--- title: "Hy4-preview" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/7891a0bb-5421-4aa9-94dc-7e06bf6ef3e2" updated: "2026-11-12" --- # Hy4-preview > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates text, code and documents with a Mixture-of-Experts model of 770B parameters that activates 49B and handles up to 1M context. For teams with the hardware to host open weights of this size. Hy4-preview is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by tencent ## About ### What it is Hy4 preview is a flagship Mixture-of-Experts language model from the Tencent Hy Team. The card gives 770B parameters in total, with 49B activated per token, and a context length of 1M. It is aimed at software engineering, office and analysis work, and game development. ### Who it suits It suits teams that run huge open-weight models on their own infrastructure and want one for coding, document work or game prototyping. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Engineers | Plan, debug and verify long development tasks | Work through a front-end feature over many steps | | Office work | Analysts and finance teams | Turn messy files into documents, spreadsheets and slides | Build a financial model from scattered files | | Games | Game developers | Start from one prompt and refine a prototype over several turns | A playable prototype that they keep improving | Small teams without datacenter-scale hardware can skip hosting it, since the weights alone are huge. ### Where to use it - Hugging Face Transformers, the library Hugging Face lists for it - vLLM and SGLang, which the card's contents list as deployment options - Windows, macOS or Linux, where you can download the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Software engineering | Developers | Help with planning, debugging and checking long tasks | | Office and analysis | Analysts | Documents, spreadsheets and presentations built from many files | | Game prototyping | Game developers | A prototype from one prompt that can be refined across turns | ### How to set it up You need these first: - Python - The huggingface\_hub package - Storage and GPU memory for weights this large, about 1,560 GB in BF16 or about 390 GB at 4-bit as an estimate 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. To serve the model, follow the card's vLLM or SGLang deployment sections. ```sh pip install -U huggingface_hub hf download tencent/Hy4-preview ``` ### How to use it Download the files, serve them with vLLM or SGLang, then send prompts for coding, analysis or game work. The card text here has no run commands, so follow its Quickstart and Deployment sections for the exact calls. ### Example A developer could give it one prompt describing a small game and ask for a playable prototype. The card says the model works with game engines, so the developer can then keep refining the project over several turns. ### For newcomers and experts Hy4 preview is a huge language model, and its files are too big for an ordinary computer. A realistic first step is to read the card and its Quickstart before downloading anything. The backbone has 78 layers. The first uses a standard dense FFN. The other 77 are MoE layers with 256 routed experts and 1 shared expert, and each token uses the top 8 routed experts plus the shared one. One native MTP layer supports speculative decoding. Attention is Gated DeepSeek Sparse Attention with IndexCache, and the residual path uses identity Hyper-Connections. The card's 770B leaves out the MTP layer, and Hugging Face lists 780B for the files. A prompt goes in → A router picks 8 of 256 experts per token → The shared expert adds its output → The model writes the reply ### Good to know - Size: Hugging Face lists 780B parameters, about 1,560 GB in BF16 and about 390 GB at 4-bit, as an estimate - Versions: Hugging Face lists 6 quantized and 2 fine-tuned versions made by other people - Sections: the card's contents also list Finetuning and Quantization, which this text does not include Parameters in billions | Label | Value | | --- | --- | | Total backbone | 770 B | | Activated per token | 49 B | Weight size in GB | Label | Value | | --- | --- | | BF16 | 1,560 GB | | 4-bit estimate | 390 GB | - License: apache-2.0 - Cost to run: none named, no API key or account - Data it reaches: it downloads model files from Hugging Face, and the card text names no other service or account - Not for: small machines, since the weights alone take about 1,560 GB in BF16 ## README ````markdown # Hy4-preview Generates text, code and