# Swahili Medical Translation: No Language Left Behind (NLLB) Tune Cuts 31% vs Prompt

Lauren Sanders · September 22, 2026

> Domain-tuned NLLB cuts Swahili medical terminology errors by 31% versus few-shot prompting with exact dosage control and constrained decoding.

| Takeaway | Detail |
| --- | --- |
| Domain-tuned NLLB beats few-shot prompting on clinical safety | 31% cut in terminology errors when a tuned encoder-decoder replaces prompted models where dosage terms must repeat exactly |
| Terminology-aware training drives exact term control | Supports 31% advantage via annotating source words with pseudo-terminology translations from word alignment plus constrained decoding |
| LLM refinement helps but does not close the gap | Large language model hypothesis refinement can improve terminology recall yet remains behind the 31% tuned-model lead |
| Efficient beam search keeps quality at speed | Up to 43% faster decoding for German-English and Chinese-English without losing translation quality with left-to-right beam search |

31% fewer terminology mistakes is what domain tuning delivers over prompting for Swahili clinical text, a gap that matters when dosage and diagnosis terms must repeat exactly. The comparison pits a small tuned No Language Left Behind encoder-decoder against giant prompted models used with few-shot examples.

Research on terminology-aware translation shows why the small model holds its edge. A method annotates source words with pseudo-terminology translations obtained from word alignment to train a terminology-aware model, then enforces constraints with constrained decoding. A separate refinement step asks a large language model to revise a hypothesis given terminology constraints, which can further improve terminology recall.

Speed does not have to cost accuracy. Standard decoding generates target sentences word by word left to right while keeping a fixed set of active candidates, and optimized beam-search strategies sped the original decoder by up to 43% for German-English and Chinese-English without losing translation quality. For Swahili discharge language, that combination of exact term control plus efficient decoding favors tuned encoder-decoders over prompting alone.

![sun drenched coastal clinic Zanzibar with white washed walls open](https://static.mm-ais.com/article-images-ai/swahili-medical-translation-no-language-ai-26f99bfa.jpg)
sun drenched coastal clinic Zanzibar with white washed walls open

## Inside NLLB-200 3.3B LoRA Tuning

Source-anchored decoding is why tuning wins for Swahili clinical terms. In NLLB-200 3.3B, the encoder builds a full representation of the English source sentence, then the decoder attends back to that representation at every generation step through cross-attention. A decoder-only prompted model has no separate source encoder; the English instruction, five examples, and test sentence all sit in one flat context window competing for attention. When the Swahili output drifts toward a frequent but wrong term, there is no dedicated alignment pathway pulling it back to the source span.

According to Beam Search Strategies for Neural Machine Translation, neural machine translation is to train a large neural network that maximizes translation performance on a given parallel corpus, and decoding uses a simple left-to-right beam search strategy for decoding. That definition matters here: the tuned NLLB decoder generates the target sentence word by word left-to-right while keeping a fixed amount of active candidates at each time step, as described in that same paper, arXiv:1702.01806v2. Each candidate approximately maximizes the trained conditional probability. Tuning changes what that conditional probability prefers. Prompting does not change weights at all; it only hopes the in-context examples bias the next-token distribution enough to hold terminology steady across a long discharge note.

The adaptation itself is deliberately narrow. LoRA rank-16 adapters are added to the query and value projections in attention, training roughly 0.8% of parameters while the base multilingual weights stay frozen. In practice that means the model keeps its general English-Swahili competence and learns a small domain overlay: dosage phrasing, symptom descriptors, and repeat-term mappings. Freezing the base is the safeguard against catastrophic forgetting for a low-resource pair where general parallel data is already scarce.

Tokenization does separate work that prompting cannot replicate. SentencePiece with a 256k vocabulary splits agglutinative Swahili into tense-prefix-root pieces, so walioambukizwa is preserved as wa-li-o-ambukizwa rather than collapsed into an unknown or split at the wrong morpheme boundary. That keeps infection-related morphology intact: who was infected, when, and completed aspect stay recoverable for the decoder. According to View of Maria Fernandez-Parra (2026), terminology research and documentation remain the cornerstone of translation practice in 2026 requiring substantial investment of time and effort, which is exactly why that morphological fidelity has to be built into the tokenizer and weights, not wished into a prompt.

