Instructions to use inclusionAI/Ling-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ling-plus with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="inclusionAI/Ling-plus", 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/Ling-plus", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/Ling-plus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ling-plus"
# 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/Ling-plus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/Ling-plus

SGLang

How to use inclusionAI/Ling-plus 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/Ling-plus" \
    --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/Ling-plus",
		"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/Ling-plus" \
        --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/Ling-plus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/Ling-plus with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ling-plus
```

Ling-plus

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{
    "architectures": [
        "BailingMoeForCausalLM"
    ],
    "attention_dropout": 0.0,
    "auto_map": {
        "AutoConfig": "configuration_bailing_moe.BailingMoeConfig",
        "AutoModel": "modeling_bailing_moe.BailingMoeModel",
        "AutoModelForCausalLM": "modeling_bailing_moe.BailingMoeForCausalLM"
    },
    "eos_token_id": 126081,
    "pad_token_id": 126081,
    "first_k_dense_replace": 0,
    "hidden_act": "silu",
    "hidden_size": 5376,
    "initializer_range": 0.006,
    "intermediate_size": 12288,
    "max_position_embeddings": 16384,
    "model_type": "bailing_moe",
    "moe_intermediate_size": 3072,
    "num_experts": 64,
    "num_shared_experts": 1,
    "norm_topk_prob": true,
    "num_attention_heads": 56,
    "num_experts_per_tok": 4,
    "num_hidden_layers": 88,
    "num_key_value_heads": 8,
    "pretraining_tp": 1,
    "rms_norm_eps": 1e-05,
    "rope_scaling": null,
    "rope_theta": 600000,
    "tie_word_embeddings": false,
    "torch_dtype": "bfloat16",
    "transformers_version": "4.36.0",
    "use_cache": true,
    "use_bias": false,
    "use_qkv_bias": false,
    "vocab_size": 126464,
    "output_router_logits": false,
    "embedding_dropout": 0.0,
    "norm_head": true,
    "norm_softmax": false,
    "output_dropout": 0.0,
    "head_dim": 128
}