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 / release_report.json

vtava

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	{
	"source": "vtava/Qwen3.5-0.8B-MemoryFusion",
	"target": "vtava/Qwen3.5-0.8B-MemoryFusion-Standalone",
	"variant": "memory_fusion",
	"source_load_mode": "memory-fusion reconstruction",
	"custom_class": "MemoryFusionQwen35Attention",
	"custom_layers": [
	3,
	7
	],
	"custom_tensor_count": 64,
	"parameters": 755373744,
	"model_bytes": 1516752888,
	"quickcheck": {
	"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": "B",
	"expected": "D",
	"correct": false
	},
	{
	"category": "Multilingual",
	"prediction": "C",
	"expected": "C",
	"correct": true
	},
	{
	"category": "Context",
	"prediction": "B",
	"expected": "B",
	"correct": true
	}
	]
	},
	"probe": {
	"prompt": "The capital of Austria is",
	"next_token_id": 44745,
	"next_token_text": " Vienna"
	},
	"standalone_reload": "PASS",
	"uploaded": false
	}