set of pipelines for financial entity extraction, sentiment analysis, and retrieval-augmented generation to improve the accuracy of financial information systems. The project distinguishes itself through efficient training workflows, utilizing low-rank adaptation and quantized low-rank adaptation to fine-tune models on consumer-grade hardware. It employs market-labeled datasets and reinforcement learning that uses actual stock price movements as reward sig

    Jupyter Notebookchatgptfinancefingpt
    View on GitHub↗20,507
  • cbluebenchmark/cblueCBLUEbenchmark avatar

    CBLUEbenchmark/CBLUE

    843View on GitHub↗

    CBLUE1 中文医疗信息处理基准CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

    Python
    View on GitHub↗843
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