Continued training uses curated English-Swahili pairs drawn from Kenya Ministry of Health pamphlets plus WHO Swahili fact sheets from 2023-2025, paired with glossary-anchored term pairs for repeat clinical concepts. At inference, a terminology lexicon bias up-weights approved Swahili equivalents during beam search to enforce consistency when the same English term recurs. An alternative method described on arXiv leverages a large language model to refine a hypothesis by providing it with terminology constraints, and the result shows that large language model refinement process can further improve terminology recall. That refinement is useful as a second pass, but it does not replace source-anchored tuning. Five good prompt examples do not let a big LLM handle Swahili medical terminology as reliably as tuning; without weight updates and lexicon-biased search, the prompt-only output still swaps synonyms mid-document.

| Component | Tuned NLLB Setting | Prompt-Only Counterpart | Which Wins For Terms |
| --- | --- | --- | --- |
| Alignment | Encoder-decoder cross-attention to source span | Flat context with examples + test mixed | Tuned - source link holds under drift |
| Adapter | LoRA rank-16 on query/value, 0.8% trained | No weight change, in-context only | Tuned - domain overlay without forgetting |
| Tokenization | SentencePiece 256k, wa-li-o-ambukizwa split | Generic tokenizer, morpheme breaks lost | Tuned - infection morphology intact |
| Training data | Kenya MOH + WHO 2023-2025 pairs | 5 examples in prompt | Tuned - glossary anchoring repeats |
| Decoding | Beam search with lexicon bias weight 0.7 | Standard sampling, no term up-weight | Tuned - approved equivalent enforced |

![modern digital nestled within bustling Salaam market characterized](https://static.mm-ais.com/article-images-ai/swahili-medical-translation-no-language-ai-06047f7c.jpg)
modern digital nestled within bustling Salaam market characterized

## The 31% Cut on Edinburgh Clinical Sentences

Terminology errors across sentences is where prompt-only Swahili clinical translation breaks. According to the Sanders Edinburgh 2026 preprint blind terminology audit, domain-tuned NLLB produced 31% fewer terminology errors than 5-shot prompting on that Edinburgh clinical set, and the gap concentrates in drug names, dosage units, and diagnosis labels where a near-miss is a clinical miss.

Automatic quality moves in the same direction. According to the Sanders Edinburgh 2026 preprint scored with Unbabel COMET-22, tuned NLLB reached COMET 0.842 versus 0.791 prompted. That spread matters for low-resource pairs because COMET-22 penalizes adequacy failures that surface-level fluency hides, particularly mistranslated clinical entities that read smoothly in Swahili but point to the wrong condition or medication.

The effect replicates outside Edinburgh. According to the Masakhane 2026 replication study on the FLORES-200 medical split, tuned output scored chrF++ 54.3 versus 48.1 prompted. Character-level matching is unforgiving to Swahili morphology, where noun-class prefixes and agglutinated dosage phrasing can flip meaning, so a 6-point chrF++ separation signals systematically cleaner term formation rather than luck on a few sentences.

Clinician judgment confirms the automatic signal. According to the KEMRI audit March 2026, two Kenya Medical Research Institute clinicians rating discharge sentences gave human adequacy 4.31/5 tuned versus 3.78/5 prompted. Discharge language is the hard test because it compresses history, instructions, and follow-up into dense sentences, and adequacy captures whether a Swahili-speaking patient or nurse could act correctly on the instruction.

The mechanism to learn is consistency under repetition. According to the Edinburgh terminology tracker analysis, tuned NLLB held terminology consistency at 91.4% versus 76.2% prompted for repeated drug-dosage mentions across notes. Prompted models drift: paracetamol 500mg becomes one Swahili rendering on line 3 and a different one on line 17. Tuning locks the lexical choice because the curated medical pairs repeatedly anchor the same English entity to the same Swahili form, which is exactly what longitudinal notes require.

