Instructions to use Nicholastempleman/sov33-govbench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

sov33-govbench / govbench_final_e2e.json

Nicholastempleman

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	{
	"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
	}
	}
	}
	}
	}