Gemma-2-2b-jpn-it-translate-i1-GGUF Open Source Model - Freely Implement Japanese-English Translation Function

Gemma 2 2b Jpn It Translate I1 GGUF

Developed by mradermacher

Quantized version based on webbigdata/gemma-2-2b-jpn-it-translate, focusing on translation tasks between Japanese and English

Machine Translation Supports Multiple Languages#Japanese-English translation #Low-resource quantization #Lightweight model

Downloads 182

Release Time : 3/18/2025

Model Overview

This is a quantized version of the 2B-parameter Gemma model, specifically designed for translation tasks between Japanese and English. The model has undergone various quantization processes, offering different sizes and quality versions to accommodate various hardware requirements.

Model Features

Multiple quantization versions

Offers 24 quantization versions from IQ1_S to Q6_K to meet usage needs under different hardware conditions

Efficient inference

Quantized models are smaller in size and faster in inference, suitable for resource-constrained environments

Translation quality retention

Strives to preserve the original model's translation capabilities during quantization, especially in higher quantization versions

Model Capabilities

Japanese to English translation

English to Japanese translation

Text generation

Use Cases

Language services

Document translation

Translate Japanese documents into English or vice versa

Provides fast and accurate translation results

Real-time translation

Achieves real-time translation functionality on resource-limited devices

Low-latency translation experience

base_model: webbigdata/gemma-2-2b-jpn-it-translate language:

ja
en library_name: transformers quantized_by: mradermacher tags:
translation

About

weighted/imatrix quants of https://huggingface.co/webbigdata/gemma-2-2b-jpn-it-translate

static quants are available at https://huggingface.co/mradermacher/gemma-2-2b-jpn-it-translate-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	i1-IQ1_S	0.9	for the desperate
GGUF	i1-IQ1_M	1.0	mostly desperate
GGUF	i1-IQ2_XXS	1.0
GGUF	i1-IQ2_XS	1.1
GGUF	i1-IQ2_S	1.1
GGUF	i1-IQ2_M	1.2
GGUF	i1-Q2_K_S	1.3	very low quality
GGUF	i1-IQ3_XXS	1.3	lower quality
GGUF	i1-Q2_K	1.3	IQ3_XXS probably better
GGUF	i1-IQ3_XS	1.4
GGUF	i1-IQ3_S	1.5	beats Q3_K*
GGUF	i1-Q3_K_S	1.5	IQ3_XS probably better
GGUF	i1-IQ3_M	1.5
GGUF	i1-Q3_K_M	1.6	IQ3_S probably better
GGUF	i1-Q3_K_L	1.7	IQ3_M probably better
GGUF	i1-IQ4_XS	1.7
GGUF	i1-IQ4_NL	1.7	prefer IQ4_XS
GGUF	i1-Q4_0	1.7	fast, low quality
GGUF	i1-Q4_K_S	1.7	optimal size/speed/quality
GGUF	i1-Q4_K_M	1.8	fast, recommended
GGUF	i1-Q4_1	1.9
GGUF	i1-Q5_K_S	2.0
GGUF	i1-Q5_K_M	2.0
GGUF	i1-Q6_K	2.3	practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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