---
language:
- sw
license: apache-2.0
library_name: transformers
tags:
- swahili
- kiswahili
- causal-lm
- base-model
- pretrained
- african-languages
- low-resource
datasets:
- HuggingFaceFW/fineweb-2
pipeline_tag: text-generation
---

# KW5-Lite Base

A 109.5M-parameter Swahili (Kiswahili) language model pretrained from scratch on
1.41B tokens, on a single NVIDIA T4.

Built by [Regnant](https://www.regnant.io/).

**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](https://huggingface.co/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](https://huggingface.co/datasets/HuggingFaceFW/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](https://huggingface.co/regnant-io/kw5-lite-instruct)
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.