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

Libraries

How to use inclusionAI/Ring-mini-linear-2.0 with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ring-mini-linear-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/Ring-mini-linear-2.0", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inclusionAI/Ring-mini-linear-2.0 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/inclusionAI/Ring-mini-linear-2.0

SGLang

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

Docker Model Runner
How to use inclusionAI/Ring-mini-linear-2.0 with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ring-mini-linear-2.0
```

pcuenq HF Staff commited on Nov 5, 2025

Commit

4c0a208

verified ·

1 Parent(s): 01e3684

import DynamicLayer

Browse files

Used in https://huggingface.co/inclusionAI/Ring-mini-linear-2.0/blob/main/modeling_bailing_moe_linear_v2.py#L1035

Files changed (1) hide show

modeling_bailing_moe_linear_v2.py +1 -1

modeling_bailing_moe_linear_v2.py CHANGED Viewed

@@ -28,7 +28,7 @@ import torch.nn.functional as F
 from torch import nn
 from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache
 from transformers.modeling_attn_mask_utils import (
     AttentionMaskConverter,
     _prepare_4d_attention_mask,

 from torch import nn
 from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, DynamicLayer
 from transformers.modeling_attn_mask_utils import (
     AttentionMaskConverter,
     _prepare_4d_attention_mask,