How to use deepgrove/maple-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepgrove/maple-preview", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)
# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("deepgrove/maple-preview", trust_remote_code=True, device_map="auto")
How to use deepgrove/maple-preview with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepgrove/maple-preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepgrove/maple-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker model run hf.co/deepgrove/maple-preview
How to use deepgrove/maple-preview with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "deepgrove/maple-preview" \ --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": "deepgrove/maple-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
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 "deepgrove/maple-preview" \ --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": "deepgrove/maple-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use deepgrove/maple-preview with Docker Model Runner:
Do you plan to release a paper on design decisions, training and evaluation of this model? The DeepGrove website provides some insights but very limited.
P.s. Looking forward to testing when the maple architecture type is incorporated into llama.cpp
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