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
```

mindlab-bot commited on 4 days ago

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@@ -24,17 +24,17 @@ tags:
 </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.

 </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.