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

Libraries

How to use inclusionAI/Ling-lite with Transformers:

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

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inclusionAI/Ling-lite with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Ling-lite / config.json

m1ngcheng

add eos token and the end of assistant content (#8)

2ea7627 verified about 1 year ago

raw

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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",
	"AutoModelForTokenClassification": "modeling_bailing_moe.BailingMoeForTokenClassification"
	},
	"eos_token_id": 126081,
	"pad_token_id": 126081,
	"first_k_dense_replace": 0,
	"hidden_act": "silu",
	"hidden_size": 2048,
	"initializer_range": 0.006,
	"intermediate_size": 5632,
	"max_position_embeddings": 32768,
	"model_type": "bailing_moe",
	"moe_intermediate_size": 1408,
	"num_experts": 64,
	"num_shared_experts": 2,
	"norm_topk_prob": true,
	"num_attention_heads": 16,
	"num_experts_per_tok": 6,
	"num_hidden_layers": 28,
	"num_key_value_heads": 4,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-06,
	"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,
	"embedding_dropout": 0.0,
	"norm_head": true,
	"norm_softmax": false,
	"output_dropout": 0.0,
	"output_router_logits": false
	}