OmniTranslate-1.1/README.md

base_model, tags, license, language, pipeline_tag, datasets

base_model	tags	license	language	pipeline_tag	datasets
MihaiPopa-1/OmniTranslate-1.0	text-generation-inference translation transformers unsloth qwen3 omnitranslate	apache-2.0	abk abq abs acm adh adi ady aeb afr agx aii aim ain ajz akb aln als alt amh anp aoz apc apt arb arg arq ars ary arz asm ast atb ava awa ayp ayr azb azj bak bam ban bar bas bbc bbk bcl bdq bel ben bew bho bhp bis biu bjn bod bos brh brx bts btx bug bul bwi bxr cat cbk ccp ceb ces cfm cha che chr chu chv cjs ckb ckt cmn cnh cnw cos crh crj crk crl crs csb csw csy ctd cym czt dak dan dar deu dik diu div dje dks dln dng dnw doi dru dsb dtp dty dzo ekk ell emj enl enm epo ess eus eve ewo ext fao fas ffm fij fil fin fit fkv fmu fra fro frp fry fuf fur fuv gag gaz gcf gla gle glg glk glv gmh gnb goh gom gos grc gsw gug guj guz hac hae hak hat hau haw hbo heb her hif hil hin hmr hne hns hrv hrx hsb hun hwc hye hyw iba ibg ibo ife ike ikt ilo ina ind inh isl ita ivv jav jpn jun kaa kab kac kak kal kam kan kas kat kaz kbd kca kdh kdr kea kei kgp kha khk khm kik kin kir kiu kjb kjh kmr knc koi kor kos kpv krj krl kru ksh ksw ktj ktz kua kum kwn kyu kzj lad lao lat lbe ldn lew lez lfn lim lin lis lit lki lld lmk lnd lrc ltg ltz lud lug luo lus lvs lwg lzh mag mah mai mak mal mar mas mbf mdf mer mfe mfg mfy mhi mhr mhy min mip mjw mkd mlt mni mnk mns mnw moh mph mqy mri mrj mrw mtg mui mup mus mvp mwf mwl mww mya myv myx mzh nah nan nap naq nbu nde ndo nds new nio njn njo nld nmf nmz nno nob nog non npi npo nrf nri nrm nse nus nya nyn nzm obo oci ojb olo orv ory oss ota oto otw pam pan pap pbt pcd pck pcm pfl plt pmq pmx pnb pnt pol por pov ppk pps prg pui pxm quc qul qup qus quz raw rcf rel rhg ria rjs rmc rml rmn rmy rnl roh ron rtm rue run rus sah san sat sck scn sda sdc sdh ses sgc sgh sid sin sju skr slk slv sma sme smj smn smo sms smt sna snd som sot spa srd srp ssw sul sun swe swg swh syc syl szl tab tam taq tat tcy tcz tel tet tgk tha thl tig tir tkl tkr tlh tly tok ton tpi tpw trc trp trs ttj tuk tur tuv twx tyv tzl tzm udm uig ukr urd uzn uzs vap vie vot vro war way wba wbm wes whk wlx wol wsg wwa xal xho xmm xmv xog yaz ydd yor yrk yrl yua yue zea zgh zom zsm zul	translation	MihaiPopa-1/OmniSurgical-1.1

OmniTranslate 1.1

OmniTranslate 1.1 is a massively multilingual machine translation model supporting over 500 languages. Fine-tuned from Qwen 3 0.6B (with Unsloth), this model is designed for translation tasks on any device!

Features

• 500+ Languages Supported: The broadest coverage of languages supported for a translation model that's under 1 billion parameters!
• Tiny Size: Beats any other large model on speed and memory usage. No other model is able to compete with this!

Improvements over 1.0

• OmniTranslate now makes less hiccups when translating to Romanian (like "ami"), and the diacritic bug on Romanian translations has been mostly fixed!

There's a tiny chance that the model will spit out without diacritics (mostly due to seeds) though, so try a different one.

Experimental Features

• We added 2 new languages, Emoji and Sulfuristic Speak (my own language for OmniTranslate 1.1 to quite fit the Chaos Cubed Minecraft vibe!). Try these out:

Emoji

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# 1. Load from your Hugging Face Repo
model_id = "MihaiPopa-1/OmniTranslate-1.1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32, # Standard for CPU
    device_map="cpu"           # Forces CPU usage
)

# 2. Translate to Emoji
prompt = "<|im_start|>user\nTranslate to emj_Emoj: We love the world!<|im_end|>\n<|im_start|>assistant\n<think>\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cpu")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=64, temperature=0.1)
    
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Sulfuristic Speak

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# 1. Load from your Hugging Face Repo
model_id = "MihaiPopa-1/OmniTranslate-1.1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32, # Standard for CPU
    device_map="cpu"           # Forces CPU usage
)

# 2. Translate to Sulfuristic Speak ("Translate to Sulfuristic Speak" also works too!)
prompt = "<|im_start|>user\nTranslate to sul_Latn: Let's ride a Sulfur Cube!<|im_end|>\n<|im_start|>assistant\n<think>\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cpu")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.1)
    
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Notes

OmniTranslate 1.1 is still a experimental model and shouldn't be used for tasks where accurate translations matter.

Notes

Providing the ISO code instead of the language name can improve the results a lot.

Usage

Code is by Gemini 3 Flash (then some little modifications by myself):

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# 1. Load from your Hugging Face Repo
model_id = "MihaiPopa-1/OmniTranslate-1.1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32, # Standard for CPU
    device_map="cpu"           # Forces CPU usage
)

# 2. Translate (replace ron_Latn with your language here)
prompt = "<|im_start|>user\nTranslate to ron_Latn: OmniTranslate is a massively multilingual machine translation model supporting over 500 languages!<|im_end|>\n<|im_start|>assistant\n<think>\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cpu")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
    
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Data Used

I used my own OmniSurgical 1.1, which the dataset itself contains a part of HF's FineTranslations

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Uploaded finetuned model

• Developed by: MihaiPopa-1
• License: apache-2.0
• Finetuned from model : unsloth/qwen3-0.6b-unsloth-bnb-4bit

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.