Text Generation
Transformers
Safetensors
English
causal-lm
mixture-of-experts
reasoning
ternary
custom-code
conversational
custom_code
Instructions to use deepgrove/maple-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use deepgrove/maple-preview with vLLM:
Install from pip and serve model
# 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?" } ] }'Use Docker
docker model run hf.co/deepgrove/maple-preview
- SGLang
How to use deepgrove/maple-preview 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 "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?" } ] }'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 "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 Model Runner
How to use deepgrove/maple-preview with Docker Model Runner:
docker model run hf.co/deepgrove/maple-preview
| {%- if tools %} | |
| {{- '<|im_start|>system\n' }} | |
| {%- if messages[0].role == 'system' %} | |
| {{- messages[0].content + '\n\n' }} | |
| {%- endif %} | |
| {{- '# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>' }} | |
| {%- for tool in tools %} | |
| {{- '\n' }} | |
| {{- tool | tojson }} | |
| {%- endfor %} | |
| {{- '\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n' }} | |
| {%- else %} | |
| {%- if messages[0].role == 'system' %} | |
| {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- for message in messages %} | |
| {%- if message.content is string %} | |
| {%- set content = message.content %} | |
| {%- else %} | |
| {%- set content = '' %} | |
| {%- endif %} | |
| {%- if message.role == 'user' or (message.role == 'system' and not loop.first) %} | |
| {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>\n' }} | |
| {%- elif message.role == 'assistant' %} | |
| {%- set reasoning_content = '' %} | |
| {%- if message.reasoning_content is string %} | |
| {%- set reasoning_content = message.reasoning_content %} | |
| {%- elif '</think>' in content %} | |
| {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %} | |
| {%- set content = content.split('</think>')[-1].lstrip('\n') %} | |
| {%- endif %} | |
| {%- if reasoning_content %} | |
| {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} | |
| {%- else %} | |
| {{- '<|im_start|>' + message.role + '\n' + content }} | |
| {%- endif %} | |
| {%- if message.tool_calls %} | |
| {%- for tool_call in message.tool_calls %} | |
| {%- if (loop.first and content) or not loop.first %} | |
| {{- '\n' }} | |
| {%- endif %} | |
| {%- if tool_call.function %} | |
| {%- set tool_call = tool_call.function %} | |
| {%- endif %} | |
| {{- '<tool_call>\n{\"name\": \"' }} | |
| {{- tool_call.name }} | |
| {{- '\", \"arguments\": ' }} | |
| {%- if tool_call.arguments is string %} | |
| {{- tool_call.arguments }} | |
| {%- else %} | |
| {{- tool_call.arguments | tojson }} | |
| {%- endif %} | |
| {{- '}\n</tool_call>' }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- '<|im_end|>\n' }} | |
| {%- elif message.role == 'tool' %} | |
| {%- if loop.first or messages[loop.index0 - 1].role != 'tool' %} | |
| {{- '<|im_start|>user' }} | |
| {%- endif %} | |
| {{- '\n<tool_response>\n' }} | |
| {{- content }} | |
| {{- '\n</tool_response>' }} | |
| {%- if loop.last or messages[loop.index0 + 1].role != 'tool' %} | |
| {{- '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{- '<|im_start|>assistant\n<think>\n' }} | |
| {%- endif %} | |