⚠️ This model is trained for the weathersensorsmcp MCP server. It is optimised for structured MCP tool-call generation and is not intended to be a general-purpose assistant.

GGUF model fine-tuned for structured MCP tool-calling, ready for local inference via Ollama.

Model Details

Property	Value
MCP server	weathersensorsmcp
Base model (training)	mlx-community/Qwen2.5-3B-Instruct-4bit
Base model (fused export)	Qwen/Qwen2.5-3B-Instruct
Fine-tune method	QLoRA / LoRA adapter fusion
Quantization	Q5_K_M (5-bit, recommended)
GGUF file	qwen2.5-3b-weathersensorsmcp-ollama-q5_k_m.gguf
Preset	qwen2_5_3b

Intended Use

Intended for agents or apps that call the weathersensorsmcp MCP server.

The model was trained on a custom MCP tool-call JSONL dataset derived from the server's tool schema. It is intended to emit structured JSON tool calls for that server and is not suited for general chat.

Quick Start (Ollama)

Option 1: Direct from Ollama registry (if published)

bash

ollama pull qwen2.5-3b-weathersensorsmcp-ollama
ollama run qwen2.5-3b-weathersensorsmcp-ollama

Option 2: Download from HuggingFace

```bash

1. Download the GGUF and Modelfile to the same directory

curl -L -o qwen2.5-3b-weathersensorsmcp-ollama-q5_k_m.gguf https://huggingface.co/lschaffer/qwen2.5-3b-weathersensorsmcp-ollama/resolve/main/qwen2.5-3b-weathersensorsmcp-ollama-q5_k_m.gguf curl -L -o Modelfile https://huggingface.co/lschaffer/qwen2.5-3b-weathersensorsmcp-ollama/resolve/main/Modelfile

2. Register with Ollama (creates local model from GGUF + Modelfile)

ollama create qwen2.5-3b-weathersensorsmcp-ollama -f Modelfile

3. Run the model

ollama run qwen2.5-3b-weathersensorsmcp-ollama ```

Important: The `Modelfile` contains the required TEMPLATE directive for tool support. Registering the GGUF without the Modelfile will result in "does not support tools" errors.

Optional Links

Docs: https://lschaffer.github.io/tealkit
Source: https://github.com/lschaffer/tealkit

Files

qwen2.5-3b-weathersensorsmcp-ollama-q5_k_m.gguf
Modelfile — Ollama model definition with system prompt

Notes

Produced via LoRA fine-tuning on Mac Apple Silicon (MLX), adapter fusion, and llama.cpp GGUF conversion. See the training guide for full pipeline details.