documents with a Mixture-of-Experts model of 770B parameters that activates 49B and handles up to 1M context. For teams with the hardware to host open weights of this size. Made by tencent on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Hy4 preview is a flagship Mixture-of-Experts language model from the Tencent Hy Team. The card gives 770B parameters in total, with 49B activated per token, and a context length of 1M. It is aimed at software engineering, office and analysis work, and game development. Who it suits: It suits teams that run huge open-weight models on their own infrastructure and want one for coding, document work or game prototyping. ```table Industry | Who uses it | How they use it | Example Software | Engineers | Plan, debug and verify long development tasks | Work through a front-end feature over many steps Office work | Analysts and finance teams | Turn messy files into documents, spreadsheets and slides | Build a financial model from scattered files Games | Game developers | Start from one prompt and refine a prototype over several turns | A playable prototype that they keep improving ``` Small teams without datacenter-scale hardware can skip hosting it, since the weights alone are huge. Where to use it: - Hugging Face Transformers, the library Hugging Face lists for it - vLLM and SGLang, which the card's contents list as deployment options - Windows, macOS or Linux, where you can download the files with Python ```table Use case | Who | What they get back Software engineering | Developers | Help with planning, debugging and checking long tasks Office and analysis | Analysts | Documents, spreadsheets and presentations built from many files Game prototyping | Game developers | A prototype from one prompt that can be refined across turns ``` How to set it up: You need these first: - Python - The huggingface_hub package - Storage and GPU memory for weights this large, about 1,560 GB in BF16 or about 390 GB at 4-bit as an estimate 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. To serve the model, follow the card's vLLM or SGLang deployment sections. ```sh pip install -U huggingface_hub hf download tencent/Hy4-preview ``` How to use it: Download the files, serve them with vLLM or SGLang, then send prompts for coding, analysis or game work. The card text here has no run commands, so follow its Quickstart and Deployment sections for the exact calls. Example: A developer could give it one prompt describing a small game and ask for a playable prototype. The card says the model works with game engines, so the developer can then keep refining the project over several turns. For newcomers and experts: Hy4 preview is a huge language model, and its files are too big for an ordinary computer. A realistic first step is to read the card and its Quickstart before downloading anything. The backbone has 78 layers. The first uses a standard dense FFN. The other 77 are MoE layers with 256 routed experts and 1 shared expert, and each token uses the top 8 routed experts plus the shared one. One native MTP layer supports speculative decoding. Attention is Gated DeepSeek Sparse Attention with IndexCache, and the residual path uses identity Hyper-Connections. The card's 770B leaves out the MTP layer, and Hugging Face lists 780B for the files. ```flow A prompt goes in A router picks 8 of 256 experts per token The shared expert adds its output The model writes the reply ``` Good to know: - Size: Hugging Face lists 780B parameters, about 1,560 GB in BF16 and about 390 GB at 4-bit, as an estimate - Versions: Hugging Face lists 6 quantized and 2 fine-tuned versions made by other people - Sections: the card's contents also list Finetuning and Quantization, which this text does not include ```chart Title: Parameters in billions Total backbone: 770 B Activated per token: 49 B ``` ```chart Title: Weight size in GB BF16: 1,560 GB 4-bit estimate: 390 GB ``` - License: apache-2.0 - Cost to run: none named, no API key or account - Data it reaches: it downloads model files from Hugging Face, and the card text names no other service or account - Not for: small machines, since the weights alone take about 1,560 GB in BF16 ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 780B - Weights: about 1,560 GB in BF16; about 390 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 11,424 - Likes: 557 - License: apache-2.0 - Quantized versions: 6 - Fine-tunes: 2 - Tags: transformers, safetensors, hy_v4, text-generation, hunyuan, hy4, conversational ## Links - Model: https://huggingface.co/tencent/Hy4-preview - Files: https://huggingface.co/tencent/Hy4-preview/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download tencent/Hy4-preview ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [tencent/Hy4-preview on Hugging Face](https://huggingface.co/tencent/Hy4-preview) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Hy4-preview (this page) | 11,424 | 557 | 780B | apache-2.0 | 2026-08-28 | | [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 | | [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 | | [Qwen2.5-7B-Instruct](https://mindket.com/p/b38cb44b-c57b-4e0c-b5f7-3e9da523b8d7) | 6,364,238 | 2,494 | 7.6B | apache-2.0 | 2025-01-12 | | [Qwen3-8B](https://mindket.com/p/408d17f8-b1a4-4b5f-8440-2d40ec001638) | 6,269,141 | 2,184 | 8.2B | apache-2.0 | 2025-07-26 | | [gpt-oss-20b](https://mindket.com/p/d353330b-254d-4db1-9a53-f0b1f8fe2945) | 5,685,450 | 5,299 | 20.9B | apache-2.0 | 2025-08-26 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · ixaxaar · 2026-08-28](https://news.ycombinator.com/item?id=49479496): Tencent Releases and Open-Sources Tencent Hy4 Preview - [Bluesky · @pranjulrathour.bsky.social · 2026-11-01](https://bsky.app/profile/pranjulrathour.bsky.social/post/3mzafjkzz5b2m): This week on Hugging Face: a 780-billion-parameter open model from Tencent and a 3-billion-parameter open model from IBM. Guess which one a student can actually run. Hy4-preview is 780B. Granite 4.2 comes in 3B, 8B and… https://pranjulrathour.scult.in · Pranjul Rathour · pranjulrathour41@gmail.com - [Hacker News · theanonymousone · 2026-08-28](https://news.ycombinator.com/item?id=49475151): Tencent Hy4 Preview - [Bluesky · Kol Tregaskes · 2026-11-04](https://bsky.app/profile/koltregaskes.bsky.social/post/3mzjfpticoy2k): Tencent says Hy4 is among the strongest open models, though it says a notable gap remains with top proprietary models on research-level maths, long-horizon coding and advanced visual question answering. In its internal tests the hallucination rate fell from 15.76% on Hy4 preview to 5.45%. - [Hacker News · itvision · 2026-08-28](https://news.ycombinator.com/item?id=49483991): Tencent Hy4 Preview LLM - [Bluesky · fry69 · 2026-11-04](https://bsky.app/profile/fry69.dev/post/3mzi7nrtqvs2z): The weights have not been released yet, but a two months old preview snapshot is available on Hugging Face -\> huggingface.co/tencent/Hy4-... MoE, 780B total parameters with 49B active - [Hacker News · iab · 2026-10-20](https://news.ycombinator.com/item?id=50159779): Ask HN: What happened to open weights models?: I mean: - Aug 26: GLM-5.3 Flash, Qwen3.8 Flash Next - Aug 28: GLM-5.3, Tencent Hy4 Preview - Aug 31: DeepSeek V4 Flash Vision Exp - Sep 3: Ling-3.0 Flash Fin - Sep 8: Ling-3.0 Flash VL - Sep 10: DeepSeek V4.1 Flash - Sep 22: Xiaomi MiMo V2.6 - Oct 3: Aleph Alpha Kolibri-1 - Oct 13: Ling-3.1 Flash… - [Bluesky · fronset.ai · 2026-11-12](https://bsky.app/profile/fronset.ai/post/3n2543hugg42s): Because length bias flips by task, benchmark scores must be read per task — Profiled Document Analysis CI for Tencent Hy4 Preview is now ±0.26 (down from ±0.32). - [Hacker News · jychang · 2026-08-31](https://news.ycombinator.com/item?id=49505329): Hy4 preview: https://huggingface.co/tencent/Hy4-preview - [Bluesky · Steven \| SelfHostedSI · 2026-11-04](https://bsky.app/profile/selfhostedsi.bsky.social/post/3mziufia2nn2f): Public Hy4 files today: the preview only, Apache 2.0, uploaded 27 Aug: huggingface.co/tencent/Hy4... - [Hacker News · scirob · 2026-08-30](https://news.ycombinator.com/item?id=49502506): Hy4 preview: Just got benchmarks done for German langauge eval index i help maintain. Hy4 is a big improvement over h3 but still below Deepseek pro and Significantly below GLM 5.3 Flash . hy4 ranks \~14th overall https://dach.peerbench.ai/compare?models=tencent%2Fhy4-previ...… - [Bluesky · Sipirtu · 2026-11-04](https://bsky.app/profile/sipirtu.com/post/3mzir6lblcp2f): Tencent released Hy4, a large language model that adds computer-use capabilities and visual understanding. The model builds on the Hy4 Preview and weighs 770 billion parameters. Source: SiliconANGLE ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-08-28 - First published: 2026-08-27 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open tencent/Hy4-preview on Hugging Face](https://huggingface.co/tencent/Hy4-preview) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/7891a0bb-5421-4aa9-94dc-7e06bf6ef3e2) - [Its page on Mindket](https://mindket.com/p/7891a0bb-5421-4aa9-94dc-7e06bf6ef3e2)