Instructions to use inclusionAI/Ling-flash-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ling-flash-2.0 with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ling-flash-2.0", 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-flash-2.0", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inclusionAI/Ling-flash-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ling-flash-2.0"
# 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-flash-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/Ling-flash-2.0

SGLang

How to use inclusionAI/Ling-flash-2.0 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-flash-2.0" \
    --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-flash-2.0",
		"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-flash-2.0" \
        --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-flash-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/Ling-flash-2.0 with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ling-flash-2.0
```

zhanghanxiao commited on Sep 26, 2025

Commit

18ca64a

verified ·

1 Parent(s): 3d203b4

Upload modeling_bailing_moe_v2.py with huggingface_hub

Browse files

Files changed (1) hide show

modeling_bailing_moe_v2.py +2 -2

modeling_bailing_moe_v2.py CHANGED Viewed

@@ -1159,8 +1159,8 @@ class BailingMoeV2Model(BailingMoeV2PreTrainedModel):
         self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
         self.layers = []
-        for layer_idx in range(config.num_hidden_layers):
-            layer_cls = BailingMoeV2DecoderLayer if layer_idx < config.num_hidden_layers - config.num_nextn_predict_layers else BailingMoeV2MTPLayer
             self.layers.append(layer_cls(config, layer_idx))
         self.layers = nn.ModuleList(self.layers)

         self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
         self.layers = []
+        for layer_idx in range(config.num_hidden_layers + config.num_nextn_predict_layers):
+            layer_cls = BailingMoeV2DecoderLayer if layer_idx < config.num_hidden_layers else BailingMoeV2MTPLayer
             self.layers.append(layer_cls(config, layer_idx))
         self.layers = nn.ModuleList(self.layers)