Open Source Today (2024-08-09): Tongyi Qianwen Releases Qwen2-Math for Advanced Math Reasoning
Explore the latest AI open-source projects like Qwen2-Math for advanced math, Parler-TTS for fast TTS, and more innovative tools and frameworks.
Here are some interesting AI open-source models and frameworks I wanted to share today:
Project: Qwen2-Math
Qwen2-Math is a series of large language models in the Qwen2 family, designed specifically for math problems. It comes in three versions: 1.5B, 7B, and 72B parameters.
This model performs exceptionally well in solving arithmetic and mathematical problems, surpassing both open-source and closed-source models like GPT-4.
Qwen2-Math is intended to solve advanced math problems that require complex, multi-step logical reasoning.
https://huggingface.co/Qwen/Qwen2-Math-1.5B
https://huggingface.co/Qwen/Qwen2-Math-7B
https://huggingface.co/Qwen/Qwen2-Math-72B
Project: Parler-TTS
Parler-TTS has released Mini (880M) and Large (2.3B) model weights, trained on 45,000 hours of audiobook data. Compared to version 0.1, the generation speed is 4 times faster.
It also supports SDPA and Flash Attention 2 for even more speed.
Parler-TTS is a lightweight text-to-speech (TTS) model that generates high-quality, natural-sounding speech in the style of specific speakers (gender, tone, speaking style, etc.).
https://github.com/huggingface/parler-tts
Project: MoonPalace
MoonPalace is an API debugging tool from Moonshot AI. It works on Mac, Windows, and Linux. It’s easy to use—just replace the base_url with http://localhost:9988 after launching to start debugging.
This tool captures full requests, including detailed information during network errors. You can quickly retrieve and view request details using request_id and chatcmpl_id.
MoonPalace also allows one-click export of structured BadCase data to help improve the Kimi large model's capabilities.
https://github.com/MoonshotAI/moonpalace
Project: LiteMultiAgent
LiteMultiAgent is a library for LLM Agent applications.
The project aims to improve the efficiency of multi-agent systems by classifying and parallelizing agents.
It organizes agents into a hierarchy by categorizing different tool sets, allowing for more types of tasks. Sub-agents act as tools and execute tasks in parallel naturally.
https://github.com/PathOnAI/LiteMultiAgent
Project: RPBench-Auto
RPBench-Auto is an automated pipeline for evaluating the performance of large language models in role-playing tasks.
The project supports multiple model configurations and generates performance rankings through a series of predefined evaluation tasks.
Users can run evaluations and submit results to a public leaderboard with simple command-line operations.
https://github.com/boson-ai/RPBench-Auto
Project: AutoGGUF
AutoGGUF provides a graphical user interface for quantizing GGUF models using the llama.cpp library.
It allows users to download different versions of llama.cpp, manage multiple backends and perform quantization tasks with various options.
