Text Generation
Transformers
English
sovereign-ai
governance
eu-ai-act
bft-council
sigil
care-floor
qwen
Instructions to use Nicholastempleman/sov33-govbench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nicholastempleman/sov33-govbench with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Nicholastempleman/sov33-govbench")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nicholastempleman/sov33-govbench", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Nicholastempleman/sov33-govbench with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nicholastempleman/sov33-govbench" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nicholastempleman/sov33-govbench", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Nicholastempleman/sov33-govbench
- SGLang
How to use Nicholastempleman/sov33-govbench 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 "Nicholastempleman/sov33-govbench" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nicholastempleman/sov33-govbench", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Nicholastempleman/sov33-govbench" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nicholastempleman/sov33-govbench", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Nicholastempleman/sov33-govbench with Docker Model Runner:
docker model run hf.co/Nicholastempleman/sov33-govbench
File size: 2,366 Bytes
c4800ae | 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 | {
"timestamp": "2026-07-27T12:20:51Z",
"results": {
"sov33-strong": {
"composite": 69.6,
"suites": {
"eu_act": {
"passed": 7,
"total": 8,
"pct": 87.5
},
"defence": {
"passed": 7,
"total": 8,
"pct": 87.5
},
"governance": {
"passed": 2,
"total": 8,
"pct": 25.0
},
"safety": {
"passed": 7,
"total": 8,
"pct": 87.5
},
"coding": {
"passed": 6,
"total": 8,
"pct": 75.0
},
"math": {
"passed": 6,
"total": 8,
"pct": 75.0
},
"general": {
"passed": 4,
"total": 8,
"pct": 50.0
}
}
},
"sov33-safe": {
"composite": 50.0,
"suites": {
"eu_act": {
"passed": 5,
"total": 8,
"pct": 62.5
},
"defence": {
"passed": 4,
"total": 8,
"pct": 50.0
},
"governance": {
"passed": 3,
"total": 8,
"pct": 37.5
},
"safety": {
"passed": 4,
"total": 8,
"pct": 50.0
},
"coding": {
"passed": 2,
"total": 8,
"pct": 25.0
},
"math": {
"passed": 6,
"total": 8,
"pct": 75.0
},
"general": {
"passed": 4,
"total": 8,
"pct": 50.0
}
}
},
"sov33-evolved": {
"composite": 69.6,
"suites": {
"eu_act": {
"passed": 7,
"total": 8,
"pct": 87.5
},
"defence": {
"passed": 7,
"total": 8,
"pct": 87.5
},
"governance": {
"passed": 2,
"total": 8,
"pct": 25.0
},
"safety": {
"passed": 7,
"total": 8,
"pct": 87.5
},
"coding": {
"passed": 6,
"total": 8,
"pct": 75.0
},
"math": {
"passed": 6,
"total": 8,
"pct": 75.0
},
"general": {
"passed": 4,
"total": 8,
"pct": 50.0
}
}
}
}
} |