Text Generation
Transformers
Safetensors
English
Chinese
glm_moe_dsa
macaron
personal-agent
tool-use
generative-ui
a2ui
glm
merged-checkpoint
conversational
Instructions to use mindlab-research/Macaron-V1-Preview-744B-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mindlab-research/Macaron-V1-Preview-744B-Merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mindlab-research/Macaron-V1-Preview-744B-Merged") 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-Preview-744B-Merged") model = AutoModelForCausalLM.from_pretrained("mindlab-research/Macaron-V1-Preview-744B-Merged") 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-744B-Merged 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-744B-Merged" # 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-744B-Merged", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mindlab-research/Macaron-V1-Preview-744B-Merged
- SGLang
How to use mindlab-research/Macaron-V1-Preview-744B-Merged 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-744B-Merged" \ --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-744B-Merged", "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-744B-Merged" \ --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-744B-Merged", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mindlab-research/Macaron-V1-Preview-744B-Merged with Docker Model Runner:
docker model run hf.co/mindlab-research/Macaron-V1-Preview-744B-Merged
Upload merged checkpoint file tokenizer_config.json
Browse files- tokenizer_config.json +33 -0
tokenizer_config.json
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{
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"backend": "tokenizers",
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"clean_up_tokenization_spaces": false,
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"do_lower_case": false,
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"eos_token": "<|endoftext|>",
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"extra_special_tokens": [
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"<|endoftext|>",
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"[MASK]",
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"[gMASK]",
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"[sMASK]",
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"<sop>",
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"<eop>",
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"<|system|>",
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"<|user|>",
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"<|assistant|>",
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"<|observation|>",
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"<|begin_of_image|>",
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"<|end_of_image|>",
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"<|begin_of_video|>",
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"<|end_of_video|>",
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"<|begin_of_audio|>",
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"<|end_of_audio|>",
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"<|begin_of_transcription|>",
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"<|end_of_transcription|>"
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],
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"is_local": true,
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"model_max_length": 202752,
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"model_specific_special_tokens": {},
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"pad_token": "<|endoftext|>",
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"padding_side": "left",
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"remove_space": false,
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"tokenizer_class": "TokenizersBackend"
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}
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