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

Libraries

How to use vtava/Qwen3.5-0.8B-MemoryFusion-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-MemoryFusion-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-MemoryFusion-Standalone", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/vtava/Qwen3.5-0.8B-MemoryFusion-Standalone

SGLang

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

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

Qwen3.5-0.8B-MemoryFusion-Standalone / run_manifest.json

vtava

Upload validated TinyCeNN standalone release

18e4d87 verified 16 days ago

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	{
	"run_id": "vtava__Qwen3.5-0.8B-Memo-20260917T220727Z",
	"created_utc": "2026-09-17T22:07:27.772513+00:00",
	"repo_id": "vtava/Qwen3.5-0.8B-MemoryFusion-Standalone",
	"notebook": null,
	"python": "3.13.15",
	"platform": "Linux-6.6.122+-x86_64-with-glibc2.39",
	"reports": [
	"config.json",
	"generation_config.json",
	"release_report.json",
	"standalone_config.json",
	"tokenizer_config.json"
	],
	"torch": "2.11.0+cu128",
	"cuda_available": true,
	"gpu": "Tesla T4"
	}