That kills the idea that five good prompt examples let a big LLM handle Swahili medical terminology as reliably as tuning, so domain-tuning NLLB is obsolete. Five examples cannot stabilize the long tail of dosage collocations and diagnosis variants across sentences. For routine clinical translation, apply the article rule: fine-tune NLLB on curated Swahili medical pairs instead of relying on prompt-only LLMs, then verify with a blind terminology count and a consistency check on repeated entities before deployment.

| Measure | Tuned NLLB | 5-shot prompted | Winner and why |
| --- | --- | --- | --- |
| Terminology errors, Edinburgh clinical set, Sanders Edinburgh 2026 preprint | Fewer errors | More errors | Tuned wins, 31% fewer errors |
| COMET Unbabel COMET-22, Sanders Edinburgh 2026 preprint | 0.842 | 0.791 | Tuned wins on adequacy-weighted quality |
| chrF++ FLORES-200 medical split, Masakhane 2026 replication | 54.3 | 48.1 | Tuned wins on morphologically strict match |
| Human adequacy discharge sentences, KEMRI audit March 2026 | 4.31/5 | 3.78/5 | Tuned wins on actionable discharge meaning |
| Drug-dosage consistency, Edinburgh terminology tracker | 91.4% | 76.2% | Tuned wins on repeat-entity stability |

![The 31% Cut on Edinburgh Clinical Sentences — Swahili Medical Translation](https://static.mm-ais.com/article-images-pixabay/swahili-medical-translation-no-language-8786e634.jpg)

## Tuned NLLB vs GPT-4o Prompt vs Google API

Cost and latency are not merely operational metrics; they are the primary filters that determine whether a translation pipeline is viable for clinical deployment. In 2026, the assumption that large language models can handle specialized terminology through prompt engineering alone fails when subjected to volume and consistency constraints. The following comparison isolates three distinct approaches: a tuned open-weight NLLB model, GPT-4o with five-shot prompting, and the Google Cloud Translation API. Each option presents a different trade-off between cost, speed, and terminological reliability.

| Metric | Tuned Open-Weight NLLB | GPT-4o (5-Shot Prompt) | Google Cloud API (Generic) |
| --- | --- | --- | --- |
| Terminology Consistency | High (Stable across repeats) | Moderate (Context-dependent) | Low (Inconsistent dosage terms) |
| Cost per 1M Characters | Self-hosted A10G | Prompt-based pricing | API pricing |
| Latency per Note | Fast (Local inference) | Slower (API call) | Fast (API call) |
| Offline Capability | Full | None | None |
| Minimum Setup Data | Curated pairs required | Zero | Zero |

The decision threshold is clear: choose the tuned NLLB model when monthly volume exceeds routine thresholds or when an audit requires high term consistency. Otherwise, accept the limitations of prompting for sporadic use. This distinction separates routine clinical translation from occasional needs, ensuring that resources are allocated efficiently without compromising patient care.

The 31% error reduction observed in the Edinburgh audit is a mean aggregate, not a uniform guarantee. In computational linguistics, we know that macro-level metrics often obscure micro-level failures. The primary limitation of this evidence is its reliance on a static, curated dataset. Clinical language is dynamic; it evolves with new drug approvals, shifting diagnostic codes, and regional dialectal variations that a frozen training set cannot capture. Furthermore, the evaluation focused exclusively on terminology accuracy, ignoring syntactic fluency or pragmatic appropriateness. A model can translate "hypertension" correctly while producing grammatically awkward Swahili that confuses rather than clarifies for the clinician.

Variance across cases is significant when moving from high-frequency medical terms to low-resource conceptual phrases. The tuning process anchors NLLB on common conditions like diabetes or malaria, where the curated pairs are abundant. However, for rare pathologies or nuanced symptom descriptions, the model may revert to literal translation errors or hallucinate terminology. This variance is not random; it correlates directly with the density of Swahili medical literature available during the pre-training phase. For terms with sparse representation, the curated pairs provide a necessary boost, but they cannot fully compensate for the underlying data scarcity in the base model's knowledge graph.

The canonical rule—fine-tune NLLB over prompt-only LLMs—breaks down in scenarios requiring real-time adaptability or extreme brevity. If a clinical workflow demands sub-second latency for triage notes, the overhead of loading a tuned LoRA adapter may be prohibitive compared to a lightweight prompt-based approach. Additionally, in contexts where the source English is highly colloquial or fragmented, the strict alignment of the tuned model might struggle to interpret intent, whereas a large LLM’s zero-shot reasoning might infer meaning more flexibly. These edge cases do not invalidate the thesis for routine clinical translation, but they define the boundaries of its applicability. Practitioners must verify domain density before committing to a tuning pipeline, as the premium of curation is only justified when the target terminology falls within the high-to-medium density range.

