What is Hy4 preview Lite Version
Hy4 preview Lite Version is a version in which Tencent Hunyuan compressed the originally 1.5TB Hy4 preview open-source large model to about 214GB through its self-developed Sherry 1.25bit sparse quantization and MIX-STQ1_0 layer-by-layer mixed-precision technology. While greatly lowering the hardware threshold, the model maintains core capabilities such as long-text understanding and code generation, and supports joint inference across networks on multiple heterogeneous devices, allowing ordinary developers to run a flagship-level large model locally.
Main Features of Hy4 preview Lite Version
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Code Generation: The model performs excellently in multilingual code evaluations such as SWE-Bench, and can provide developers with high-quality programming assistance and the ability to solve complex engineering problems.
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Office Document Processing: Supports long-document understanding, content summarization, and general question answering, and is capable of handling text processing and information extraction tasks in daily office scenarios.
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Scientific Reasoning: The model has mathematical calculation and scientific task-solving capabilities, and still performs steadily in scenarios requiring logical deduction and numerical computation.
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Long-Context Retrieval: In multi-turn conversations, it can accurately locate and extract key information from long texts, with long-text understanding ability almost on par with the original model.
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Tool Calling: Through protocols such as MCP, the model can connect to external tools and APIs to enable automated task execution and complex workflow orchestration.
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Game Generation: Based on instructions, the model can generate runnable games including interfaces and interaction logic, such as the NEON DRIFT game generation case shown in the article.
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Heterogeneous Joint Inference: With cross-device scheduling from prima.cpp, multiple machines with different configurations can run the model collaboratively across a local area network, without the need to purchase a homogeneous high-end hardware cluster.
Technical Principles of Hy4 preview Lite Version
- Sherry Sparse Ternary Quantization: A extreme compression algorithm self-developed by Tencent Hunyuan that maps weights in groups of 4 to {-1, 0, +1} and forces each group to retain one zero. Each group requires only 5-bit encoding, achieving an average 1.25 bit/weight storage efficiency, and is naturally suited for SIMD acceleration, completing the full pipeline from quantization to inference in llama.cpp.
- MIX-STQ1_0 Mixed-Precision Strategy: Abandons a uniform compression scheme and, after evaluating sensitivity layer by layer on a calibration set, processes them in tiers - sensitive layers retain higher precision (IQ2_XXS), while insensitive layers adopt aggressive STQ1_0. In the end, the routed expert weights average only 1.78 bpw, and the overall error is instead lower than the uniform quantization baseline, while also saving more than 5GB of storage.
- Heterogeneous Distributed Inference: Based on the cross-device scheduling capability of prima.cpp, MoE layers are split across different hardware (such as a laptop 4090 and a server A4000) for execution. By overlapping computation and weight reading, a 214GB model achieves usable inference speed in a heterogeneous environment with a total of 144GB of VRAM + memory, without the need to purchase a homogeneous high-end cluster.
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How to Use Hy4 preview Lite Version
- Model Acquisition: Download the AngelSlim/Hy4-preview-GGUF quantized model files locally from HuggingFace.
- Local inference: Use llama.cpp with STQ1_0 inference kernel support to load and run the 214GB lightweight version model.
- Heterogeneous deployment: Through prima.cpp, multiple heterogeneous devices (such as laptops and servers) are stitched together across a network to achieve distributed joint inference.
Core advantages of the Hy4 preview lightweight version
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Extreme compression lowers the barrier: Through self-developed quantization technology, the model is compressed from 1.5TB to 214GB, allowing machines with limited configurations to run flagship-level large models.
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Sherry quantization is small and fast: The self-developed sparse ternary quantization algorithm achieves an average of 1.25 bit/weight and naturally adapts to SIMD, with inference speed superior to similar low-bit solutions.
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Layer-by-layer mixed precision preserves quality: MIX-STQ1_0 compresses in tiers layer by layer according to sensitivity, reducing overall error without increasing the bit budget, and its evaluation performance leads unified quantization baselines.
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Core capabilities are almost lossless: Long-text understanding and multi-turn retrieval are on par with the original version, while tasks such as code, math, and tool calling only decline slightly, fully covering daily productivity scenarios.
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Joint inference across heterogeneous devices: Supports running across multiple machines with different configurations within a LAN, enabling low-cost deployment of ultra-large models without purchasing homogeneous high-end clusters.
Project address of the Hy4 preview lightweight version
- HuggingFace model repository：https://huggingface.co/AngelSlim/Hy4-preview-GGUF
Comparison of the Hy4 preview lightweight version with similar competitors
| Comparison dimension | Hy4 preview lightweight version | DeepSeek-V4 |
|---|---|---|
| Publisher | Tencent Hunyuan | DeepSeek |
| Model architecture | MoE, total parameter count nearly 1.5T | MoE, total parameter count 1.6T (about 32B activated) |
| Lightweight version weights | 214 GB(MIX-STQ1_0 quantization) | Official FP8 about 300GB+; community INT4 about 200GB level |
| Core compression technology | Self-developed Sherry 1.25bit sparse ternary quantization + layer-by-layer mixed precision | Official native FP8 training; community relies on conventional solutions such as GGUF/Q4_K_M |
| SWE-Bench multilingual | 81.3% | About 82.6% (original version) |
| Long-context retrieval | Basically on par with the BF16 original version, almost lossless | 128K context, excellent performance |
| Minimum deployment threshold | A single 80GB VRAM + 64GB memory or heterogeneous stitching is enough to run | Full FP8 requires multiple A100/H100 cards; a single card requires extensive offload |
| Heterogeneous joint inference | Native support(prima.cpp cross-device cross-LAN scheduling) | No official support; you need to build a distributed framework yourself |
| Quantization scheme characteristics | Dynamically allocates precision based on layer sensitivity, with lower error than uniform quantization | Relies on community uniform quantization, with relatively uncontrollable precision loss |
Application scenarios of the Hy4 preview lightweight version
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Private code development assistant: Based on its coding capability of 82.9 on SWE-Bench multilingual, enterprises can deploy it on their intranet as a programming Copilot to ensure code security is not leaked.
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Intelligent Q&A for ultra-long documents: With near-lossless long-context retrieval capability, it is used for precise information extraction and summarization of hundred-page-level documents such as legal contracts, paper libraries, and financial reports.
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Scientific computing and mathematical reasoning: Covers scenarios such as mathematical derivation and equation solving, suitable for research institutions as an auxiliary reasoning tool to handle complex computational tasks.
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Low-cost cross-device AI services: Through heterogeneous joint inference, idle 4090 laptops and servers in the office are linked to run together, building internal AI services at extremely low hardware cost.
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Automated tool orchestration (Agent): Leveraging leading performance in tool-calling evaluations such as MCP Atlas, it connects external APIs and databases to implement automated workflows such as report generation and data scraping.