Tencent-Hunyuan

Tencent-Hunyuan / Hy3-preview

Public

Hy3 preview (295B A21B), a leading reasoning and agent model in its size, with great cost efficiency

222
9
100% credibility
Found Apr 25, 2026 at 222 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Hy3-preview is a 295-billion-parameter open-source Mixture-of-Experts AI model from Tencent's Hy Team, strong in complex reasoning, context understanding, coding, and agent tasks, with weights available on Hugging Face and tools for deployment and fine-tuning.

How It Works

1
📰 Discover Hy3 preview

You hear about this super-smart AI brain from Tencent that excels at tough thinking, math, coding, and following instructions perfectly.

2
💾 Grab the AI model

Visit trusted sharing sites like Hugging Face to download the ready-to-use AI files for free.

3
🚀 Wake up your AI

Follow the easy guide to start your own powerful AI helper on a strong computer with multiple graphics cards—it comes alive in minutes.

4
💬 Start chatting

Ask it anything from simple questions to complex puzzles, math problems, or code help, and watch it reason step-by-step.

5
🎯 Customize if needed

Use the included guides to teach it your own data, making it even better for your special tasks.

🎉 Your smart sidekick is ready

Now you have a top-tier AI companion for reasoning, creating code, or handling real-world challenges right at your fingertips.

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Star Growth

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AI-Generated Review

What is Hy3-preview?

Hy3-preview delivers weights and Python scripts for a 295B Mixture-of-Experts model with 21B active parameters (A21B), optimized for advanced reasoning and agent tasks. Developers download from Hugging Face or ModelScope, deploy via vLLM or SGLang on 8 GPUs for an OpenAI-compatible API, and fine-tune with LoRA or full params using DeepSpeed. It tackles high-compute LLM inference by activating far fewer parameters than dense rivals, slashing costs while handling 256K context.

Why is it gaining traction?

This preview stands out as a leading model in its size class, topping benchmarks in STEM reasoning, coding agents like SWE-bench, and search agents, all with great cost efficiency from sparse activation. Python quickstarts let you spin up a server in minutes and tweak reasoning effort ("high" for math/coding), beating denser models on perf-per-flop. Low active params mean real-world agent apps run cheaper on standard H100/H20 clusters.

Who should use this?

AI engineers building reasoning-heavy agents or code assistants, like backend devs automating SWE tasks or researchers prototyping multi-step planners. Teams needing 256K context for long docs or tool-calling without proprietary APIs. Avoid if you're on consumer GPUs—needs enterprise-scale hardware.

Verdict

Grab it if you're scaling agent workflows; Tencent's benchmarks and deployment guides make it production-ready despite 222 stars and 1.0% credibility score. Still early—test thoroughly before prime time, but the efficiency edge justifies the dive.

(198 words)

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