chinese-llm-benchmark logo
chinese-llm-benchmark
★ 6.5k
vs
on-policy logo
on-policy
★ 2.1k

chinese-llm-benchmark vs on-policy

chinese-llm-benchmark: ReLE Benchmark (formerly CLiB) provides a continuously updated evaluation for Chinese AI large language models, covering over 337 commercial and open-source LLMs. It offers multi-dimensional capability assessments across various domains, along with comprehensive rankings and a large defect library for model improvement.; on-policy: This repository implements MAPPO, a multi-agent variant of PPO, widely used in cooperative multi-agent games and research. It provides robust implementations for various multi-agent environments like StarCraft II, Hanabi, and Google Research Football, along with detailed training scripts and hyperparameter guidance.

01

TL;DR

chinese-llm-benchmark logo
chinese-llm-benchmarkLLM Infra · ★ 6.5k

Comparing and selecting the best performing LLMs for specific applications.

on-policy logo
on-policyLLM Infra · ★ 2.1k

Research and experimentation in cooperative multi-agent reinforcement learning

02

Side-by-Side Comparison

Field
chinese-llm-benchmark logochinese-llm-benchmark
on-policy logoon-policy
Category
LLM Infra
LLM Infra
Stars
★ 6.5k
★ 2.1k
License
—
MIT
Install Command
git clone https://github.com/jeinlee1991/chinese-llm-benchmark.git
pip install -e .
Updated
1d ago
2y ago
Open Source
Yes
Yes
Tags
LLM Evaluation, Chinese LLMs, AI Benchmark
Multi-Agent Reinforcement Learning, PPO, MAPPO
03

Features

chinese-llm-benchmark logochinese-llm-benchmark
01Extensive coverage of 337+ commercial and open-source Chinese LLMs.
02Multi-dimensional evaluation across 7 main domains and ~300 sub-dimensions.
03Provides detailed ranking lists for various capabilities and specific domains.
04Offers a large defect library with over 2 million LLM flaws for research and improvement.
05Supports customized model selection and free evaluation services for private models.
on-policy logoon-policy
01Implementation of MAPPO (Multi-Agent PPO)
02Support for diverse multi-agent environments (e.g., StarCraft II, Hanabi)
03Ready-to-use training scripts for various scenarios
04Detailed hyperparameter guidance and updated results
05Default support for shared policy among agents
04

Use Cases

chinese-llm-benchmark logochinese-llm-benchmark
↳Comparing and selecting the best performing LLMs for specific applications.
↳Identifying weaknesses and improving the capabilities of large language models.
↳Benchmarking private or custom LLMs against public models for performance and cost optimization.
on-policy logoon-policy
↳Research and experimentation in cooperative multi-agent reinforcement learning
↳Benchmarking and evaluating PPO's effectiveness in MARL scenarios
↳Training AI agents for popular multi-agent games like StarCraft II and Hanabi
05

Best For

chinese-llm-benchmark logochinese-llm-benchmark
EssentialTrending
on-policy logoon-policy
—
FAQ

FAQ

What is the difference between chinese-llm-benchmark and on-policy?
Both chinese-llm-benchmark and on-policy are in the LLM Infra category. chinese-llm-benchmark has 6.5k stars, while on-policy has 2.1k stars.
Which is better, chinese-llm-benchmark or on-policy?
The best choice depends on your use case. Choose chinese-llm-benchmark if Comparing and selecting the best performing LLMs for specific applications., and on-policy if Research and experimentation in cooperative multi-agent reinforcement learning.
Is chinese-llm-benchmark free or open source?
Yes, chinese-llm-benchmark is open source on GitHub.
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
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