Text Generation
Transformers
Safetensors
qwen3_5_text
tinycenn
cenn
language-modeling
research
conversational
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
- Transformers
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
File size: 4,135 Bytes
38b3221 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | {
"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
}
]
} |