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 / fast_eval.json

vtava

Add TinyCeNN FastEval results

38b3221 verified 13 days ago

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4.14 kB

	{
	"suite": "TinyCeNN FastEval v1",
	"official_full_benchmark": false,
	"method": "deterministic sampled zero-shot next-token letter scoring",
	"samples_per_benchmark": 50,
	"seed": 42,
	"datasets": {
	"MMLU-Pro": "TIGER-Lab/MMLU-Pro:test",
	"PIQA": "lighteval/piqa:validation",
	"MMMLU-DE": "openai/MMMLU:DE_DE:test",
	"GPQA-Diamond": "Wanfq/gpqa:gpqa_diamond:train"
	},
	"device": "cuda",
	"torch_version": "2.11.0+cu128",
	"results": [
	{
	"name": "vtava__Qwen3.5-0.8B-MemoryFusion-Standalone",
	"benchmarks": [
	{
	"benchmark": "MMLU-Pro",
	"samples": 50,
	"correct": 5,
	"accuracy_pct": 10.0,
	"seconds": 16.41,
	"items_per_second": 3.046,
	"input_tokens": 11865
	},
	{
	"benchmark": "PIQA",
	"samples": 50,
	"correct": 27,
	"accuracy_pct": 54.0,
	"seconds": 7.66,
	"items_per_second": 6.529,
	"input_tokens": 3588
	},
	{
	"benchmark": "MMMLU-DE",
	"samples": 50,
	"correct": 14,
	"accuracy_pct": 28.0,
	"seconds": 8.62,
	"items_per_second": 5.802,
	"input_tokens": 4562
	},
	{
	"benchmark": "GPQA-Diamond",
	"samples": 50,
	"correct": 15,
	"accuracy_pct": 30.0,
	"seconds": 15.72,
	"items_per_second": 3.18,
	"input_tokens": 10910
	}
	],
	"overall": {
	"samples": 200,
	"correct": 61,
	"accuracy_pct": 30.5,
	"seconds": 48.41,
	"input_tokens": 30925
	},
	"generation": {
	"prompts": 3,
	"generated_tokens": 72,
	"seconds": 8.094,
	"tokens_per_second": 8.895,
	"peak_vram_gib": 1.51,
	"sample_outputs": [
	"\n\n<think>\n\n</think>\n\nVienna is the capital of Austria because it serves as the central political, cultural, and administrative",
	"\n\nTo determine how long the robot can operate, we need to calculate the total number of hours it can run on one",
	"\n\n<think>\n\n</think>\n\nAn API gateway is a central server that acts as a bridge between a web application ("
	]
	},
	"peak_vram_gib_observed": 1.51
	},
	{
	"name": "Qwen/Qwen3.5-0.8B",
	"benchmarks": [
	{
	"benchmark": "MMLU-Pro",
	"samples": 50,
	"correct": 6,
	"accuracy_pct": 12.0,
	"seconds": 10.51,
	"items_per_second": 4.756,
	"input_tokens": 11865
	},
	{
	"benchmark": "PIQA",
	"samples": 50,
	"correct": 24,
	"accuracy_pct": 48.0,
	"seconds": 4.68,
	"items_per_second": 10.674,
	"input_tokens": 3588
	},
	{
	"benchmark": "MMMLU-DE",
	"samples": 50,
	"correct": 17,
	"accuracy_pct": 34.0,
	"seconds": 5.45,
	"items_per_second": 9.17,
	"input_tokens": 4562
	},
	{
	"benchmark": "GPQA-Diamond",
	"samples": 50,
	"correct": 13,
	"accuracy_pct": 26.0,
	"seconds": 9.65,
	"items_per_second": 5.181,
	"input_tokens": 10910
	}
	],
	"overall": {
	"samples": 200,
	"correct": 60,
	"accuracy_pct": 30.0,
	"seconds": 30.3,
	"input_tokens": 30925
	},
	"generation": {
	"prompts": 3,
	"generated_tokens": 72,
	"seconds": 5.225,
	"tokens_per_second": 13.781,
	"peak_vram_gib": 1.473,
	"sample_outputs": [
	"\n\n<think>\n\n</think>\n\nVienna is the capital of Austria because it is the largest city in the country and serves as",
	"\n\nTo determine how long the robot can operate, we need to calculate the total number of hours it can run on one",
	"\n\n<think>\n\n</think>\n\nAn API Gateway is a central server that acts as a single entry point for all incoming"
	]
	},
	"peak_vram_gib_observed": 1.473
	}
	]
	}