nllb-sango-finetuned-600m-v2-3 — superseded experiment, published for the record
Use
MEYNG/nllb-sango-finetuned-600m-v2-2instead. This checkpoint is the v2.3 run of MEYNG's five-experiment Sango↔French program. It is beaten by v2-2 in both directions and is kept public only so the negative result stays checkable.
This model was uploaded on 2026-07-17 with an auto-generated placeholder card and no licence declaration. This card replaces it. Nothing about the weights has changed.
What this run tested
v2.3 changed exactly one variable from the v2.2 recipe: it added native-verified vocabulary, oversampled ×5, to the cleaned training corpus. The hypothesis was that a small amount of high-quality, human-checked lexical data would improve out-of-distribution generation.
It did not. From the five-experiment log published in the v2-2 model card:
| Run | One variable changed | FR→SAG Δ vs baseline | SAG→FR Δ vs baseline |
|---|---|---|---|
| v2.2 (recommended) | lr 2e-4→1e-4, OOD-selected checkpoint | −1.71 | +1.25 |
| v2.3 (this model) | + native-verified vocabulary ×5 | −2.22 | +1.11 |
v2.3 is worse than v2.2 on both directions. The conclusion MEYNG drew from it — that small high-quality vocabulary injection does not improve OOD generation — is the reason this run exists and the reason it did not become the recommended model.
Those deltas are chrF against the untrained facebook/nllb-200-distilled-600M baseline, as
recorded in that log. They correspond to the SIB-200 column: the v2.2 row in the same table
(−1.71 / +1.25) matches v2-2's published SIB-200 deltas exactly. Per-benchmark FLORES+ figures
for this run have not been published, and no evaluation artifact for v2.3 exists in MEYNG's
results directory. Do not quote a FLORES number for this model.
Direction
As with every model in this series, the honest summary is direction-dependent: SAG→FR improves
on the baseline, FR→SAG does not. For French→Sango, use facebook/nllb-200-distilled-600M
directly. For Sango→French, use v2-2, not this model.
Model details
- Base model:
facebook/nllb-200-distilled-600M(Meta AI, NLLB-200). ArchitectureM2M100ForConditionalGeneration,d_model1024, 12 encoder + 12 decoder layers, vocab 256,206. - Method: LoRA fine-tune, merged to standalone weights (no
peftneeded at inference). - Requires
transformers>=4.40,<5.0— v5 breaks the NLLB tokenizer prefix. - Language tokens: NLLB conditioning via
forced_bos_token_id;sag_Latn/fra_Latn.
Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # transformers>=4.40,<5.0
model_id = "MEYNG/nllb-sango-finetuned-600m-v2-3" # prefer ...-v2-2
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
def translate(text, src="sag_Latn", tgt="fra_Latn"):
tok.src_lang = src
enc = tok(text, return_tensors="pt")
out = model.generate(**enc,
forced_bos_token_id=tok.convert_tokens_to_ids(tgt),
max_length=160, num_beams=4)
return tok.batch_decode(out, skip_special_tokens=True)[0]
Limitations
- Superseded. v2-2 is better in both directions. There is no use case where this model is the right choice over v2-2.
- FR→SAG trails the baseline out-of-distribution (−2.22 chrF): use the baseline for that direction.
- Domain skew: training data is predominantly religious/NGO-register web text; conversational and technical Sango are untested.
- "Verified" in MEYNG's vocabulary dataset means pipeline-verified, not native-speaker sign-off.
- No FLORES+ figures published for this run (see above).
Licence
CC BY-NC 4.0 — non-commercial use only.
This licence is inherited, not chosen. facebook/nllb-200-distilled-600M is released by Meta
under CC BY-NC 4.0. These weights are a derivative work of it, so the NonCommercial term carries
through.
Permitted: research, academic work, teaching, humanitarian and NGO deployment, public-sector and personal use. Not permitted: paid APIs or subscriptions, licensing to commercial entities, bundling into paid products, advertising-supported services.
Under Creative Commons' guidance the NonCommercial term restricts the use, not the user: a company may use these weights for genuinely non-commercial purposes, and a non-profit may breach the term by using them commercially.
Commercial use requires separate permission from Meta (for the base model) and from MEYNG (for the fine-tune). MEYNG cannot grant the first.
Attribution
This model is a derivative of NLLB-200 by Meta AI
(facebook/nllb-200-distilled-600M, CC BY-NC 4.0). Redistribution or adaptation of these weights
must credit both the base model and this fine-tune.
NLLB Team et al., No Language Left Behind: Scaling Human-Centered Machine Translation, 2022. arXiv:2207.04672
About MEYNG
MEYNG builds language AI infrastructure for under-resourced African languages, starting with Sango (Central African Republic, 5M+ speakers). Out-of-distribution gates before every release, failures documented and left public — this card is one of them. https://huggingface.co/MEYNG · https://meyng.com