PrinceAlhassanNasamu/tekyerema-1-native-reply

Tɛkyerɛma-1 reply adapter (arm ②)

Author: Prince Nasamu Alhassan

Overview

The reply adapter for arm ②, trained on target-language text directly so no translator runs at inference.

A warning from how this was built. The first attempt produced reply adapters for the two arms that were identical to 17 decimal places, because the training script only applied its TEXT_COL switch inside the tool branch. Two runs, two repos, one model. If you are comparing these adapters, check they differ before believing any difference you measure.

Use it

A LoRA adapter: load the base, then apply it. Take the tokenizer from the base, not from this repo — the adapter's saved tokenizer carries a chat template that silently ignores enable_thinking, and without that flag Qwen3 opens a reasoning block and never reaches the JSON.

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE = "Qwen/Qwen3-4B"
tok  = AutoTokenizer.from_pretrained(BASE)         # the BASE, deliberately
model = PeftModel.from_pretrained(
    AutoModelForCausalLM.from_pretrained(
        BASE, dtype=torch.bfloat16, device_map="auto"),
    "PrinceAlhassanNasamu/tekyerema-1-native-reply").eval()

prompt = tok.apply_chat_template(
    [{"role": "user", "content": TOOL_PROMPT}],    # schema + user command
    add_generation_prompt=True, tokenize=False,
    enable_thinking=False)                         # not optional
enc = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**enc, max_new_tokens=96, do_sample=False)
print(tok.decode(out[0][enc["input_ids"].shape[1]:],
                 skip_special_tokens=True))

TOOL_PROMPT must carry the full 21-tool schema exactly as training did — see H200/sessions/s3_train_tekyerema1.py. Asked without it, the model has no tool names to choose from and scores zero.

Training data

Trained on the Ghana Speech dataset and related Ghanaian corpora, licensed CC BY-NC 4.0.

Intended use & license

Non-commercial use only (CC BY-NC 4.0). This is inherited from the training data and required by the terms under which the compute was granted: models trained in that window are non-commercial by condition of access, not by inference.

Limitations, stated plainly

• Dagbani had no recogniser of its own for this whole project, and the reason given for that was wrong. Every card here said "one fine-tuning session on 74 validation rows would not change that". Those 74 rows are the eng-dag machine-translation validation split. The Dagbani speech data in this same account is waxal_dag: 13,228 training rows, 1,750 validation rows, ~71 hours, 1,041 speakers with the largest at 1% — more data and better speaker diversity than Ewe, which produced a working 42.19 WER recogniser. A number was carried across from a translation table into a speech claim, and then repeated on every model card on the account. It is training now, on 2026-08-31. Until it is scored, the honest statement is that Dagbani's best available recogniser scores 86.6 WER and nobody had tried fine-tuning on the data already in hand.
• Evaluation is on read and machine-translated text. No recordings of people speaking agent commands in these languages exist. Numbers measured this way are optimistic about phrasing and pessimistic about code-switching, and should not be read as field performance.
• Research work from a hackathon entry, not a supported product.

The rest of the family

Recognisers

• whisper-large-v3-turbo-tekyerema-eng-foundation — Ghanaian English ASR — course 1 (foundation)
• kusaal-whisper-small-lora — Kusaal ASR (Whisper-small LoRA, superseded)
• kasa42-asr — KASA-42 (Kusaal, third-party export)
• tekyerema-asr-ctc — Twi ASR (w2v-BERT CTC)
• tekyerema-asr-mms-ewe — Ewe ASR (MMS adapter)
• tekyerema-asr-mms-dag — Dagbani ASR (MMS adapter)
• tekyerema-asr-mms-hau — Hausa ASR (MMS adapter)
• tekyerema-asr-mms-kus — Kusaal ASR (MMS adapter)
• whisper-large-v3-turbo-tekyerema-eng — Ghanaian English ASR (Whisper large-v3-turbo)

Voices

• tekyerema-tts-twi — Twi TTS (VITS)
• tekyerema-tts-kus — Kusaal TTS (VITS)
• tekyerema-tts-ewe — Ewe TTS (VITS)
• tekyerema-tts-hau — Hausa TTS (VITS)
• tekyerema-tts-eng — Ghanaian English TTS (VITS)

Agent models

• tekyerema-1-reply — Tɛkyerɛma-1 reply adapter (arm ①)
• tekyerema-1-native-reply — Tɛkyerɛma-1 reply adapter (arm ②)
• tekyerema-1-tool — Tɛkyerɛma-1 tool adapter (arm 1)
• tekyerema-audio-native — Tɛkyerɛma-1 audio-native (arm 3)
• tekyerema-audio-native-4k — Tɛkyerɛma-1 audio-native, 4,000 clips (arm 3 v2)
• tekyerema-1-native-tool — Tɛkyerɛma-1 tool adapter (arm 2)

Translation

• tekyerema-nllb600m-v1 — Tɛkyerɛma MT v1 (NLLB-600M)
• kusaal-nllb-600M — Kusaal MT specialist (NLLB-600M)

Routing

• tekyerema-intent-afroxlmr — Intent classifier (AfroXLMR)

Acknowledgements

Compute resources provided by AI Skills and Compute Africa (AISCA). Trained on the Ghana NLP H200 GPU. Please keep derivatives non-commercial and share improvements back with the Ghana NLP community (ghananlpcommunity).