hypaai

Hypa-SmolLM-135M-Instruct-16bit

README

License: apache-2.0

Which build do I want?

Table with columns: Repo, Format, Size, Use it when
Repo	Format	Size	Use it when
`-GGUF`	GGUF Q4_K_M	105 MB	On-device, llama.cpp, Ollama, LM Studio, mobile
→ `-16bit` (this one)	BF16 safetensors	0.1B params	Transformers or vLLM; converting or quantising yourself
`-LoRAs`	PEFT adapter	—	Merging onto your own base, or continued training

What it does

Five tasks, each selected by its system prompt:

Table with columns: Task, System prompt, Behaviour
Task	System prompt	Behaviour
Next-word prediction	`You are Hypa Keyboard. Predict the next word.`	Text ends at a word boundary → emit the next word
Word completion	`You are Hypa Keyboard. Complete the current word.`	Text ends mid-word → finish the word being typed
Last-word correction	`You are Hypa Keyboard. Correct the last word.`	Fix only the final, just-typed token
Block correction	`You are Hypa Keyboard. Correct the text block.`

Correction is trained against a controlled corruption vocabulary that treats tone-mark damage as a first-class error type. Losing diacritics is the single most common failure when typing tonal orthographies on a standard mobile keyboard, so restoring them is a core capability rather than an afterthought.

Usage

python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "hypaai/Hypa-SmolLM-135M-Instruct-16bit"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")


def keyboard(system_prompt, text, max_new_tokens=8):
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user",   "content": text},
    ]
    inputs = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt",
    ).to(model.device)

    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
    return tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True
    )


# Next-word prediction
keyboard("You are Hypa Keyboard. Predict the next word.", "Ndewo, kedu ka ị")

# Word completion
keyboard("You are Hypa Keyboard. Complete the current word.", "Ẹ káàbọ̀ sí ilé ìwé wa, a")

# Fix the last word
keyboard("You are Hypa Keyboard. Correct the last word.", "I dey go markit")

# Clean the whole span
keyboard("You are Hypa Keyboard. Correct the text block.",
         "Omi Omi kp anya'ami mail ekubo uche r'abo ohigbeli mi.", max_new_tokens=64)

Serving with vLLM

bash
vllm serve hypaai/Hypa-SmolLM-135M-Instruct-16bit

Settings that matter

Use greedy decoding (do_sample=False). Sampling makes keyboard suggestions feel erratic — users experience variance as the keyboard being broken, not creative.

Keep max_new_tokens low: 4–8 for prediction and completion, 32–64 for block and grammar correction. On a 135M model, longer generations drift.

Converting to GGUF yourself

bash
python llama.cpp/convert_hf_to_gguf.py \
    hypaai/Hypa-SmolLM-135M-Instruct-16bit \
    --outfile hypa-keys-135m-f16.gguf --outtype f16

./llama-quantize hypa-keys-135m-f16.gguf hypa-keys-135m-Q8_0.gguf Q8_0

Training

Table

Base model	`unsloth/smollm-135m-instruct-bnb-4bit` (SmolLM-135M-Instruct)
Method	LoRA (PEFT) via TRL `SFTTrainer`, accelerated with Unsloth, then merged to BF16
Dataset	`hypaai/Hypa-Keyboard-v2` — 409,598 examples
Languages	27
Precision	BF16
LoRA rank / alpha

Framework versions

TRL 1.9.2
Transformers 5.13.1
PyTorch 2.11.0+cu128
Datasets 3.6.0
Tokenizers 0.22.2

Evaluation

Not yet published.

Limitations

No per-language evaluation. The training dataset has no language column, so quality across the 27 languages is unmeasured and certainly uneven. Expect better results in Hausa, Igbo, Yorùbá and Swahili than in Eggon, Igede, Ebira or Nupe.
Synthetic training noise. Corruptions were programmatically injected. The model has not seen real keyboard-layout adjacency errors (fat-finger typos), swipe-typing failures, or genuine mid-sentence code-switching — all of which dominate actual mobile input.
Close-relative confusion. Efik, Ibibio and Annang share substantial vocabulary and orthography. A correction valid in one may be applied to text written in another.
Trained through a quantised base. The LoRA was trained against a 4-bit bnb checkpoint and then merged to BF16, so these weights are not identical to what full-precision training would have produced.
Source-formatting leakage. Some training spans carried Markdown, prompt fragments and JSON punctuation, so the model occasionally treats prompt-like text as ordinary typing.
Not a chat model. Despite the instruct base, this is trained for five narrow keyboard tasks. Conversation, question answering and translation are out of scope and will produce poor output.

Intended use

For: serving and evaluating the keyboard model at full precision; as the conversion source for on-device builds; research on low-resource keyboard modelling.

Not for: general text generation, translation, question answering, or any setting where output is treated as authoritative text in these languages. Corrections must be shown as suggestions the user can reject, never applied silently — a wrong autocorrect in a language the user speaks and the model barely knows is worse than no autocorrect at all.

Citation

bibtex
@misc{hypaai2026hypakeys,
  title        = {Hypa SmolLM 135M: A Compact Multilingual Keyboard Model for African Languages},
  author       = {Hypa Intelligence},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/hypaai/Hypa-SmolLM-135M-Instruct-16bit}}
}

License

Apache 2.0, inherited from SmolLM-135M-Instruct.

Contact

Hypa Intelligence • Website • Hugging Face • GitHub • Blog

Trained with Unsloth and Hugging Face TRL.

Available on FriendliAI

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