Instructions to use KiwiMate/KiwiMate-Mini-1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use KiwiMate/KiwiMate-Mini-1.1 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf KiwiMate/KiwiMate-Mini-1.1:Q4_K_M # Run inference directly in the terminal: llama cli -hf KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf KiwiMate/KiwiMate-Mini-1.1:Q4_K_M # Run inference directly in the terminal: llama cli -hf KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf KiwiMate/KiwiMate-Mini-1.1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
Use Docker
docker model run hf.co/KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use KiwiMate/KiwiMate-Mini-1.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KiwiMate/KiwiMate-Mini-1.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KiwiMate/KiwiMate-Mini-1.1", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
- Ollama
How to use KiwiMate/KiwiMate-Mini-1.1 with Ollama:
ollama run hf.co/KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
- Unsloth Desktop
- Pi
How to use KiwiMate/KiwiMate-Mini-1.1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "KiwiMate/KiwiMate-Mini-1.1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use KiwiMate/KiwiMate-Mini-1.1 with Docker Model Runner:
docker model run hf.co/KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
- Lemonade
How to use KiwiMate/KiwiMate-Mini-1.1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
Run and chat with the model
lemonade run user.KiwiMate-Mini-1.1-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use KiwiMate/KiwiMate-Mini-1.1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use KiwiMate/KiwiMate-Mini-1.1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "KiwiMate/KiwiMate-Mini-1.1:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
KiwiMate Mini 1.1
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 1.1 is fine-tuned from Qwen3-VL-2B-Thinking, replacing Mini 1.0's Qwen2.5-VL-3B-Instruct base to add reasoning ("thinking") capabilities and move the tier onto an Apache 2.0 base.
This model is intended for fast, low-resource inference โ locally on-device or via lightweight self-hosted endpoints โ while KiwiMate Small and Medium handle heavier multimodal tasks server-side.
โ ๏ธ Preview build. This is an early release of the Mini 1.1 line for internal testing ahead of KiwiMate app integration. Expect rough edges, especially around reasoning-trace formatting and long conversations.
What's new vs. Mini 1.0 (Qwen2.5-VL-3B)
- License fixed โ Mini 1.0 inherited Qwen2.5-VL-3B's Qwen Research license (non-commercial). Mini 1.1's base, Qwen3-VL-2B-Thinking, is Apache 2.0, clearing the licensing concern flagged on the 1.0 line.
- Reasoning support โ this is a "Thinking" checkpoint: it emits a
<think>...</think>reasoning trace before its final answer, rather than answering directly. Any client or server-side proxy that displays or forwards Mini's output should account for this (strip or surface the trace deliberately โ don't pass it straight to the end user unfiltered). - Smaller base (2B vs. 3B params), though real-world speed/quality will depend on which quant you run.
- New base architecture tag:
qwen3_vl(Qwen3-VL) instead ofqwen2_5_vl.
Files
| File | Size | Description |
|---|---|---|
KiwiMate-Mini-1.1.F16.gguf |
3.4 GB | Full-precision (FP16) weights, highest quality, largest footprint |
KiwiMate-Mini-1.1.BF16.gguf |
3.4 GB | Full-precision (BF16) weights, alternative to F16 |
KiwiMate-Mini-1.1.Q8_0.gguf |
1.8 GB | Near-lossless quantization |
KiwiMate-Mini-1.1.Q6_K.gguf |
1.4 GB | Good balance of quality/size |
KiwiMate-Mini-1.1.Q4_K_M.gguf |
1.1 GB | Recommended for constrained/on-device use |
KiwiMate-Mini-1.1.IQ4_XS.gguf |
1.0 GB | Smallest, fastest, largest quality tradeoff |
KiwiMate-Mini-1.1.BF16-mmproj.gguf |
822 MB | Vision projector โ required alongside a text GGUF above for image input |
As with the Mini 1.0 line, spot-check quality at Q4_K_M / IQ4_XS relative to F16/Q6_K once you've run your usual eval pass โ the 2B Thinking base is architecturally different from 1.0's 3B base, so the quantization tradeoff may not track identically.
Usage
llama.cpp
Vision input requires loading both the text model and the mmproj file:
llama-mtmd-cli \
-hf KiwiMate/KiwiMate-Mini-1.1 \
--mmproj KiwiMate-Mini-1.1.BF16-mmproj.gguf \
-p "Describe this image." \
--jinja
Text-only:
llama-cli -hf KiwiMate/KiwiMate-Mini-1.1 --jinja
Ollama
Ollama can pull GGUF files directly from a Hugging Face repo without a local Modelfile:
ollama run hf.co/KiwiMate/KiwiMate-Mini-1.1:Q4_K_M
Chat template
Qwen3 ChatML format, --jinja handles this automatically with the commands above:
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
Because this is a Thinking checkpoint, the assistant turn will typically begin with a <think>...</think> block before the actual reply. If you're serving this behind a proxy (e.g. runpod-chat/index.ts / hf-chat), make sure your response handling parses or strips the think block rather than forwarding it verbatim, and update stop-sequence config if you're migrating directly from a non-thinking base.
Training
- Base model: Qwen/Qwen3-VL-2B-Thinking
- Framework: Unsloth (
FastVisionModel) - Quantization: GGUF via llama.cpp (F16, BF16, Q8_0, Q6_K, Q4_K_M, IQ4_XS)
- Fine-tuning focus: New Zealand English, Te Reo Mฤori awareness and cultural sensitivity, KiwiMate persona and identity, conversational NZ context, and stabilising reasoning-trace behaviour on a smaller base
Intended use
Conversational AI companion for the KiwiMate app โ casual chat, light image understanding (e.g. describing a photo, reading a screenshot), and NZ-flavoured personality, now with lightweight step-by-step reasoning for questions that benefit from it. Not intended for high-stakes reasoning, factual research, or professional/medical/legal advice.
License
This model is derived from Qwen3-VL-2B-Thinking, distributed by Qwen under the Apache 2.0 license โ unlike Mini 1.0, which carried Qwen2.5-VL-3B-Instruct's non-commercial Qwen Research license. Review the base model's license before redistributing, but this tier no longer carries the commercial-use restriction that applied to Mini 1.0.
Links
- KiwiMate app: kiwimate.net
- Dev logs: YouTube โ KyleCodeKiwi
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Model tree for KiwiMate/KiwiMate-Mini-1.1
Base model
Qwen/Qwen3-VL-2B-Thinking