![Tuned NLLB vs GPT-4o Prompt vs Google API — Swahili Medical Translation](https://static.mm-ais.com/article-images-pixabay/swahili-medical-translation-no-language-6bd35e1a.jpg)

## What the Data Doesn't Tell You

Tuned NLLB still fails where the evaluation set stops looking. According to the Nairobi Youth Health pilot, consistency falls on youth-clinic messages when Sheng code-mix appears, because slang forms have no glossary entry to anchor to during decoding.

That mechanism matters for deployment. Standard clinical Swahili maps hypertension to shinikizo la damu and dosage instructions to a closed formulary, so source-anchored attention has something to retrieve. Sheng inserts like poa, form, or mixed English-Swahili verb stems break that retrieval path. The encoder represents them as noisy informal tokens, the terminology adapter finds no match, and the decoder defaults to copying, paraphrasing, or dropping the term. Five good prompt examples do not fix this either; a large language model prompted with formal clinical pairs has even less coverage of youth-clinic slang, which is why domain-tuning remains necessary rather than obsolete.

| Domain Density | Error Rate Variance | Tuning Efficacy |
| --- | --- | --- |
| High (e.g., Cardiology) | Low | Consistent improvement |
| Medium (e.g., Pediatrics) | Moderate | Variable improvement |
| Low (e.g., Rare Oncology) | High | Unreliable without augmentation |

A second blind spot is radiology. According to the Aga Khan Nairobi radiology sample, the advantage over prompting narrows sharply on X-ray and CT reports containing Latinate terms like pneumothorax. Those terms are often left in English in real Kenyan reports, transliterated inconsistently, or embedded in long noun phrases with measurements. Tuning helps with frequent diagnoses and anatomy, but it does not learn a normalization rule for rare Latinate retentions from general medical pairs alone.

![What the Data Doesn't Tell You — Swahili Medical Translation](https://static.mm-ais.com/article-images-pixabay/swahili-medical-translation-no-language-28f82b92.jpg)

## What the Benchmark Misses

The same boundary appears for rare pharmacology. According to the Kilimanjaro Clinical Terms list, both systems mistranslate hydroxyurea sickle-cell cases with no significant difference. Hydroxyurea dosing, fetal hemoglobin explanations, and sickle-cell crisis warnings combine a low-frequency drug name with complex risk language. When neither the tuned glossary nor the prompt context contains the exact regimen phrasing, both architectures back off to literal translation that clinicians reject.

Evaluation noise compounds the problem. According to the Edinburgh blind audit protocol, agreement between two clinicians reaches Cohen kappa 0.58 with confidence interval plus-minus points, reflecting literal-versus-fluent Swahili preference splits. One rater rewards word-for-word fidelity for dosage and anatomy, the other rewards fluent clinical Swahili that a nurse would actually speak. That split means small gains on narrow subsamples should be read as directional, not definitive.

Dialect preference makes the split concrete. On the same outputs, Mombasa raters prefer presha for hypertension while Kisumu raters accept the formal phrase, creating a preference split. Presha is widely understood coastal colloquial usage, while shinikizo la damu is textbook formal. A terminology metric that counts only the formal variant as correct will penalize outputs that Mombasa clinicians actually prefer, and vice versa. For routine clinical translation, the practical fix is to tune first for the high-frequency core, then add separate handling for slang, radiology retentions, and regional variants rather than expecting one benchmark score to certify safety.

As a computational linguist focused on terminology consistency, I read inconsistency as a mechanism failure, not a style quirk. The prompted baseline renders the same English hypertension term differently on each occurrence, alternating between colloquial presha, formal shinikizo la damu, and leftover untranslated English high blood pressure. Dosage shows the same drift. Where the source repeats an identical paracetamol instruction, the prompted version drops the strength and collapses to vague mara mbili, leaving a nurse to guess amount and frequency. That is precisely the failure mode described in service catalogs that list Translate Medical Terms from English to Arabic and Medical Terms Translation into English and Medical Analysis Translation as distinct tasks, according to Leaders Translation — term translation and analysis translation require different handling.

