--- title: "Hy-MT2-1.8B-1.25Bit-GGUF" kind: "Kit" category: "Writing" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5af55098-8581-4ddd-9305-7b8866d4abe0" updated: "2026-11-13" --- # Hy-MT2-1.8B-1.25Bit-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Translates between 33 languages on your own device with a 1.8B model squeezed to 1.25 bits, about 440 MB as GGUF files for llama.cpp. For developers adding offline translation to apps. Hy-MT2-1.8B-1.25Bit-GGUF is a kit in the Writing category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by tencent ## About ### What it is This is a 1.25-bit GGUF build of Hy-MT2-1.8B, a Tencent translation model that covers 33 languages. AngelSlim did the quantization. The card says it shrinks the model to 440 MB and speeds up inference by 1.5 times, which makes it a fit for on-device use. ### Who it suits It suits developers who want translation running locally, in an app or on a small machine, without sending text to a server. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Apps | Mobile and desktop developers | Add offline translation to an app | Translating a chat message on the device | | Localization | Localization teams | Draft translations between the supported languages | Turning a product string into another language | | Edge devices | Embedded developers | Run translation where storage is tight | Fitting a model of about 440 MB | | Research | Translation researchers | Compare quantization levels of one model | Choosing between the 1.25-bit, 2-bit and full files | If you want a larger model, you can skip this one, since the family also has 7B and 30B-A3B sizes. ### Where to use it - llama.cpp, which the card names for its GGUF builds - Windows, macOS or Linux, with Python for downloading the files - Tencent's Hy-MT2-Translator skill, which the card offers for bringing the models into translation tasks | Use case | Who | What they get back | | --- | --- | --- | | On-device translation | App developers | Translated text with no server call | | Instruction-guided translation | Users | Translations that follow instructions written in several languages | | Quantization comparison | Researchers | A tiny build to set against the 2-bit and full versions | ### How to set it up - Python and the Hugging Face hub package, to download the files - llama.cpp, installed by its own instructions - About 440 MB of space for the model 1. Install the Hugging Face hub package. 2. Download the model files with the command below. 3. Install llama.cpp. 4. Point llama.cpp at the downloaded GGUF file and give it a translation request. ```sh pip install -U huggingface_hub hf download tencent/Hy-MT2-1.8B-1.25Bit-GGUF ``` ### How to use it The card shows no run command for this build. You load the GGUF file in llama.cpp, then ask for a translation and name the target language. The card says the model follows translation instructions written in several languages. Builds in the Hy-MT2 family | Model | What it is | | --- | --- | | Hy-MT2-1.8B | The 1.8B translation model in full form | | Hy-MT2-1.8B-FP8 | The 1.8B model with FP8 quantization | | Hy-MT2-1.8B-GGUF | The 1.8B model as GGUF files for llama.cpp | | Hy-MT2-1.8B-2bit-GGUF | The 1.8B model as 2-bit GGUF files | | Hy-MT2-1.8B-1.25bit-GGUF | The 1.8B model as 1.25-bit GGUF files | | Hy-MT2-7B | The 7B translation model | | Hy-MT2-7B-FP8 | The 7B model with FP8 quantization | | Hy-MT2-7B-GGUF | The 7B model as GGUF files for llama.cpp | | Hy-MT2-30B-A3B | The 30B-A3B translation model | | Hy-MT2-30B-A3B-FP8 | The 30B-A3B model with FP8 quantization | ### Example You give the model a short English sentence and ask for it in another of the 33 languages, in the instruction language you prefer. A translated sentence comes back. The card text shown here gives no sample output. ### For newcomers and experts GGUF is a file format that llama.cpp can run on an ordinary computer. A 1.25-bit file is squeezed hard, so it is small and quick. A good first try is one short sentence, checked by someone who reads both languages. The Hy-MT2 line has three sizes, 1.8B, 7B and 30B-A3B, built to give quick translation answers. This build comes from the 1.8B model through AngelSlim quantization. The card says the full 1.8B model beats mainstream commercial translation APIs overall, but it lists no scores for this 1.25-bit file. Text and a translation instruction → 1.25-bit 1.8B model in llama.cpp → Translated text comes out ### Good to know - Squeezing weights to 1.25 bits usually costs some quality, and the visible card gives no scores for this build. - The release also includes IFMTBench, a benchmark for how well a model follows translation instructions. - The 440 MB and 1.5 times figures come from the card, and they describe the 1.25-bit build. - License: Apache 2.0 - Cost to run: none named - Data it reaches: none named beyond the text you give it - Not for: none named ## README ````markdown # Hy-MT2-1.8B-1.25Bit-GGUF Translates between 33 languages on your own device with a 1.8B model squeezed to 1.25 bits, about 440 MB as GGUF files for llama.cpp. For developers adding offline translation to apps. 