Instructions to use mindlab-research/Macaron-V1-Preview-749B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mindlab-research/Macaron-V1-Preview-749B with Transformers:

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

pipe = pipeline("text-generation", model="mindlab-research/Macaron-V1-Preview-749B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mindlab-research/Macaron-V1-Preview-749B")
model = AutoModelForCausalLM.from_pretrained("mindlab-research/Macaron-V1-Preview-749B")
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 mindlab-research/Macaron-V1-Preview-749B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mindlab-research/Macaron-V1-Preview-749B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mindlab-research/Macaron-V1-Preview-749B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mindlab-research/Macaron-V1-Preview-749B

SGLang

How to use mindlab-research/Macaron-V1-Preview-749B 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 "mindlab-research/Macaron-V1-Preview-749B" \
    --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": "mindlab-research/Macaron-V1-Preview-749B",
		"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 "mindlab-research/Macaron-V1-Preview-749B" \
        --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": "mindlab-research/Macaron-V1-Preview-749B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mindlab-research/Macaron-V1-Preview-749B with Docker Model Runner:
```
docker model run hf.co/mindlab-research/Macaron-V1-Preview-749B
```

Macaron-V1-Preview-749B

Macaron-V1-Preview-749B is a 749B-class Mixture-of-LoRA personal-agent model from MindLab Research, post-trained from GLM-5.1 with MinT. It combines a 744B base model with five specialist LoRA adapters and a router-driven serving design for multi-turn personal-life assistance, tool-grounded planning, coding and terminal workflows, and protocol-grounded Generative UI.

Release blog: https://macaron.im/mindlab/research/macaron-v1-preview

Highlights

749B-class Mixture-of-LoRA preview model: 744B base + 5 specialist LoRAs.
Built for personal-agent tasks where user intent, private state, tools, and world state change across turns.
Uses an explicit router-tool design: the default adapter can route to specialist LoRAs through change_model.
Covers personal planning, search/calendar/tool workflows, coding and terminal tasks, computer-agent workflows, and A2UI Generative UI.
Ships as a single Hugging Face repository: base model files at root, LoRA adapters in l0/ through l4/.

Model Overview

Field	Value
Model name	Macaron-V1-Preview-749B
Organization	MindLab Research
Base model	GLM-5.1
Architecture	Mixture-of-LoRA
Parameter footprint	749B-class: 744B base + 5 x ~1B LoRA
Post-training system	MinT
Primary domain	Personal agents, tool-use agents, Generative UI
Release type	Preview
Checkpoint format	Single HF repo: base checkpoint at root; LoRAs under `l0/`-`l4/`
Context length	202,752 tokens, from `config.json` / `tokenizer_config.json`
Precision	bfloat16, from `config.json`
License	MIT; see License

Repository Layout

The release is intentionally kept in one Hugging Face model repository:

.
|-- config.json
|-- generation_config.json
|-- model.safetensors.index.json
|-- model-00001-of-00282.safetensors
|-- ...
|-- model-00282-of-00282.safetensors
|-- tokenizer.json
|-- tokenizer_config.json
|-- l0/
|   |-- adapter_config.json
|   `-- adapter_model.safetensors
|-- l1/
|-- l2/
|-- l3/
`-- l4/

Adapter roles:

Adapter	Role
`l0`	Default chat, general-purpose behavior, and routing entry point
`l1`	Personal-agent tasks such as calendar, planning, search, and life automation
`l2`	Coding, terminal, repository, and shell tasks
`l3`	A2UI and Generative UI
`l4`	Computer-agent / OpenClaw-style workflows

What Macaron Is For

A useful personal agent has to work where the user actually lives. Daily life is full of small contingent decisions: what to eat tonight, where to find a quiet table, how to reroute when traffic changes, how to schedule an errand around family obligations, or how to choose the right UI surface for a task. These tasks become hard because the user, tools, and environment all change while the agent is working.

Macaron-V1-Preview-749B targets three linked abilities:

Capability: using real tools such as search, maps, restaurants, calendars, coding environments, and task APIs.
Coherence: tracking a real human across turns, preferences, constraints, and changing intent.
Expression: choosing the right surface, such as text, card, form, table, slider, or dashboard, and rendering it quickly enough to remain useful.

Architecture

Mixture-of-LoRA

Macaron-V1-Preview-749B keeps divergent skill families in separate LoRAs over a shared base model. This is intended to reduce interference between chat, personal-agent tool use, coding, computer-agent behavior, and Generative UI, while still allowing the system to add new specialist domains without modifying the base model or existing specialists.

Router Tool

Macaron exposes model selection as a tool call rather than as an opaque separate router model. The default adapter is l0. When a specialist is needed, the serving harness can route through an OpenAI-compatible tool call such as:

{
  "name": "change_model",
  "arguments": {
    "target_model": "l1"
  }
}

The route is visible in traces and compatible with a standard tool-calling serving loop. A complete deployment should define the adapter registry, routing policy, confirmation policy, and how the system returns to the default adapter after a specialist turn.

Harness Co-Design

Macaron-V1-Preview-749B is a model-and-harness release. The model was trained and evaluated with a production-style agent harness that manages LoRA routing, tool calls, memory/state exposure, system prompts, and task metadata. Deployments that remove or replace that harness should expect behavior and benchmark results to change.

Generative UI and A2UI

Generative UI is a core Macaron capability. For many personal-agent tasks, the best answer is not only text: it may be a comparison card, editable task summary, booking form, route choice, slider, or dashboard.

