Hy4 Preview Lightweight Version - Tencent Hunyuan Open Source Hy4 Quantization Compression Model - AiBoss

What is the Hy4 preview lightweight version?

The Hy4 preview lightweight version is a 1.5TB version of the original 1.5TB model from Tencent's Hunyuan platform. Hy4 preview This open-source large model, compressed to approximately 214GB, utilizes self-developed Sherry 1.25-bit sparse quantization and MIX-STQ1_0 layer-by-layer mixed precision technology. While significantly lowering the hardware requirements, the model retains core capabilities such as long text understanding and code generation, and supports cross-network joint inference across multiple heterogeneous devices, allowing ordinary developers to run flagship-level large models locally.

Key features of Hy4 preview Lite

Code generationThe model performs excellently in multi-language code evaluations such as SWE-Bench, providing developers with high-quality programming assistance and the ability to solve complex engineering problems.
Office document processingIt supports long document comprehension, content summarization, and routine Q&A, and can handle text processing and information extraction tasks in daily office scenarios.
Scientific reasoningThe model possesses mathematical calculation and scientific task solving capabilities, and maintains stable performance even in scenarios requiring logical deduction and numerical computation.
Long context searchIn multi-turn dialogues, it can accurately locate and extract key information from long texts, and its ability to understand long texts is almost on par with the original model.
Tool callThrough protocols such as MCP, the model can connect to external tools and APIs to automate task execution and complex workflow orchestration.
Game generationBased on instructions, the model can generate runnable games that include interfaces and interactive logic, such as the NEON DRIFT game generation case shown in the article.
Heterogeneous Joint ReasoningWith the help of cross-device scheduling in prima.cpp, multiple machines with different configurations can run the model collaboratively across local area networks without the need to purchase homogeneous high-end hardware clusters.

Technical Principles of Hy4 Preview Lite

Sherry Sparse Ternary QuantizationTencent's Hunyuan self-developed extreme compression algorithm maps weights into groups of four to {-1, 0, +1} and forces each group to retain one zero. Each group requires only 5 bits for encoding, achieving average compression. 1.25 bit/weight It offers high storage efficiency and is naturally compatible with SIMD acceleration, completing the entire chain from quantization to inference in llama.cpp.
MIX-STQ1_0 Mixed Precision StrategyAbandoning a uniform compression scheme, the system evaluates sensitivity layer by layer on the calibration set and then processes data in tiers—sensitive layers retain higher precision (IQ2_XXS), while insensitive layers adopt a more aggressive STQ1_0 approach. Ultimately, the average weight of routing experts is only [missing information]. 1.78 bpwThe overall error is actually lower than the unified quantization baseline, while saving more than 5GB of storage.
Heterogeneous Distributed ReasoningBased on the cross-device scheduling capability of prima.cpp, the MoE layer is split and executed on different hardware (such as a laptop 4090 and a server A4000). By overlapping the computation and weight reading, the 214GB model can achieve usable inference speed in a heterogeneous environment with a total of 144GB of video memory + memory, without the need to purchase homogeneous high-end clusters.

How to use Hy4 preview lightweight version

Model AcquisitionDownload the AngelSlim/Hy4-preview-GGUF quantization model file from HuggingFace to your local machine.
Local reasoningLoad and run a lightweight 214GB model using llama.cpp, which supports the STQ1_0 inference kernel.
Heterogeneous deployment: By using prima.cpp, multiple heterogeneous devices (such as laptops and servers) can be connected across a network to achieve distributed joint inference.

Hy4 preview lightweight version's core advantages

Extreme compression lowers the barrier to entryBy using self-developed quantization technology, the model was compressed from 1.5TB to 214GB, enabling even machines with limited configurations to run flagship-level large models.
Sherry's quantization is both small and fast.The self-developed sparse ternary quantization algorithm achieves an average weight of 1.25 bits and is naturally compatible with SIMD, with inference speed superior to similar low-bit solutions.
Layer-by-layer mixing ensures precision and qualityThe MIX-STQ1_0 uses layer-by-layer compression based on sensitivity, reducing overall error without increasing the bit budget, and outperforming the unified quantization baseline in evaluations.
Core capabilities virtually undamagedLong text comprehension and multi-round retrieval are on par with the original version, while tasks such as code, mathematics, and tool calls only show a slight decline, fully covering daily productivity scenarios.
Heterogeneous device joint inferenceIt supports the joint operation of multiple machines with different configurations across local area networks, enabling low-cost deployment of ultra-large models without the need to purchase homogeneous high-end clusters.

Hy4 preview lightweight version project address

HuggingFace model library：https://huggingface.co/AngelSlim/Hy4-preview-GGUF

Hy4 Preview Lightweight Version Comparison with Similar Products

Comparison Dimensions	Hy4 preview lightweight version	DeepSeek-V4
Publisher	Tencent Hunyuan	DeepSeek
Model Architecture	MoE, with a total parameter count of nearly 1.5T.	MoE, total parameters 1.6T (approximately 32B for activation)
Lightweight copyright	214 GB(MIX-STQ1_0 Quantitative Analysis)	Official FP8: approximately 300GB+; Community INT4: approximately 200GB+
Core compression technology	Self-developed Sherry 1.25-bit sparse ternary quantization + layer-by-layer mixing precision	Official native FP8 training; community-based solutions rely on conventional GGUF/Q4_K_M, etc.
SWE-Bench Multilingual	81.3%	Approximately 82.6% (original version)
Long context search	Basically identical to the original BF16, with almost no damage.	Excellent performance with 128K context.
Minimum deployment threshold	Single unit with 80GB of video memory + 64GB of RAM It can also be run by combining heterogeneous components.	A complete FP8 requires multiple A100/H100 cards; a single card requires significant offload.
Heterogeneous Joint Reasoning	Native support(prima.cpp: Cross-device and cross-LAN scheduling)	There is no official support; you need to build your own distributed framework.
Features of Quantification Scheme	Precision is dynamically allocated based on layer sensitivity, with an error lower than that of uniform quantization.	Relying on community-wide unified quantization results in relatively uncontrollable loss of accuracy.

Application scenarios of Hy4 preview lightweight version

Private code development assistantBased on its SWE-Bench multi-language code capability of 82.9, enterprises can deploy it on the intranet as a programming Copilot to ensure code security and prevent leakage.
Intelligent Q&A for Long DocumentsWith near-lossless long-context retrieval capabilities, it is used for accurate information extraction and summarization of hundreds of pages of documents such as legal contracts, academic papers, and financial reports.
Scientific computing and mathematical reasoningIt covers scenarios such as mathematical derivation and formula solving, and is suitable for scientific research institutions to use as an auxiliary reasoning tool to handle complex calculation tasks.
Low-cost AI services across devicesBy using heterogeneous joint inference, idle 4090 laptops in the office are spliced together with servers to run internal AI services at extremely low hardware costs.
Automation tool orchestration (Agent)Leveraging tools like MCP Atlas to call upon leading performance metrics, and connecting external APIs and databases, we can automate workflows such as report generation and data scraping.