Text Generation
Transformers
Safetensors
English
Chinese
glm_moe_dsa
macaron
personal-agent
tool-use
mixture-of-lora
generative-ui
a2ui
glm
conversational
Eval Results
Instructions to use mindlab-research/Macaron-V1-Preview-749B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mindlab-research/Macaron-V1-Preview-749B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mindlab-research/Macaron-V1-Preview-749B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("mindlab-research/Macaron-V1-Preview-749B") model = AutoModelForMultimodalLM.from_pretrained("mindlab-research/Macaron-V1-Preview-749B") 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-Preview-749B 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-Preview-749B" # 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-Preview-749B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mindlab-research/Macaron-V1-Preview-749B
- SGLang
How to use mindlab-research/Macaron-V1-Preview-749B 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-Preview-749B" \ --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-Preview-749B", "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-Preview-749B" \ --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-Preview-749B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mindlab-research/Macaron-V1-Preview-749B with Docker Model Runner:
docker model run hf.co/mindlab-research/Macaron-V1-Preview-749B

- Xet hash:
- f21b2c56115cbcbdb8d3aee19726486f9de7f1b1592fee0c66350acde1272d4d
- Size of remote file:
- 223 kB
- SHA256:
- 63a448403695e19ef0a50881767b0d81a03d3884b30bd61451c8ee2d9dc51918
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