language, license, base_model, tags, datasets, pipeline_tag

language	license	base_model	tags	datasets	pipeline_tag
en	apache-2.0	meta-llama/Llama-3.2-3B-Instruct	unsloth text-generation-inference conversational gguf llama	KiwiMate/KiwiMate-Mini-training	text-generation

KiwiMate-Mini-Preview

KiwiMate-Mini-Preview is a lightweight, New Zealand–flavoured conversational language model, fine-tuned for the KiwiMate AI companion app. It is the smallest model in the KiwiMate model family and is designed for fast, low-cost inference on the app's free and lower-tier subscription plans.

⚠️ Preview status: This is a prototype release. The name reflects its preview status — expect breaking changes, retraining, and behavioural shifts before a stable v1 release.

Model Details

Developed by	KiwiMate / KyleCodeKiwi
Base model	Llama 3.2 3B Instruct
Architecture	Llama
Parameters	~3.21B
Fine-tuning framework	Unsloth
License	Apache 2.0
Languages	English (with New Zealand English and Te Reo Māori vocabulary coverage)
Model class	AutoModelForCausalLM

Intended Use

KiwiMate-Mini-Preview is intended as the default conversational backend for the KiwiMate app, providing:

• General-purpose chat and assistant-style conversation
• New Zealand cultural and "Kiwi" context awareness (slang, geography, fun facts)
• Light Te Reo Māori vocabulary recognition and use
• Lore and knowledge specific to the KiwiMate app itself ("KiwiMate Origin" data)
• Lightweight knowledge support for in-app mini-games

It is not intended for high-stakes, medical, legal, or financial advice, and should not be relied on as an authoritative source on Māori language or tikanga — for genuinely sensitive Te Reo or cultural content, defer to community-governed resources.

Training Data

Fine-tuned on the KiwiMate/KiwiMate-Mini-training dataset, organised into categories including:

• NZ English
• Te Reo Māori
• KiwiMate Origin (app-specific lore/identity)
• NZ Fun Facts
• MiniGame Knowledge

Files & Quantizations

Distributed as safetensors (full precision) and GGUF quantizations for efficient local/edge inference:

Format	Use case
F16	Highest fidelity, largest size
Q6_K	Near-lossless, smaller footprint
Q4_K_M	Balanced quality/size — recommended default for on-device use
Q2_K_L	Smallest footprint, lowest fidelity

Deployment

Served in production via a Hugging Face Inference Endpoint on a T4 GPU with scale-to-zero, fronted by a Supabase Edge Function (OpenAI-compatible proxy) that routes KiwiMate app traffic to this and other KiwiMate model endpoints behind a single API.

Known Limitations

• A server-side mitigation is in place for an occasional role-bleed / over-generation issue (the model sometimes continuing past <|eot_id|>), handled via stop-sequence aliases and trimming at the proxy layer.
• The long-term fix — adding <|eot_id|> (token ID 128009) properly to the training loss and generation_config.json — is planned for a future retraining pass rather than this preview.
• As a 3B-parameter model, reasoning depth and factual reliability are limited compared to larger models; it is tuned for speed and personality over raw capability.

License

Released under the Apache 2.0 license, consistent with the open weights commitment for the KiwiMate model family.