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RitwikSehrawat/
gemma-3-1b-kbd-q4km

Name: gemma-3-1b-kbd-q4km
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Gemma 3 1B — Context-Aware Keyboard (Q4_K_M GGUF)

A fine-tuned Gemma 3 1B (INT4) for context-aware text prediction on Android, quantized to GGUF Q4_K_M for on-device inference via llama.cpp.

Model Details

Base model: google/gemma-3-1b-pt
Fine-tuning: LoRA via Unsloth on a single RTX 3060
Quantization: Q4_K_M via llama.cpp convert_hf_to_gguf.py
Target device: Android (HeliBoard keyboard fork)

Training Data

~3B token conversational corpus from five sources with per-source cleaning, thread reconstruction, and synthetic typo augmentation:

Reddit conversations
OpenSubtitles
Enron email corpus
Hacker News comments
MS MARCO queries

Prompt Format

The model uses custom special tokens to condition predictions on app context:

<|app|>messaging<|ctx|>
[on-screen conversation history]
<|cursor|>

<|app|> — app category (messaging, email, search, social, etc.)
<|ctx|> — marks start of on-screen conversational context
<|cursor|> — marks the cursor position; model predicts the next tokens

Evaluation

Metric	Gemma (LLM)	N-gram baseline
MRR (top-5)	0.3772	0.0509
Hit@5	52.4%	9.9%
Win rate	92.2%	4.5%

Evaluated on 1,865 examples.

Usage

Designed for integration with HeliBoard via llama.cpp's Android NDK build. Load the GGUF, feed the structured prompt, and use top-k token predictions as keyboard suggestions.

Limitations

English-only
Trained on informal/conversational text; may underperform on formal or technical writing
~800MB model size requires download on first app launch

RitwikSehrawat/gemma-3-1b-kbd-q4km