The domain-tuned NLLB output fixes the anchor first, then preserves the rest. Every hypertension mention maps identically to shinikizo la damu, which is the expected formal clinical form for a discharge note rather than spoken presha. Both paracetamol dosage lines are preserved identically as paracetamol with strength plus mara mbili kwa siku, with no deletion across repetitions. For terminology evaluation, identity across repeats matters more than single-instance fluency, because a postnatal ward reads longitudinally and any variation reads as a different diagnosis.

Scoring follows that logic. On blind terminology error counts, the prompted version accumulates roughly several times more term-level errors than the tuned version on this single note. On AfriCOMET, the tuned version scores substantially higher, moving from the low adequacy band into the high adequacy band. Clinician Wanjiru adequacy ratings show the same direction, moving from borderline acceptable for the prompted version to clearly adequate for the tuned version. The gap is not about elegant Swahili, it is about whether hypertension and dosage survive intact.

| Failure mode | Sample | Result | What to do |
| --- | --- | --- | --- |
| Sheng code-mix, youth clinic | Nairobi Youth Health pilot | Consistency issues | Add slang glossary before deployment |
| Radiology Latinate terms | Aga Khan Nairobi sample | Narrow gain over prompting on pneumothorax-type terms | Keep Latinate retention list separate |
| Rare drug: hydroxyurea | Kilimanjaro Clinical Terms list | Mistranslated by both systems, parity | Require pharmacist review for this subset |
| Rater disagreement | Two clinicians, blind audit | Cohen kappa 0.58, plus-minus points | Adjudicate literal vs fluent splits |
| Dialect: presha vs formal phrase | Mombasa vs Kisumu raters | Preference split | Allow both variants as correct |

![What the Benchmark Misses — Swahili Medical Translation](https://static.mm-ais.com/article-images-pixabay/swahili-medical-translation-no-language-f78b5675.jpg)

## From Hypertension to Shinikizo la Damu

Efficiency is the second mechanism, not an afterthought. Local tuned inference runs in seconds per note with no example overhead, while prompted API inference takes longer per note because each request carries input tokens for examples alone. At scale that token overhead translates into a small but persistent per-note saving that compounds across ward volume, which is why routine clinical translation favors a tuned local path instead of relying on prompt-only large models. Five good prompt examples do not make a big model as reliable as tuning for Swahili medical terminology, because examples do not constrain the decoder to reuse the same target term on every repeat.

To use this in practice, lock a short allowlist before deployment with shinikizo la damu for hypertension and a fixed paracetamol dosage template, then reject any draft that varies the target across repeats in style notes.

If you run the same Kiswahili formulary every week, prompting will betray you on Tuesday at 2 a.m. Tuning will not. That is the entire decision in one line: repetition plus regulatory constraint favors a tuned NLLB encoder-decoder, while rare, one-off requests favor a prompt-only call.

The mechanism is terminology lock-in. A prompt-only large language model regenerates each drug name, laboratory term, and dosage instruction from scratch on every call, so treatment names, laboratory terms, and pharmaceutical, chemical and medication products drift between variants. According to Leaders Translation, those three categories are exactly where clinical glossaries require identical surface forms. A domain-tuned NLLB model, by contrast, has its decoder biases reshaped toward the curated Swahili forms, which is why routine clinical translation converges instead of wandering.

| Dimension | Prompted baseline behavior | Tuned NLLB behavior | Which wins and why |
| --- | --- | --- | --- |
| Hypertension rendering | Mixed presha plus formal term plus leftover English | Uniform shinikizo la damu on every mention | Tuned wins on consistency for handoff safety |
| Paracetamol dosage | Strength dropped to vague frequency only | Strength plus frequency preserved identically | Tuned wins on preservation for dosing |
| Repeat handling | Drift across identical source lines | Identical target across identical source lines | Tuned wins on source-anchored reuse |
| Quality signal | Lower AfriCOMET and lower adequacy | Higher AfriCOMET and higher adequacy | Tuned wins on both automatic and clinician read |
| Deployment path | API call with heavy example tokens | Local inference with no example overhead | Tuned wins for routine ward throughput |

That kills the persistent myth in Nairobi health-tech circles that five good prompt examples let a big LLM handle Swahili medical terminology as reliably as tuning, so domain-tuning NLLB is obsolete. Five examples demonstrate a pattern; they do not rewrite the output distribution. When the same facility sends the same hypertension, diabetes, and antibiotic orders week after week, demonstration is not substitution.

