6 Downloads Updated 1 month ago
ollama run KiwiMate/KiwiMate-Mini-1.0:Q4_K_M
Updated 1 month ago
1 month ago
5169cdf10dad · 3.3GB
license: other license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE base_model: Qwen/Qwen2.5-VL-3B-Instruct language: - en library_name: llama.cpp pipeline_tag: image-text-to-text tags: - gguf - qwen2_5_vl - llama.cpp - unsloth - vision-language-model
KiwiMate Mini is the lightweight, on-device model tier for KiwiMate, an AI companion app for iOS and Android with a New Zealand cultural identity. Mini 1.0 is fine-tuned from Qwen2.5-VL-3B-Instruct, replacing the previous Llama 3.2 3B base to add image input support alongside text chat.
This model is intended for fast, low-resource inference — locally on-device or via lightweight self-hosted endpoints — while KiwiMate Small (Qwen3.5-9B) handles heavier multimodal tasks server-side.
⚠️ Preview build. This is an early release of the Mini 1.0 line for internal testing ahead of KiwiMate app integration. Expect rough edges, especially around vision grounding and long conversations.
qwen2vl (Qwen2.5-VL) instead of llama.<|eot_id|> style to Qwen’s ChatML-style <|im_start|> / <|im_end|> tokens — update any client or server-side stop-sequence config accordingly if you’re migrating from Mini Preview.| File | Size | Description |
|---|---|---|
KiwiMate-Mini-1.0-instruct.F16.gguf |
6.18 GB | Full-precision weights, highest quality, largest footprint |
KiwiMate-Mini-1.0-instruct.Q6_K.gguf |
2.54 GB | Near-lossless quantization, good balance of quality/size |
KiwiMate-Mini-1.0-instruct.Q4_K_M.gguf |
1.93 GB | Smallest, fastest, recommended for constrained/on-device use |
KiwiMate-Mini-1.0.BF16-mmproj.gguf |
1.34 GB | Vision projector — required alongside a text GGUF above for image input |
Modelfile |
— | Ollama Modelfile for local serving |
config.json |
— | Model config (Unsloth training output) |
As with the previous Mini line, expect some quality drop at Q4_K_M relative to F16/Q6_K — worth spot-checking once you’ve run your usual eval pass, since this is a different base architecture and the tradeoff may not track identically to the Llama 3.2 quants.
Vision input requires loading both the text model and the mmproj file:
llama-mtmd-cli \
-m KiwiMate-Mini-1.0-instruct.Q4_K_M.gguf \
--mmproj KiwiMate-Mini-1.0.BF16-mmproj.gguf \
-p "Describe this image."
ollama create kiwimate-mini -f Modelfile
ollama run kiwimate-mini
Qwen ChatML format:
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
If you’re serving this behind a proxy (e.g. runpod-chat/index.ts), make sure stop sequences are updated to <|im_end|> rather than the old Llama <|eot_id|> — the two are not interchangeable.
FastVisionModel)Conversational AI companion for the KiwiMate app — casual chat, light image understanding (e.g. describing a photo, reading a screenshot), and NZ-flavoured personality. Not intended for high-stakes reasoning, factual research, or professional/medical/legal advice.
⚠️ Please read before redistributing. This model is derived from Qwen2.5-VL-3B-Instruct, which Qwen distributes under a Qwen Research license, not Apache 2.0 — this differs from KiwiMate’s other model tiers. Review the license terms — research licenses commonly restrict commercial deployment — before treating this build the same as the Apache-2.0 Mini Preview / Small tiers, or before making this repo public.