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

🧭 Parent model: Macaron-V1-Venti
📖 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-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.

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 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.

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 and the Mixture of LoRA (MoL) serving harness.

Macaron Artifacts

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:

# 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

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

@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}
}