---
language:
- en
- yo
- ha
- ig
- pcm
pipeline_tag: translation
---

# STORM-OS-MT-3B-BIDIRECTIONAL

A single, standalone machine translation model that translates **both
ways** between English and Yoruba, Hausa, Igbo, and Nigerian Pidgin —
local language into English, and English into any of the four local
languages. Fine-tuned from `facebook/nllb-200-3.3B` on a combined
forward + reverse dataset (204,094 examples), then merged into one clean
set of weights. No adapter loading or toggling required.

Nigerian Pidgin is not natively supported by the base model — this model
learned pcm as both a source and a target language from scratch.

## Supported languages

| Code | Language          |
|------|-------------------|
| yo   | Yoruba            |
| ha   | Hausa             |
| ig   | Igbo              |
| pcm  | Nigerian Pidgin   |

Both directions: local-language -> English, and English -> local-language.

## Loading the model

    import torch
    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

    MODEL_ID = "wolethereader/STORM-OS-MT-3B-BIDIRECTIONAL"

    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
    model = AutoModelForSeq2SeqLM.from_pretrained(
        MODEL_ID,
        torch_dtype=torch.bfloat16,
        device_map="auto",
    )
    model.eval()

## Running translation

    LANG_CODES = {
        "yo": "yor_Latn",
        "ha": "hau_Latn",
        "ig": "ibo_Latn",
        "pcm": "pcm_Latn",
    }
    EN = "eng_Latn"

    def translate(text: str, src_lang: str, tgt_lang: str, max_new_tokens: int = 128) -> str:
        tokenizer.src_lang = src_lang
        inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128).to(model.device)
        tgt_id = tokenizer.convert_tokens_to_ids(tgt_lang)
        with torch.no_grad():
            out = model.generate(
                **inputs,
                forced_bos_token_id=tgt_id,
                max_new_tokens=max_new_tokens,
                max_length=None,
            )
        return tokenizer.decode(out[0], skip_special_tokens=True)

### Example usage — local language -> English

    print(translate("Bawo ni?", LANG_CODES["yo"], EN))
    # -> "How are you?"

    print(translate("Na Malay guy I wan, I wan...", LANG_CODES["pcm"], EN))
    # -> "I want a Malay guy, I want..."

### Example usage — English -> local language

    print(translate("How are you doing today?", EN, LANG_CODES["ha"]))
    # -> "Yaya kake yau?"

    print(translate("Please help me find the hospital.", EN, LANG_CODES["pcm"]))
    # -> "Abeg help me find di hospital."

## Notes on quality

- Both directions are strong across all four languages, including on
  longer, real-world sentences (dates, facts, informal speech).
- Nigerian Pidgin works well in both directions despite having no native
  support in the base model — trained from scratch as part of this
  fine-tune.
- Igbo greetings (e.g. "Kedu?") remain a known weak spot; results can be
  inconsistent.

## Background: why this exists as a separate model

An earlier approach trained the reverse (English -> local language)
capability as a LoRA adapter on top of the already-shipped forward model
(`wolethereader/STORM-OS-MT-3B`), toggled per-request
(`wolethereader/STORM-OS-MT-3B-REVERSE`). That works, and is still in
production, but requires loading both a base model and an adapter and
switching between them per request.

This model was trained differently: from the raw base model, on both
directions combined in a single training run, so it merges cleanly into
one standalone set of weights with no adapter required. It's kept as its
own separate repo rather than replacing the original forward model, so
existing integrations aren't affected.

## Intended use

Machine translation component for PlotWeaver's voice infrastructure —
pairing with ASR transcription for full bidirectional speech<->text
translation across Yoruba, Hausa, Igbo, Nigerian Pidgin, and English.