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

Libraries

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

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

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

Use Docker

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

SGLang

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

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

Macaron-V1-Coding-Venti / README.md

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	---
	language:
	- en
	- zh
	license: mit
	base_model: zai-org/GLM-5.2
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- macaron
	- macaron-v1
	- glm-5.2
	- coding-agent
	- merged-lora
	- tool-use
	- terminal-agent
	- swe-agent
	---

	# Macaron-V1-Coding-Venti

	<div align="center">
	<img src="assets/mindlab_logo.svg" width="32%" alt="MindLab logo"/>
	</div>

	<p align="center">
	🧭 <b>Parent model:</b> <a href="https://huggingface.co/mindlab-research/Macaron-V1-Venti" target="_blank">Macaron-V1-Venti</a>
	<br>
	📖 <b>Blog:</b> <a href="https://macaron.im/mindlab/research/introducing-macaron-v1" target="_blank">Introducing Macaron-V1</a>
	<br>
	🚀 <b>Hosted API:</b> <a href="https://mintcn.macaron.xin/" target="_blank">Macaron API Platform</a>
	<br>
	🧩 <b>Artifacts:</b> <a href="https://github.com/MindLab-Research/macaron-artifacts" target="_blank">Macaron Artifacts</a>
	<br>
	🛠️ <b>Self-hosted serving:</b> <a href="https://github.com/MindLab-Research/Mixture-of-LoRA-Harness" target="_blank">Mixture of LoRA (MoL) serving harness</a>
	<br>
	📄 <b>Technical report:</b> coming soon
	</p>

	Macaron-V1-Coding-Venti is the coding-specialist checkpoint in the Macaron-V1 family. It is produced by merging the Macaron-V1-Venti L2 Coding LoRA specialist into the GLM-5.2 BF16 base model, yielding a single merged checkpoint for coding, terminal, repository, and software-engineering workflows.

	This repository is intended for deployments that want the coding specialist as a standard merged model without runtime LoRA routing. For the full routed Chat, Agent, Coding, and GenUI Mixture of LoRA (MoL) system, use [Macaron-V1-Venti](https://huggingface.co/mindlab-research/Macaron-V1-Venti).

	## Highlights

	- A merged GLM-5.2 coding-specialist checkpoint derived from the Macaron-V1-Venti L2 Coding LoRA.
	- No runtime adapter loading is required: the LoRA update is merged directly into the BF16 base weights.
	- Built for code understanding, repository-level software engineering, terminal use, and coding-agent workflows.
	- Shares the Macaron-V1 post-training stack and evaluation harness used for the flagship Macaron-V1-Venti release.

	## Model Overview

	\| Field \| Value \|
	\|---\|---\|
	\| Model name \| Macaron-V1-Coding-Venti \|
	\| Organization \| MindLab Research \|
	\| Release family \| Macaron-V1 \|
	\| Base model \| GLM-5.2 \|
	\| Source specialist \| Macaron-V1-Venti L2 Coding LoRA \|
	\| Checkpoint type \| BF16 merged checkpoint \|
	\| Runtime architecture \| GLM-5.2 weights with coding LoRA update merged into the base \|
	\| Primary domains \| Coding, terminal workflows, software-engineering agents \|
	\| Context length \| 1M \|
	\| License \| MIT \|

	## Evaluation

	The coding-specialist release reports the coding and terminal slices from the Macaron-V1 evaluation suite.

	\| Benchmark \| Macaron V1 \| GLM 5.2 \| GPT 5.5 \| Claude Opus 4.8 \| Gemini 3.1 Pro \| Qwen 3.7 Max \| Minimax M3 \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| 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 \|

	Higher is better for all scores shown in the table.

	### Evaluation Protocols

	The coding benchmark table is mirrored in [`evaluation/coding_benchmark_summary.yaml`](evaluation/coding_benchmark_summary.yaml) for reproducibility and downstream parsing.

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

	## Usage

	This repository contains a merged checkpoint. Load it as a standard GLM-5.2-compatible causal language model; no PEFT adapter attachment or MoL router is required.

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	repo_id = "mindlab-research/Macaron-V1-Coding-Venti"

	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()
	```

	For production serving, use the same GLM-5.2-compatible serving stack you would use for a merged BF16 checkpoint. The full Macaron routed system, including Chat, Agent, Coding, and GenUI specialists, remains available through [Macaron-V1-Venti](https://huggingface.co/mindlab-research/Macaron-V1-Venti) and the [Mixture of LoRA (MoL) serving harness](https://github.com/MindLab-Research/Mixture-of-LoRA-Harness).

	## Macaron Artifacts

	[Macaron Artifacts](https://github.com/MindLab-Research/macaron-artifacts) is the companion local WebUI and plugin bundle for viewing Macaron sessions and generated artifacts. 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:

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

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

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

	## 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 their serving stack.

	## Citation

	```bibtex
	@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}
	}
	```