3.5 KiB
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 |
|
|
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 andgeneration_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.