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

Libraries

How to use mindlab-research/Macaron-V1-Venti with Transformers:

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

pipe = pipeline("text-generation", model="mindlab-research/Macaron-V1-Venti")
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-Venti")
model = AutoModelForCausalLM.from_pretrained("mindlab-research/Macaron-V1-Venti", 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 mindlab-research/Macaron-V1-Venti 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-Venti"
# 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-Venti",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mindlab-research/Macaron-V1-Venti

SGLang

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

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

Macaron-V1-Venti

Macaron-V1-Venti is a 748B-parameter flagship model in the Macaron-V1 family, built for personal intelligence, tool use, coding workflows, and code-native Generative UI.

The model uses a Mixture of LoRA (MoL) architecture on top of GLM-5.2, consisting of a 744B-parameter base model and four 1B-parameter LoRA specialists. The specialists cover chat, personal-agent tasks, coding, and GenUI, with an L0 router selecting the most suitable specialist for each new user request.

Macaron-V1-Venti follows Macaron-V1-Preview and keeps the routed MoL design while moving to GLM-5.2. It is the first model to be post-trained on GLM-5.2, expanding personal-agent and Generative UI training while aligning model behavior with the production harness used for agentic workflows.

Highlights

A 748B-parameter flagship model, consisting of a 744B GLM-5.2 base model and four 1B-parameter LoRA specialists across Chat, Agent, Coding, and GenUI.
The first model to be post-trained on GLM-5.2, moving Macaron's personal-intelligence stack beyond Macaron-V1-Preview.
Model-and-harness co-design: post-trained with MinT and MindForge for production-aligned routing, tool use, UI4A Generative UI, and agent workflows.
Long-context post-training infrastructure: the V1 training stack incorporates LongStraw for multi-million-token RL execution, with Venti supporting a 1M context length.
Broad evaluation across personal intelligence, agentic tasks, coding, terminal use, and Generative UI; Macaron V1 leads the listed baselines on ChatBench, LivingBench, PinchBench, TerminalBench 2.1, and UI4ABench.

Model Overview

Field	Value
Model name	Macaron-V1-Venti
Organization	MindLab Research
Release family	Macaron-V1
Base model	GLM-5.2
Architecture	GLM-5.2 base + Mixture of LoRA (MoL) specialists
Parameter footprint	748B total: 744B base + 4 x 1B LoRA specialist parameters
Specialists	L0 Chat, L1 Agent, L2 Coding, L3 GenUI (1B each)
Post-training system	MinT + MindForge
Primary domains	Personal intelligence, tool use, coding, Generative UI
Context length	1M
Precision / serving format	BF16 base checkpoint with routed LoRA serving
License	MIT

Mixture of LoRA (MoL) Architecture

Adapter	Role	Description
`l0`	Chat	Conversational and instruction-following backbone; entry point for routing.
`l1`	Agent	Personal-life agent tasks, heavy tool use, long-horizon planning, and dynamic workflows.
`l2`	Coding	Code understanding, SWE tasks, terminal use, and repository workflows.
`l3`	GenUI	UI4A rendering and UI-driven action.

At runtime, L0 routes each new user request to the most suitable specialist. Ongoing reasoning and tool interactions remain within the selected LoRA, while completed work can be shared across specialists through concise summaries.

Evaluation

Benchmark	Macaron V1	GLM 5.2	GPT 5.5	Claude Opus 4.8	Gemini 3.1 Pro	Qwen 3.7 Max	Minimax M3
ChatBench	58.3	54.5	55.5	52.8	52.0	52.5	49.1
LivingBench	64.0	60.5	61.9	63.8	52.1	56.1	57.1
VitaBench	60.0	55.8	55.8	56.5	55.2	61.2	56.8
PinchBench	94.0	88.1	89.0	91.8	82.9	93.4	86.1
ClawGym	77.7	74.6	82.5	80.5	77.5	75.7	76.2
SWE Verified	85.6	80.4	82.9	88.6	80.6	80.4	80.5
TerminalBench 2.1	87.6	82.7	83.4	78.9	70.7	73.5	66.0
DeepSWE	58.4	54.9	70.0	58.0	10.0	18.0	20.0
SWE Atlas QnA	49.5	48.9	45.4	57.3	13.5	22.6	37.9
UI4ABench	87.8	67.1	72.1	75.9	60.3	62.5	63.0

Higher is better for all scores shown in the chart and table.

Evaluation Protocols

The headline table reports aggregate scores from the Macaron-V1 evaluation suite covering personal intelligence, agentic tool use, coding, terminal workflows, and Generative UI.

Hugging Face ingestible result files are included under .eval_results for SWE-bench Verified. The full model-card table is also mirrored in evaluation/benchmark_summary.yaml for reproducibility and downstream parsing.

The full benchmark methodology will be released with the technical report.

Usage

Hosted API

The hosted API is available at https://mintcn.macaron.xin/. Use the site for current model names, authentication, pricing, and rate-limit details.

For OpenAI-compatible deployments, requests follow the standard chat-completions shape:

curl https://mintcn.macaron.xin/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <api-key>" \
  -d '{
    "model": "Macaron-V1-Venti",
    "messages": [
      {"role": "user", "content": "Create a compact dashboard UI for tracking weekly fitness goals."}
    ],
    "temperature": 0.2,
    "max_tokens": 2048
  }'

Mixture of LoRA (MoL) Serving

For self-hosted routed serving, use the Mixture of LoRA (MoL) serving harness. The harness keeps the endpoint OpenAI-compatible while adding an L0 router, server-side LoRA metadata, and same-request switching into the selected specialist.

See the MoL serving repository for more details.

Macaron Artifacts

Macaron Artifacts is the companion local WebUI and plugin bundle for viewing Macaron sessions and GenUI output. It supports Claude Code, Codex, and Kimi Code, and can run against Macaron or another Anthropic-compatible endpoint.

Install it as a plugin in the agent runtime you use:

# Claude Code
/plugin marketplace add https://github.com/MindLab-Research/macaron-artifacts
/plugin install macaron@macaron

# Codex
codex plugin marketplace add https://github.com/MindLab-Research/macaron-artifacts
codex plugin add macaron@macaron

# Kimi Code
/plugins install https://github.com/MindLab-Research/macaron-artifacts
/reload

The WebUI can be configured from its settings page, or through environment variables:

MACARON_API_BASE=https://mintcn.macaron.xin/v1
MACARON_API_KEY=<api-key>
MACARON_MODEL=Macaron-V1-Venti

The plugin includes the genui-builder skill so supported agents can produce GenUI TSX and preview the rendered artifact in the browser. See the Artifacts repository for full install, update, and provider configuration details.

License

This repository is released under the MIT License. Users should also respect any requirements inherited from the GLM-5.2 base model and from dependencies used by the serving harness.

Citation

@misc{mindlab2026macaronv1,
  author = {{Mind Lab}},
  title = {Introducing Macaron-V1},
  year = {2026},
  howpublished = {Mind Lab: A Lab for Experiential Intelligence},
  note = {https://macaron.im/mindlab/research/introducing-macaron-v1}
}

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Safetensors

Model size

753B params

Tensor type

BF16

F32

Collection including mindlab-research/Macaron-V1-Venti

Macaron-V1

Collection

4 items • Updated 2 days ago • 12

Evaluation results

datacurve/deep-swe · Deep Swe View evaluation results leaderboard

58.4
SWE-bench/SWE-bench_Verified · Swe Bench Resolved View evaluation results leaderboard

85.6