Instructions to use vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone with Transformers:

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

pipe = pipeline("text-generation", model="vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone

SGLang

How to use vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone 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 "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone" \
    --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": "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone",
		"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 "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone" \
        --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": "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone with Docker Model Runner:
```
docker model run hf.co/vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone
```

Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone

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{
  "name": "TinyCeNN QuickCheck v1",
  "official_benchmark": false,
  "score": 80.0,
  "correct": 4,
  "total": 5,
  "items": [
    {
      "category": "Knowledge",
      "prediction": "B",
      "expected": "B",
      "correct": true
    },
    {
      "category": "STEM",
      "prediction": "C",
      "expected": "C",
      "correct": true
    },
    {
      "category": "Reasoning",
      "prediction": "C",
      "expected": "D",
      "correct": false
    },
    {
      "category": "Multilingual",
      "prediction": "C",
      "expected": "C",
      "correct": true
    },
    {
      "category": "Context",
      "prediction": "B",
      "expected": "B",
      "correct": true
    }
  ]
}