Instructions to use badtheorylabs/Macaw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use badtheorylabs/Macaw with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="badtheorylabs/Macaw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("badtheorylabs/Macaw")
model = AutoModelForCausalLM.from_pretrained("badtheorylabs/Macaw", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use badtheorylabs/Macaw with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/badtheorylabs/Macaw

SGLang

How to use badtheorylabs/Macaw with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "badtheorylabs/Macaw" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "badtheorylabs/Macaw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "badtheorylabs/Macaw" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "badtheorylabs/Macaw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use badtheorylabs/Macaw with Docker Model Runner:
```
docker model run hf.co/badtheorylabs/Macaw
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🦜 Macaw

The assistant your Mac should have shipped with.

Ask for something in plain English and Macaw does it — sends the email, finds the file, reads the PDF, tells you what's eating your battery. It runs entirely on your Mac. Nothing is uploaded, nothing is logged, and there is no account to make.

Available today. Not a waitlist.

👉 badtheorylabs.com/macaw

What you can say to it

"email ada the q3 numbers and tell her i approved"
"why is my mac slow?"
"read ~/Documents/contract.pdf and summarise it in two sentences"
"take a screenshot, make a folder called Shots, and move it there"
"what's on my screen?"
"am i free tomorrow afternoon?"
"find my biggest files"

It chains steps on its own. "Take a screenshot, make a folder, move it there" is three actions from one sentence, and it checks each one worked before moving to the next.

What it can reach

97 tools, each one hand-written and tested — not a scrape of every command macOS exposes.


✉️ Mail	send, search, reply, unread counts, senders
📅 Calendar	create events, today's schedule, free slots, next meeting's video link
📁 Files	Spotlight search, open, move, folder sizes, biggest files, old downloads
📝 Notes & Reminders	create, append, search, complete, overdue
🎵 Music	play, pause, skip, by artist, by playlist, shuffle
🌐 Safari & Chrome	current tab, all tabs, open, search
🖥️ System	battery, disk, wifi, brightness, dark mode, volume, focus, lock, sleep
👀 Screen	read what's on screen, record it, screenshot it
📄 Documents	read and summarise PDFs, Word files, code, plain text
⚡️ Diagnostics	what's using CPU, what's eating memory, battery health

Private by construction

Not a policy — an architecture. The model weighs 1.5 GB and lives on your machine. There is no server to send anything to.

No cloud, no API keys, no account
No telemetry, no analytics, no crash reporting
Works on a plane
Your mail, files and calendar never leave the device

Specs


Size	2.7B parameters · 1.5 GB on disk (4-bit)
Memory	~2 GB while running
Speed	~1.2 s per request on an M2
Context	128K tokens
Requires	Apple Silicon (M1 or later), macOS 14+

Two builds:

Macaw-4bit-MLX — 1.5 GB, what the app runs. Start here.
Macaw (this repo) — 5.4 GB BF16, for fine-tuning or serving on a GPU.

Run it yourself

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained("badtheorylabs/Macaw")
model = AutoModelForCausalLM.from_pretrained("badtheorylabs/Macaw",
                                             dtype=torch.bfloat16)

messages = [
    {"role": "system", "content": "You are Macaw, an on-device assistant running on this Mac."},
    {"role": "user", "content": "what's my battery at?"},
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
                                 return_dict=True, return_tensors="pt")
out = model.generate(inputs["input_ids"], max_new_tokens=128)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Pass your tools with tools=[...] and it replies with a call:

<|tool_call_start|>[battery_status()]<|tool_call_end|>

The app in github.com/Badtheorylabs/Macaw handles the rest — parsing, running, and asking before anything destructive.

Measured

On an Apple M2, 4-bit build:


Tool-call accuracy	10 / 10 on the capability suite
Request latency	1.21 s mean · 0.77 s best
Decode	~40 tok/s

Method and harness are in the GitHub repo.

model.safetensors SHA-256 966d206651b96446f6a4c415d118049f478a8df79be3fa45bfd04959a4d68be0

License

Weights derive from LFM2.5-2.6B under the LFM Open License v1.0. Redistribution keeps that license and Liquid AI's attribution. Commercial use is free below $10M annual revenue. The app and tooling are MIT.

Downloads last month: 360

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for badtheorylabs/Macaw

Base model

LiquidAI/LFM2.5-2.6B-Base

Finetuned

LiquidAI/LFM2.5-2.6B

Finetuned

(11)

this model

Quantizations

3 models