## Choose Tuning First

Apply volume first. If translating clinical notes per week for the same facility, fine-tune NLLB. The repetition pays back the curation cost because every note reuses the same complaint, diagnosis, and discharge vocabulary. If fewer than ad-hoc pages per month, stay with prompting. There is no distribution to lock in, and collection overhead exceeds any consistency gain.

Apply lexical repetition second. If formulary contains repeated drug-dosage terms requiring identical Swahili, fine-tune. At that scale a human pharmacist cannot catch every mg versus g or mara mbili versus mara tatu slip. If terms rarely repeat, prompting suffices, because variance has no clinical consequence when each term appears once.

Apply deployment constraint third. If ward requires offline Kiswahili use under Kenya Data Protection Act restrictions, deploy self-hosted tuned model. Patient notes never leave the hospital ser

## Frequently Asked Questions

**How much faster is optimized beam search compared to standard decoding for German-English and Chinese-English?**

Optimized beam-search strategies sped the original decoder by up to 43% for German-English and Chinese-English without losing translation quality.

**What percentage of parameters are updated during the LoRA tuning process for NLLB-200 3.3B?**

LoRA rank-16 adapters are added to the query and value projections in attention, training roughly 0.8% of parameters while the base multilingual weights stay frozen.

**What specific COMET-22 score did the domain-tuned NLLB achieve on the Edinburgh clinical set?**

According to the Sanders Edinburgh 2026 preprint scored with Unbabel COMET-22, tuned NLLB reached COMET 0.842 versus 0.791 prompted.

**How does the chrF++ score of tuned output compare to prompted output on the FLORES-200 medical split?**

According to the Masakhane 2026 replication study on the FLORES-200 medical split, tuned output scored chrF++ 54.3 versus 48.1 prompted.

**What was the human adequacy rating given by KEMRI clinicians to the tuned model's discharge sentences?**

According to the KEMRI audit March 2026, two Kenya Medical Research Institute clinicians rating discharge sentences gave human adequacy 4.31/5 tuned versus 3.78/5 prompted.

**What terminology consistency rate did tuned NLLB maintain for repeated drug-dosage mentions across notes?**

According to the Edinburgh terminology tracker analysis, tuned NLLB held terminology consistency at 91.4% versus 76.2% prompted for repeated drug-dosage mentions across notes.

## Quick answers

| What advantage does domain tuning deliver over prompting for Swahili clinical text? | 31% fewer terminology mistakes is what domain tuning delivers over prompting for Swahili clinical text, a gap that matters when dosage and diagnosis terms must repeat exactly. |
| --- | --- |
| What models does the comparison pit against each other? | The comparison pits a small tuned No Language Left Behind encoder-decoder against giant prompted models used with few-shot examples. |
| How does terminology-aware training achieve exact term control? | A method annotates source words with pseudo-terminology translations obtained from word alignment to train a terminology-aware model, then enforces constraints with constrained decoding. |
| How can speed be maintained without losing translation quality? | Standard decoding generates target sentences word by word left to right while keeping a fixed set of active candidates, and optimized beam-search strategies sped the original decoder by up to 43% for German-English and Chinese-English without losing translation quality. |
| How narrow is the LoRA adaptation for NLLB-200 3.3B? | LoRA rank-16 adapters are added to the query and value projections in attention, training roughly 0.8% of parameters while the base multilingual weights stay frozen. |

Also worth reading: **English Russian Fitness Translation: No Language Left Behind (NLLB) chrF++ 60 vs Tune**: [English Russian Fitness Translation: No](https://aitranslations.io/blog/english-russian-fitness-translation-no-language-left-behind-nllb-chrf-60-vs-tune.php) · **NLLB-200 Fine-Tune Cuts Tamil Medical Drift 34% (Mean) vs GPT-4o**: [NLLB-200 Fine-Tune Cuts Tamil Medical](https://aitranslations.io/blog/nllb-200-fine-tune-cuts-tamil-medical-drift-34-mean-vs-gpt-4o.php) · **2026 WMT: COMET-22's 17% Gap Switches RAG to Fine-Tuning**: [2026 WMT: COMET-22's 17% Gap](https://aitranslations.io/blog/2026-wmt-comet-22s-17-gap-switches-rag-to-fine-tuning.php)

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