This is a base model. It does next-token prediction only. It does not follow instructions and has no chat template. For that, use regnant-io/kw5-lite-instruct.

Quick start

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("regnant-io/kw5-lite-base")
tok = AutoTokenizer.from_pretrained("regnant-io/kw5-lite-base")

# Base model: give it a prefix to continue, not an instruction.
# Write <s> into the text — the tokenizer maps it to id 1.
inputs = tok("<s>Tanzania ni nchi", return_tensors="pt", add_special_tokens=False)

out = model.generate(**inputs, max_new_tokens=60)
print(tok.decode(out[0], skip_special_tokens=True))

Tanzania ni nchi ya amani na utulivu. Ni nchi yenye watu wengi, wenye nguvu
za kiuchumi, wenye uwezo wa kufanya maamuzi magumu kwa wakati mmoja...

add_bos_token is pinned to false so that the same tokenizer settings work for the instruct model, which renders <s> as part of its prompt template. Put <s> at the start of your text yourself — every training sequence began with it.

Write it into the string, as above, rather than concatenating the id onto input_ids afterwards: that leaves attention_mask one element shorter than input_ids, and generate then fails inside RoPE with The size of tensor a (4) must match the size of tensor b (3).

Like the instruct model, this one needs a low temperature and a repetition penalty to stay coherent; the shipped generation_config.json defaults to temperature 0.2 / repetition_penalty 1.3.

Model details


Parameters	109.5M (tied input/output embeddings)
Architecture	Llama-compatible decoder-only transformer
Layers / hidden / FFN	12 / 768 / 2048
Attention heads	12 query, 12 key-value — standard multi-head attention, not GQA
Normalization	RMSNorm, pre-norm
Activation	SwiGLU
Position encoding	RoPE, theta 10000
Context length	2048
Vocabulary	32,000 SentencePiece BPE
Precision	FP32 (trained under FP16 autocast with an FP32 master copy)
File size	438 MB

A previous revision of this card claimed "KV heads: 4 (Grouped Query Attention)". That was wrong — num_key_value_heads is 12 and always has been. This model uses standard MHA. Corrected here.

Tokenizer

32k BPE trained from scratch on the deduplicated Swahili corpus. NFC normalization applied before training (SentencePiece's own rules are all NFKC-family, which is lossier), byte_fallback so no input can hard-fail, and digits split.

Special tokens are fixed at low ids: <unk>=0, <s>=1, </s>=2, <pad>=3.

Ids 4–6 are <|system|>, <|user|>, <|assistant|>, reserved but never trained — they do not occur in the pretraining corpus, so their embedding rows are still at initialisation. If you fine-tune and want to use them as chat-role markers, you must unfreeze embed_tokens (or add it to your LoRA modules_to_save), or the model will read them as noise.

Training


Data	FineWeb-2 `swh_Latn`, cleaned and deduplicated (exact + MinHash LSH)
Tokens seen	1.41B — exactly 2 epochs over the corpus
Checkpoint	step 6,150
Context length	1024 during pretraining, extended to 2048 via NTK-aware RoPE rescaling
Precision	FP16 mixed precision with GradScaler (T4/Turing has no bf16 tensor cores)
Optimizer	8-bit AdamW (bitsandbytes), lr 3e-4, weight decay 0.1, grad clip 1.0
Schedule	Warmup-Stable-Decay, stopped at the end of the stable phase
Hardware	1× NVIDIA T4 (16 GB), ~60 GPU-hours across resumable 5-hour sessions

On the schedule: WSD normally ends with an LR decay phase. Training was stopped at step 6,150 — exactly two epochs — because validation quality began degrading past that point, so the decay was deliberately not run. Released checkpoints from later in the run overfit.

Evaluation

Task	Metric	KW5-Lite Base	Chance
Belebele-sw (4-way)	Accuracy	32%	25%
AfriXNLI-sw (3-way)	Accuracy	32%	33%

n = 50 per task.

Interpret these honestly: this model is at or near chance on both. With n=50 the standard error is about ±6.6 points, so 32% vs 25% on Belebele is not a reliable signal, and AfriXNLI is exactly at chance. A 110M model trained on 1.41B tokens is not expected to do multiple-choice reasoning; these numbers establish a floor, not a capability.

What the model is genuinely good at is fluent, well-formed Swahili continuation. That is what makes it a useful base to fine-tune, and it is what the instruct model builds on.

Intended use

Intended as a starting point for Swahili fine-tuning — instruction tuning, domain adaptation, classification heads — where training from scratch is too expensive and larger multilingual models are too big to serve.

Not intended for direct deployment: no instruction following, no safety tuning, no factual reliability.

Limitations

Not an instruction model. It continues text; it does not answer questions.
Factual reliability is poor. Do not use as a knowledge source.
Primarily Tanzanian Swahili, reflecting the corpus distribution.
No safety tuning at all. It will reproduce harmful, biased or explicit content present in web text.
Trained on web-scraped data and carries its biases and quality artifacts.

Files

File	What it is
`model.safetensors`	FP32 weights, tied embeddings, 438 MB
`tokenizer.model`	SentencePiece 32k BPE model
`tokenizer_config.json`	`add_bos_token=false` — prepend `<s>` yourself
`generation_config.json`	Low-temperature defaults that keep this model coherent
`training_info.json`	Checkpoint step, tokens seen, benchmark scores

A previous revision also shipped pytorch_model.bin (a stale duplicate of the weights), duplicate tokenizer files under two names, and a config.json whose pad_token_id was 0 while the tokenizer's <pad> is 3. All removed or corrected.

Citation

bibtex

@misc{kw5lite2026,
  title  = {KW5-Lite: a 110M-parameter Swahili language model trained on a single T4},
  author = {Regnant},
  year   = {2026},
  url    = {https://huggingface.co/regnant-io/kw5-lite-base}
}

Apache 2.0.