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
| """Configuration for Maple models.""" | |
| from transformers.configuration_utils import PretrainedConfig | |
| class MapleConfig(PretrainedConfig): | |
| """Configuration for the Maple mixture-of-experts causal language model.""" | |
| model_type = "maple" | |
| def __init__( | |
| self, | |
| vocab_size=151936, | |
| hidden_size=2048, | |
| num_hidden_layers=20, | |
| num_attention_heads=16, | |
| num_key_value_heads=4, | |
| hidden_act="silu", | |
| use_bias=False, | |
| rms_norm_eps=1e-6, | |
| tie_word_embeddings=False, | |
| attention_dropout=0.0, | |
| initializer_range=0.02, | |
| max_position_embeddings=32768, | |
| rope_theta=10000.0, | |
| use_cache=True, | |
| rope_scaling=None, | |
| partial_rotary_factor=0.5, | |
| pad_token_id=None, | |
| eos_token_id=None, | |
| num_experts=256, | |
| num_experts_per_tok=8, | |
| moe_intermediate_size=512, | |
| head_dim=128, | |
| output_router_logits=False, | |
| **kwargs, | |
| ): | |
| self.num_hidden_layers = num_hidden_layers | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_attention_heads = num_attention_heads | |
| self.num_key_value_heads = num_key_value_heads | |
| self.hidden_act = hidden_act | |
| self.use_bias = use_bias | |
| self.rms_norm_eps = rms_norm_eps | |
| self.attention_dropout = attention_dropout | |
| self.initializer_range = initializer_range | |
| self.max_position_embeddings = max_position_embeddings | |
| self.rope_theta = rope_theta | |
| self.use_cache = use_cache | |
| self.head_dim = head_dim or self.hidden_size // self.num_attention_heads | |
| self.rope_scaling = rope_scaling | |
| self.partial_rotary_factor = partial_rotary_factor | |
| self.num_experts = num_experts | |
| self.num_experts_per_tok = num_experts_per_tok | |
| self.moe_intermediate_size = moe_intermediate_size | |
| self.output_router_logits = output_router_logits | |
| super().__init__( | |
| pad_token_id=pad_token_id, | |
| eos_token_id=eos_token_id, | |
| tie_word_embeddings=tie_word_embeddings, | |
| **kwargs, | |
| ) | |