Text Generation
Transformers
Safetensors
Chinese
English
bailing_moe_linear
conversational
custom_code
Instructions to use inclusionAI/Ring-lite-linear-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/Ring-lite-linear-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inclusionAI/Ring-lite-linear-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("inclusionAI/Ring-lite-linear-preview", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use inclusionAI/Ring-lite-linear-preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inclusionAI/Ring-lite-linear-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": "inclusionAI/Ring-lite-linear-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/inclusionAI/Ring-lite-linear-preview
- SGLang
How to use inclusionAI/Ring-lite-linear-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 "inclusionAI/Ring-lite-linear-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": "inclusionAI/Ring-lite-linear-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 "inclusionAI/Ring-lite-linear-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": "inclusionAI/Ring-lite-linear-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use inclusionAI/Ring-lite-linear-preview with Docker Model Runner:
docker model run hf.co/inclusionAI/Ring-lite-linear-preview
| """ Bailing MoE model configuration """ | |
| from transformers.configuration_utils import PretrainedConfig | |
| class BailingMoeLinearConfig(PretrainedConfig): | |
| model_type = "bailing_moe_linear" | |
| def __init__( | |
| self, | |
| vocab_size=30592, | |
| hidden_size=1024, | |
| intermediate_size=None, | |
| num_hidden_layers=24, | |
| num_attention_heads=16, | |
| num_key_value_heads=0, | |
| hidden_act="silu", | |
| use_qkv_bias=False, # bailing only | |
| use_bias=True, # bailing only | |
| rms_norm_eps=1e-05, | |
| norm_head=False, # bailing only | |
| tie_word_embeddings=False, # PretrainedConfig key, here change default value. | |
| embedding_dropout=0.1, | |
| attention_dropout=0.1, | |
| output_dropout=0.1, | |
| initializer_range=0.02, | |
| max_position_embeddings=16384, | |
| rope_theta=10000.0, | |
| use_cache=True, | |
| use_sliding_window=False, | |
| sliding_window=4096, | |
| max_window_layers=28, | |
| rope_scaling=None, | |
| pad_token_id=126081, | |
| num_experts=16, | |
| num_shared_experts=0, | |
| num_experts_per_tok=2, | |
| norm_topk_prob=True, | |
| moe_intermediate_size=None, | |
| first_k_dense_replace=0, | |
| head_dim=None, | |
| output_router_logits=False, | |
| layer_group_size=1, | |
| use_linear_silu=False, | |
| linear_rope=True, | |
| use_linear_gqa=False, | |
| use_low_rank=False, | |
| rotary_type='full-1d', | |
| linear_mode='chunk', | |
| **kwargs, | |
| ): | |
| self.num_hidden_layers = num_hidden_layers | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_attention_heads = num_attention_heads | |
| self.num_key_value_heads = num_key_value_heads | |
| self.hidden_act = hidden_act | |
| self.use_qkv_bias = use_qkv_bias | |
| self.use_bias = use_bias | |
| self.norm_head = norm_head | |
| self.rms_norm_eps = rms_norm_eps | |
| self.embedding_dropout = embedding_dropout | |
| self.attention_dropout = attention_dropout | |
| self.output_dropout = output_dropout | |
| self.initializer_range = initializer_range | |
| self.max_position_embeddings = max_position_embeddings | |
| self.rope_theta = rope_theta | |
| self.use_cache = use_cache | |
| self.use_sliding_window = use_sliding_window | |
| self.sliding_window = sliding_window | |
| self.max_window_layers = max_window_layers | |
| self.head_dim = head_dim or self.hidden_size // self.num_attention_heads | |
| self.rope_scaling = rope_scaling | |
| # MoE configs | |
| self.num_experts = num_experts | |
| self.num_shared_experts = num_shared_experts | |
| self.num_experts_per_tok = num_experts_per_tok | |
| self.norm_topk_prob = norm_topk_prob | |
| self.moe_intermediate_size = moe_intermediate_size | |
| self.first_k_dense_replace = first_k_dense_replace | |
| self.output_router_logits = output_router_logits | |
| # hybrid linear configs | |
| self.layer_group_size = layer_group_size | |
| self.use_linear_silu = use_linear_silu | |
| self.linear_rope = linear_rope | |
| self.use_linear_gqa = use_linear_gqa | |
| self.use_low_rank = use_low_rank | |
| self.rotary_type = rotary_type | |
| self.linear_mode = linear_mode | |
| super().__init__(pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs) | |