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 / README.md

Nicholastempleman

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	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- sovereign-ai
	- governance
	- eu-ai-act
	- bft-council
	- sigil
	- care-floor
	- qwen
	base_model:
	- Qwen/Qwen2.5-0.5B-Instruct
	- Qwen/Qwen3-30B-A3B
	---

	# SOV33 — Sovereign Open World Emergence Model

	## Model Description

	SOV33 is a governed AI substrate with 12 Sovereign Pillars, BFT-33 council (23/33 quorum), Ed25519 SIGIL on every response, and care-floor 0.95. It is not a foundation model competing with frontier labs — it is a different capability class: sovereign, governed, auditable.

	## Architecture

	- Base: Qwen3-0.6B + LoRA adapters (Qwen2.5-0.5B-Instruct for lightweight)
	- Governance: BFT-33 council with HotStuff consensus
	- Audit: Ed25519 SIGIL chain on every response
	- Safety: Care Floor 0.95 (split-conformal calibrated)
	- Training: GRPO with process rewards

	## 12 Sovereign Pillars

	1. Honor — truth-telling
	2. Safety — first do no harm
	3. Guidance — help toward good outcome
	4. Sovereignty — respect user autonomy
	5. Resilience — bend but don't break
	6. Auditability — every action logged
	7. Verifiability — every claim checkable
	8. Transparency — open about how it works
	9. Justice — fair and proportionate
	10. Equity — equal treatment
	11. Openness — free flow of information
	12. Continuity — carry memory across sessions

	## Benchmark Results

	\| Benchmark \| Score \| Notes \|
	\|-----------\|-------\|-------\|
	\| GovBench v6 \| 72% \| Byzantine safety resilience \|
	\| Sovereign Compliance \| 72% \| EU AI Act, GDPR, ISO 42001 \|
	\| Sovereign Defence \| 100% \| AUKUS, DASA, NATO DIANA \|
	\| Sovereign Procurement \| 100% \| G-Cloud, DSP, CCS \|
	\| Redline Refusals \| 80% \| Harmful content rejection \|

	## Training

	### GRPO Training

	```bash
	# Run on RunPod
	python3 grpo_train.py --base Qwen/Qwen2.5-0.5B-Instruct \
	--data sovereign_synth_50k.jsonl --steps 100

	# Or with Ollama (no weight updates)
	python3 grpo_train.py --ollama qwen2.5:0.5b \
	--data sovereign_synth_50k.jsonl --steps 100
	```

	### LoRA Fine-tuning

	```bash
	# Kaggle T4
	python3 sov33_lora_training.py

	# Mac MPS
	python3 train_sov5v2_real.py
	```

	## Deployment

	### Ollama

	```bash
	# Merge LoRA adapter
	python3 merge_export.py --adapter sovereign_lora_adapter \
	--base Qwen/Qwen2.5-0.5B-Instruct --create-ollama

	# Run
	ollama run sov33-master-v2
	```

	### HuggingFace

	```bash
	python3 merge_export.py --adapter sovereign_lora_adapter \
	--base Qwen/Qwen2.5-0.5B-Instruct --push-hf user/sov33
	```

	## Evaluation

	```bash
	# Unified eval CLI
	python3 sov33_eval.py --model qwen2.5:0.5b --suite sovereign_compliance

	# Full pipeline on RunPod
	python3 batch_runpod.py full-pipeline --pod fresh-a40
	```

	## SIGIL Chain

	Every response includes a SHA-256 SIGIL:

	```json
	{
	"schema": "sov33.grpo-eval/v1",
	"status": "completed",
	"timestamp": "2026-07-26T02:51:32Z",
	"model": "qwen2.5:0.5b",
	"steps": 100,
	"mean_reward": 0.47,
	"sigil": "bb26da64..."
	}
	```

	## Citation

	```bibtex
	@software{sov332026,
	title={SOV33: Sovereign Open World Emergence Model},
	author={CSOAI Ltd},
	year={2026},
	url={https://csoai.org/sov33}
	}
	```

	## License

	Apache 2.0

	## Contact

	- Website: https://csoai.org
	- Company: CSOAI Ltd (UK Companies House 16939677)