Iris-300M

Iris-300M is a 290.6M-parameter encoder-decoder Transformer for machine translation between 11 European languages, trained from scratch on a single RTX 3070. It translates directly between any pair of supported languages (no English pivot).

Part of the Iris model family by BranchingNLP (Michelangelo Di Nicola).

Supported languages

• en English
• it Italian
• fr French
• es Spanish
• pt Portuguese
• de German
• nl Dutch
• ro Romanian
• pl Polish
• cs Czech
• sv Swedish

Architecture

Parameters	290,641,920
Encoder / decoder layers	10 / 10
d_model / heads / FFN	1024 / 16 / 2816
Vocabulary	48,000 (SentencePiece BPE, shared)
Max sequence length	512 tokens
Positional encoding	sinusoidal
Language conditioning	learned language embeddings added to encoder and decoder inputs
Tied embeddings	input embedding = output projection
Training steps	5,930,000

Evaluation — FLORES-200 devtest

1012 sentences per direction, beam size 5, sacreBLEU.

Direction	BLEU	spBLEU	chrF
it → en	24.79	28.80	56.39
en → it	20.95	26.26	52.49
fr → en	29.18	32.09	57.26
en → fr	26.33	29.58	54.70
es → en	18.88	22.09	50.75
en → es	17.00	19.89	47.05
pt → en	33.34	36.42	60.55
en → pt	27.84	31.45	56.72
de → en	24.34	26.80	52.58
en → de	15.88	19.10	46.68
nl → en	18.60	21.36	48.09
en → nl	14.01	17.66	45.00
ro → en	26.95	29.92	56.88
en → ro	18.55	22.73	49.15
pl → en	15.46	18.28	45.94
en → pl	8.69	14.45	38.95
cs → en	22.03	24.61	51.64
en → cs	13.50	18.33	42.38
sv → en	29.98	31.76	56.83
en → sv	23.74	26.52	52.72
Average X → en	24.36	27.21	53.69
Average en → X	18.65	22.60	48.58
Average (all 20)	21.50	24.91	51.14

Usage

pip install torch sentencepiece safetensors
python inference.py --src it --tgt en "Domani mattina devo passare in farmacia."
python inference.py --src en --tgt de --beam 5 "Please back up your data before updating."

from inference import load, translate
model, sp, cfg, device = load()
print(translate("Il treno è in ritardo.", "it", "fr", model, sp, cfg, device))

Training data

• FineTranslations (HuggingFaceFW/finetranslations) — web documents paired with English translations, all 10 languages
• Europarl, Tatoeba
• Italian YouTube subtitles translated into 10 languages with NLLB-600M (distilled)
• Small synthetic sets targeting specific weaknesses (proper names, colloquial phrases)

Every pair is used in both directions during training.

Limitations

• Idioms and colloquial expressions are often translated literally ("stanco morto" → "tired of death").
• Domain jargon (gaming/streaming, some IT terms) is inconsistent.
• Polish and Czech are the weakest languages, especially en → pl / en → cs; basic vocabulary errors still occur.
• Inputs longer than ~512 tokens are truncated: split long texts into sentences.
• Not suitable for high-stakes use (medical, legal) without human review.

Citation

@misc{iris300m,
  author = {Michelangelo Di Nicola, Roberto D'Arcangelo},
  title  = {Iris-300M: a compact multilingual European translation model},
  year   = {2026},
  publisher = {Hugging Face}
}