Instructions to use hardikchadda/hatch-agent-qwen2.5-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hardikchadda/hatch-agent-qwen2.5-1.5b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hardikchadda/hatch-agent-qwen2.5-1.5b",
	filename="hatch-q25-1.5b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hardikchadda/hatch-agent-qwen2.5-1.5b 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 hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M

Use Docker

docker model run hf.co/hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M

LM Studio
Jan

vLLM

How to use hardikchadda/hatch-agent-qwen2.5-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hardikchadda/hatch-agent-qwen2.5-1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hardikchadda/hatch-agent-qwen2.5-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M

Ollama
How to use hardikchadda/hatch-agent-qwen2.5-1.5b with Ollama:
```
ollama run hf.co/hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M
```

Unsloth Studio

How to use hardikchadda/hatch-agent-qwen2.5-1.5b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for hardikchadda/hatch-agent-qwen2.5-1.5b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for hardikchadda/hatch-agent-qwen2.5-1.5b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hardikchadda/hatch-agent-qwen2.5-1.5b to start chatting

How to use hardikchadda/hatch-agent-qwen2.5-1.5b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hardikchadda/hatch-agent-qwen2.5-1.5b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hardikchadda/hatch-agent-qwen2.5-1.5b: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 hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use hardikchadda/hatch-agent-qwen2.5-1.5b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hardikchadda/hatch-agent-qwen2.5-1.5b: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 "hardikchadda/hatch-agent-qwen2.5-1.5b: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"

Docker Model Runner
How to use hardikchadda/hatch-agent-qwen2.5-1.5b with Docker Model Runner:
```
docker model run hf.co/hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M
```

Lemonade

How to use hardikchadda/hatch-agent-qwen2.5-1.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hardikchadda/hatch-agent-qwen2.5-1.5b:Q4_K_M

Run and chat with the model

lemonade run user.hatch-agent-qwen2.5-1.5b-Q4_K_M

List all available models

lemonade list

HatchAgent — Qwen2.5-1.5B (GGUF)

A small language model fine-tuned to turn plain-English instructions into a structured control program for the Arduino UNO Q and its Modulino modules. It runs fully on-device through llama.cpp — no cloud, no API keys, works offline.

This is the model behind HatchAgent: you say "if the knob goes above 60, turn the lights red" and the board does it, with the model running locally on the UNO Q's Qualcomm MPU.

What it does

Given a natural-language instruction, it emits a compact JSON "program" — a small domain-specific language describing effects, conditions, and rules — which a rule engine on the board then executes.

{ "instruction": "blink the lights red really fast",
  "program": { "effect": "blink", "color": "red", "rate": "fast" } }

{ "instruction": "if the knob is between 40 and 60, glow yellow",
  "program": { "rule": { "src": "knob", "op": "between", "lo": 40, "hi": 60 },
               "effect": { "type": "solid", "color": "yellow" } } }

Training

Base model: Qwen2.5-1.5B-Instruct
Method: LoRA fine-tuning
Data: a synthetic dataset of ~2,800 instruction → program pairs covering single commands, sensor-triggered rules, numeric ranges, AND/OR conditions, animations, and saved scenes
Held-out evaluation: 19/19 correct on unseen paraphrases (never-seen wordings, ranges, boolean logic, scenes)

Usage (llama.cpp)

llama-server -m hatch-q25-1.5b-Q4_K_M.gguf --host 0.0.0.0 --port 8080 -c 2048

Then call the OpenAI-compatible endpoint at http://<host>:8080/v1/chat/completions.

Tip: include chat_template_kwargs: {"enable_thinking": false} in your request — the base model can otherwise spend its turn "thinking" and return empty content.

Files

hatch-q25-1.5b-Q4_K_M.gguf — Q4_K_M quantization (~0.9 GB), CPU-friendly for edge devices

Intended use

On-device natural-language control of microcontroller hardware (lights, buzzers, sensors). Designed for the Arduino UNO Q + Modulino ecosystem, but the instruction→program pattern is reusable.

License & attribution

Released under Apache-2.0. This is a fine-tune of Qwen2.5-1.5B-Instruct (© Alibaba, Apache-2.0); please retain attribution to the base model.

Part of Hatch — making hardware approachable for everyone. 🐣

Downloads last month: 28

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for hardikchadda/hatch-agent-qwen2.5-1.5b

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(213)

this model