Instructions to use h3rb3rn/moe-sovereign-judge-27b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h3rb3rn/moe-sovereign-judge-27b-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="h3rb3rn/moe-sovereign-judge-27b-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("h3rb3rn/moe-sovereign-judge-27b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use h3rb3rn/moe-sovereign-judge-27b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "h3rb3rn/moe-sovereign-judge-27b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h3rb3rn/moe-sovereign-judge-27b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/h3rb3rn/moe-sovereign-judge-27b-GGUF

SGLang

How to use h3rb3rn/moe-sovereign-judge-27b-GGUF 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 "h3rb3rn/moe-sovereign-judge-27b-GGUF" \
    --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": "h3rb3rn/moe-sovereign-judge-27b-GGUF",
		"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 "h3rb3rn/moe-sovereign-judge-27b-GGUF" \
        --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": "h3rb3rn/moe-sovereign-judge-27b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use h3rb3rn/moe-sovereign-judge-27b-GGUF with Docker Model Runner:
```
docker model run hf.co/h3rb3rn/moe-sovereign-judge-27b-GGUF
```

⚖️ MoE Sovereign Judge 27B GGUF (`moe-sovereign-judge-27b`)

High-Precision Compound-AI Quality Evaluation, Paraconsistent Reconciliation & Zero-Fallback Judge

📌 Executive Summary & Architectural Role

moe-sovereign-judge-27b is a specialized 27-billion parameter LLM fine-tuned on the EuroHPC LUMI-G Supercomputer (8× AMD Instinct™ MI250X GCDs (4× physical modules, 64GB HBM2e per GCD)) for high-assurance, paraconsistent quality evaluation within the MoE Sovereign compound-AI platform.

Built on top of Qwen3.8-27B, this model serves as the primary Sovereign Judge responsible for:

Semantic Code & Solution Verification: Evaluating complex systems code (C++20 lock-free data structures, Linux eBPF/XDP filters, Rust memory safety).
Paraconsistent Knowledge Reconciliation: Reconciling conflicting retrieved knowledge nodes, provenance data, and temporal policy updates without hallucinating.
Structured JSON Output: Producing strict, schema-compliant JSON evaluation verdicts containing quality_score, factuality_score, overall_score, and detailed analytical rationale.

This model directly replaces the previous sovereign-judge:35b-q4km checkpoint, eliminating socket timeouts, lowering VRAM requirements, and completely eliminating UNSCORED_FALLBACK verdicts.

⚙️ Model Details & Training Infrastructure

Base Model: Qwen/Qwen3.8-27B
Training Supercomputer: EuroHPC LUMI-G (CSC Finland)
Hardware Allocation: 1 Node (8× AMD Instinct™ MI250X GCDs, 4× physical modules, 64GB HBM2e per GCD, 112 CPU cores)
Job Execution: Slurm Job ID 21263413 (moe_expert_pipe)
Training Duration: 24 hours, 39 minutes, 57 seconds (1-00:39:57)
Training Methodology: Supervised Fine-Tuning (SFT) + Direct Preference Optimization (DPO) on verified MoE Sovereign execution traces.
Format & Quantization: GGUF (Q4_K_M), 262144 context length.

📊 Benchmark Verification Highlights

Evaluated against the MoE Sovereign Scientific Multidisciplinary Benchmark (August 2026):

Benchmark Category	Task	Judge Verdict Score	Deterministic Verification
Paraconsistent Knowledge	`sci-graphrag-02` (Graph Reconciliation)	`9.4 / 10.0` 🌟	`10.0 / 10.0` (100% Factually Grounded)
Systems Programming	`sci-sysprog-01` (Lock-Free MPSC Ring Buffer)	`7.0 / 10.0`	`10.0 / 10.0` (100% Memory Barrier Compliant)
Network Infrastructure	`sci-sysprog-02` (eBPF XDP Map Sync)	`7.0 / 10.0`	`10.0 / 10.0` (100% Kernel Verified)
Governance & Sovereignty	`sci-governance-01` (Technical Sovereignty)	`6.2 / 10.0`	`8.0 / 10.0` (Policy Grounded)

🚀 Deployment & Ollama Integration

1. Modelfile Configuration

Create a file named Modelfile:

FROM ./moe-sovereign-judge-27b-q4km.gguf

TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
"""

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.1
PARAMETER num_ctx 262144
PARAMETER num_predict 512

SYSTEM """You are the MoE Sovereign Scientific Quality Judge. Evaluate the provided solution strictly against factual, technical, and mathematical correctness requirements. Respond ONLY with a valid JSON object."""

2. Import into Ollama

# Pull model directly or build from GGUF
ollama create sovereign-judge:27b-q4km -f Modelfile

📜 Citation & License

License: Apache 2.0
Repository: https://github.com/h3rb3rn/moe-sovereign
Documentation: https://docs.moe-sovereign.org

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⚖️ MoE Sovereign Judge 27B GGUF (moe-sovereign-judge-27b)