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: This is a 1.25-bit GGUF build of Hy-MT2-1.8B, a Tencent translation model that covers 33 languages. AngelSlim did the quantization. The card says it shrinks the model to 440 MB and speeds up inference by 1.5 times, which makes it a fit for on-device use. Who it suits: It suits developers who want translation running locally, in an app or on a small machine, without sending text to a server. ```table Industry | Who uses it | How they use it | Example Apps | Mobile and desktop developers | Add offline translation to an app | Translating a chat message on the device Localization | Localization teams | Draft translations between the supported languages | Turning a product string into another language Edge devices | Embedded developers | Run translation where storage is tight | Fitting a model of about 440 MB Research | Translation researchers | Compare quantization levels of one model | Choosing between the 1.25-bit, 2-bit and full files ``` If you want a larger model, you can skip this one, since the family also has 7B and 30B-A3B sizes. Where to use it: - llama.cpp, which the card names for its GGUF builds - Windows, macOS or Linux, with Python for downloading the files - Tencent's Hy-MT2-Translator skill, which the card offers for bringing the models into translation tasks ```table Use case | Who | What they get back On-device translation | App developers | Translated text with no server call Instruction-guided translation | Users | Translations that follow instructions written in several languages Quantization comparison | Researchers | A tiny build to set against the 2-bit and full versions ``` How to set it up: - Python and the Hugging Face hub package, to download the files - llama.cpp, installed by its own instructions - About 440 MB of space for the model 1. Install the Hugging Face hub package. 2. Download the model files with the command below. 3. Install llama.cpp. 4. Point llama.cpp at the downloaded GGUF file and give it a translation request. ```sh pip install -U huggingface_hub hf download tencent/Hy-MT2-1.8B-1.25Bit-GGUF ``` How to use it: The card shows no run command for this build. You load the GGUF file in llama.cpp, then ask for a translation and name the target language. The card says the model follows translation instructions written in several languages. ```table Title: Builds in the Hy-MT2 family Model | What it is Hy-MT2-1.8B | The 1.8B translation model in full form Hy-MT2-1.8B-FP8 | The 1.8B model with FP8 quantization Hy-MT2-1.8B-GGUF | The 1.8B model as GGUF files for llama.cpp Hy-MT2-1.8B-2bit-GGUF | The 1.8B model as 2-bit GGUF files Hy-MT2-1.8B-1.25bit-GGUF | The 1.8B model as 1.25-bit GGUF files Hy-MT2-7B | The 7B translation model Hy-MT2-7B-FP8 | The 7B model with FP8 quantization Hy-MT2-7B-GGUF | The 7B model as GGUF files for llama.cpp Hy-MT2-30B-A3B | The 30B-A3B translation model Hy-MT2-30B-A3B-FP8 | The 30B-A3B model with FP8 quantization ``` Example: You give the model a short English sentence and ask for it in another of the 33 languages, in the instruction language you prefer. A translated sentence comes back. The card text shown here gives no sample output. For newcomers and experts: GGUF is a file format that llama.cpp can run on an ordinary computer. A 1.25-bit file is squeezed hard, so it is small and quick. A good first try is one short sentence, checked by someone who reads both languages. The Hy-MT2 line has three sizes, 1.8B, 7B and 30B-A3B, built to give quick translation answers. This build comes from the 1.8B model through AngelSlim quantization. The card says the full 1.8B model beats mainstream commercial translation APIs overall, but it lists no scores for this 1.25-bit file. ```flow Text and a translation instruction 1.25-bit 1.8B model in llama.cpp Translated text comes out ``` Good to know: - Squeezing weights to 1.25 bits usually costs some quality, and the visible card gives no scores for this build. - The release also includes IFMTBench, a benchmark for how well a model follows translation instructions. - The 440 MB and 1.5 times figures come from the card, and they describe the 1.25-bit build. - License: Apache 2.0 - Cost to run: none named - Data it reaches: none named beyond the text you give it - Not for: none named ## On Hugging Face, 13 November 2026 - Parameters: 1.8B - Weights: about 896 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 11,029 - Likes: 50 - License: apache-2.0 - Based on: tencent/Hy-MT2-1.8B - Tags: gguf, conversational ## Links - Model: https://huggingface.co/tencent/Hy-MT2-1.8B-1.25Bit-GGUF - Files: https://huggingface.co/tencent/Hy-MT2-1.8B-1.25Bit-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download tencent/Hy-MT2-1.8B-1.25Bit-GGUF ``` 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/Hy-MT2-1.8B-1.25Bit-GGUF on Hugging Face](https://huggingface.co/tencent/Hy-MT2-1.8B-1.25Bit-GGUF) ## 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 | | --- | --- | --- | --- | --- | --- | | Hy-MT2-1.8B-1.25Bit-GGUF (this page) | 11,029 | 50 | 1.8B | apache-2.0 | 2026-06-11 | | [humanizer-GGUF](https://mindket.com/p/89f54338-f7b0-4cfe-ae35-5814d0974a44) | 26,382 | 65 | 11.9B | apache-2.0 | 2026-10-08 | | [gugujiji\_novel-humanize-4b-GGUF](https://mindket.com/p/3f3b715a-77be-423f-a5d1-37746de742fe) | 7,774 | 5 | 4B | apache-2.0 | 2026-10-26 | | [Gemma-3-12b-it-MAX-HORROR-Imatrix-GGUF](https://mindket.com/p/9b60b978-ab30-4682-9d98-a9a00c9e38e8) | 2,805 | 19 | 11.8B | gemma | 2026-11-09 | | [Bielik-11B-v3.1-Instruct GGUF](https://mindket.com/p/d0b293b3-7c12-4f26-b233-dbb17c88aa25) | 1,872 | 8 | 11.2B | apache-2.0 | 2026-11-10 | | [ibnsina-1.5b](https://mindket.com/p/c8069e86-0fcf-40bd-b069-66308bd20c78) | 803 | 3 | 1.5B | apache-2.0 | 2026-09-17 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-06-11 - First published: 2026-05-20 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open tencent/Hy-MT2-1.8B-1.25Bit-GGUF on Hugging Face](https://huggingface.co/tencent/Hy-MT2-1.8B-1.25Bit-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5af55098-8581-4ddd-9305-7b8866d4abe0) - [Its page on Mindket](https://mindket.com/p/5af55098-8581-4ddd-9305-7b8866d4abe0)