Macaron-V1-Preview-749B is trained and evaluated with A2UI-style protocol actions. A2UI-Bench scores Generative UI along three layers:

Protocol correctness: emitted actions are well formed and faithful to protocol semantics.
Task construction correctness: the generated UI answers the user's request.
User-experience lift: the UI makes the task easier than a text-only answer.

The evaluation also includes rendered visual checks for failures that text-only judges can miss, such as overflow, broken layouts, hidden controls, and spacing issues.

Evaluation

The headline benchmark suite focuses on personal-agent behavior, daily-life task surfaces, Generative UI, and OpenClaw-style workflows.

Higher is better for all scores shown in the figures.

Evaluation Protocols

Macaron LivingBench. Models are evaluated on 30 multi-turn personal-agent cases with a 10-turn budget. The tested agent may make up to three tool-use decisions per user turn. API calls use a 240-second timeout and up to three request-level retries. The reported mean case score is 0.7 x need score + 0.3 x process score.

A2UI-Bench. Macaron-V1-Preview-749B is evaluated without explicit schema hints. Scores include protocol correctness, task construction correctness, and rendered UI quality.

VitaBench. VitaBench is used to stress realistic daily-life workflows. Since the original official judge model is no longer available, GLM-5.1 is used as both the judge and user model. Each query is run three times and the reported value is the average score.

PinchBench. PinchBench is used for search-grounded, high-precision personal-agent tasks. The reported setup uses Claude Haiku 4.5 as the judge model and Perplexity as the search API, and reports the best observed score.

Tau3 Bench. The reported setup uses GPT-5.2 with reasoning_effort=low as the user simulator and reports pass@1.

SWE-Bench Verified. The reported setup allows up to three retries only when an evaluation error occurs and reports the best successful attempt. The overall evaluation-error rate is approximately 0.8%.

Terminal-Bench 2.0. The reported setup uses the Harbor framework to run Macaron with the Pi Coding Agent Harness in sandboxed environments, with a maximum timeout of 4 hours, and reports pass@1.

AIME 2026. The reported score is included as a general-capability reference; the preview release is optimized primarily for personal-agent behavior and Generative UI rather than for maximizing this benchmark.

Intended Use

Macaron-V1-Preview-749B is intended for:

personal assistant research
multi-turn tool-use agents
daily-life planning and automation
coding and terminal-agent research
Generative UI / A2UI research
agent benchmark evaluation
research on modular post-training and LoRA specialization

Out-of-Scope Use

Macaron-V1-Preview-749B is not intended for:

autonomous high-stakes decisions without human confirmation
medical, legal, financial, or safety-critical advice as a sole authority
covert surveillance or privacy-invasive automation
fully unsupervised payments, bookings, messages, calendar changes, or other external write actions
production deployment without task-specific safety testing, audit logs, and confirmation flows

Installation and Loading

The repository contains both the base checkpoint and LoRA adapters, but full Macaron behavior depends on the router-aware serving harness. Loading a single LoRA is useful for inspection and specialist experiments; it is not equivalent to the full routed personal-agent system.

Install dependencies:

pip install -U transformers accelerate peft safetensors

Example: load the base checkpoint and attach one specialist LoRA:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

repo_id = "mindlab-research/Macaron-V1-Preview-749B"
adapter = "l1"

tokenizer = AutoTokenizer.from_pretrained(
    repo_id,
    trust_remote_code=True,
)

base_model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(
    base_model,
    repo_id,
    subfolder=adapter,
)
model.eval()

For full routed serving, use a harness that:

registers all five LoRA specialists
starts each conversation from l0
exposes change_model as a tool call
routes to specialists according to the adapter registry
returns control to l0 after specialist turns
enforces confirmation for external write actions

Tool Use

Macaron-V1-Preview-749B is designed to operate with external tools. Personal-agent deployments may include:

search
calendar
route planning
restaurant/place lookup
booking
messaging
task-specific APIs
A2UI rendering actions
coding, shell, and repository tools

The model should request explicit user confirmation before external write actions such as booking, sending messages, changing calendars, or making purchases.

Safety, Privacy, and Limitations

Macaron-V1-Preview-749B is designed for personal-agent settings where user state, calendar details, preferences, and inferred motivations may be sensitive. The model should avoid revealing private state unless the user explicitly authorizes disclosure.

Deployment recommendations:

keep audit logs for tool calls
require confirmation for external write actions
separate private user state from visible conversation
evaluate privacy leakage in the target harness
test tool schemas before production use

Limitations:

Preview release; behavior may change across versions.
Full behavior depends on a correct harness, router, and tool schema.
Agent performance can degrade if tools return stale, partial, or contradictory data.
Long-horizon personal-agent tasks still require human confirmation for external actions.
A2UI quality depends on renderer and protocol compatibility.
Benchmark scores may not transfer to deployments with different tools, user simulators, routing policies, or safety constraints.

License

Macaron-V1-Preview-749B is released under the MIT License. Users should also respect any requirements inherited from the GLM-5.1 base model and from dependencies used by the serving harness.

Citation

@misc{macaron2026preview749b,
  title = {Macaron-V1-Preview-749B: Mixture-of-LoRA Personal Agent Model},
  author = {MindLab Research},
  year = {2026},
  howpublished = {Hugging Face}
}

Contact

Organization: MindLab Research
Project: Macaron
Release blog: https://macaron.im/mindlab/research/macaron-v1-preview

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