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

Libraries

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

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

pipe = pipeline("text-generation", model="mindlab-research/Macaron-V1-Tall")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("mindlab-research/Macaron-V1-Tall")
model = AutoModelForMultimodalLM.from_pretrained("mindlab-research/Macaron-V1-Tall", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Macaron-V1-Tall

📖 Blog: Introducing Macaron-V1
🚀 Hosted API: Macaron API Platform
🧩 Artifacts: Macaron Artifacts
🛠️ Self-hosted serving: Mixture of LoRA (MoL) serving harness
📄 Technical report: coming soon

Macaron-V1-Tall is a Mixture of LoRA (MoL) model in the Macaron-V1 family, built on Qwen3.6-35B-A3B for personal intelligence, tool use, coding workflows, and code-native Generative UI.

The model combines a Qwen3.6 MoE base with the Macaron-V1 specialist system: four LoRA specialists for chat, personal-agent tasks, coding, and GenUI, with an L0 router selecting the most suitable specialist for each new user request.

Macaron-V1-Tall is released alongside Macaron-V1-Venti as a sibling model in the Macaron-V1 family. Both models share the same product-facing specialist layout and routed serving interface while targeting different deployment and experimentation profiles.

Highlights

A Qwen3.6-35B-A3B-based Macaron-V1 model with the full Macaron specialist system.
Mixture of LoRA (MoL) architecture with four specialists across Chat, Agent, Coding, and GenUI.
Built for personal-agent workflows, tool use, repository-level coding, and UI4A Generative UI.
BF16 checkpoint with four LoRA adapters stored under loras/L0 through loras/L3.
Supports a 262K context length according to the released checkpoint configuration.

Model Overview

Field	Value
Model name	Macaron-V1-Tall
Organization	MindLab Research
Release family	Macaron-V1
Base model	Qwen3.6-35B-A3B
Architecture	Qwen3.6 MoE base + Mixture of LoRA (MoL) specialists
Checkpoint contents	BF16 base checkpoint + four LoRA adapters
Specialists	L0 Chat, L1 Agent, L2 Coding, L3 GenUI
LoRA rank	64
Post-training system	MinT + MindForge
Primary domains	Personal intelligence, tool use, coding, Generative UI
Context length	262K
Text config	40 layers, 2048 hidden size, 16 attention heads, 2 KV heads, 256 experts, 8 experts per token
Precision / serving format	BF16 base checkpoint with routed LoRA serving
License	MIT

Mixture of LoRA (MoL) Architecture

Adapter	Role	Description
`loras/L0`	Chat	Conversational and instruction-following backbone; entry point for routing.
`loras/L1`	Agent	Personal-life agent tasks, heavy tool use, long-horizon planning, and dynamic workflows.
`loras/L2`	Coding	Code understanding, SWE tasks, terminal use, and repository workflows.
`loras/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

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-Tall",
    "messages": [
      {"role": "user", "content": "Create a compact dashboard UI for tracking weekly fitness goals."}
    ],
    "temperature": 0.2,
    "max_tokens": 2048
  }'

Transformers

This repository contains the base checkpoint at the repository root and the Macaron LoRA specialists under loras/. Load the base checkpoint with a Transformers version that supports the Qwen3.6 / Qwen3.5 MoE architecture.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "mindlab-research/Macaron-V1-Tall"

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

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

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.

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 Qwen3.6-35B-A3B 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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Tensor type

BF16

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