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 / evaluation /coding_benchmark_summary.yaml

mindlab-bot

Publish Macaron-V1-Coding-Venti

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	model: Macaron-V1-Coding-Venti
	source:
	name: Model Card
	url: https://huggingface.co/mindlab-research/Macaron-V1-Coding-Venti
	notes:
	higher_is_better: true
	missing_values_are_null: false
	checkpoint_type: merged GLM-5.2 BF16 checkpoint with Macaron-V1 L2 Coding LoRA update merged into the base weights
	baselines:
	- Macaron V1
	- GLM 5.2
	- GPT 5.5
	- Claude Opus 4.8
	- Gemini 3.1 Pro
	- Qwen 3.7 Max
	- Minimax M3
	results:
	- benchmark: SWE Verified
	scores: {Macaron V1: 85.6, GLM 5.2: 80.4, GPT 5.5: 82.9, Claude Opus 4.8: 88.6, Gemini 3.1 Pro: 80.6, Qwen 3.7 Max: 80.4, Minimax M3: 80.5}
	- benchmark: TerminalBench 2.1
	scores: {Macaron V1: 87.6, GLM 5.2: 82.7, GPT 5.5: 83.4, Claude Opus 4.8: 78.9, Gemini 3.1 Pro: 70.7, Qwen 3.7 Max: 73.5, Minimax M3: 66.0}
	- benchmark: DeepSWE
	scores: {Macaron V1: 58.4, GLM 5.2: 54.9, GPT 5.5: 70.0, Claude Opus 4.8: 58.0, Gemini 3.1 Pro: 10.0, Qwen 3.7 Max: 18.0, Minimax M3: 20.0}
	- benchmark: SWE Atlas QnA
	scores: {Macaron V1: 49.5, GLM 5.2: 48.9, GPT 5.5: 45.4, Claude Opus 4.8: 57.3, Gemini 3.1 Pro: 13.5, Qwen 3.7 Max: 22.6, Minimax M3: 37.9}