🗄️ MoE Sovereign DataInfra Expert 4B (moe-expert-datainfra-4b)

Database Query Optimization, EXPLAIN ANALYZE Tuning & Deterministic Schema Migration

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📌 Executive Summary & Architectural Role

moe-expert-datainfra-4b is a specialized 4-billion parameter Small Language Model (SLM) distilled from DeepSeek-V3 and Qwen2.5-72B-Instruct on the LUMI-G Supercomputer (8× AMD Instinct™ MI250X 128GB GPUs).

Within the MoE Sovereign compound AI architecture, this model operates as the Data Engineering, Analytical Query Optimization & Database Infrastructure Expert. Because database operations permit objective machine verification (syntax validation, EXPLAIN ANALYZE execution plans, migration rollbacks), this model is optimized for high-precision SQL (PostgreSQL, DuckDB, ClickHouse), partitioning strategies, index selection (B-Tree, BRIN, GIN, GiST), and zero-downtime DDL schema migrations.

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🎯 Functional Scope & Capabilities

1. Analytical & Relational SQL Synthesis: Writes complex CTEs, window functions (LEAD, LAG, DENSE_RANK), and analytical aggregations across PostgreSQL, DuckDB, and ClickHouse.
2. Query Plan (EXPLAIN ANALYZE) Diagnosis: Identifies sequential table scans, hash join spills, bitmap heap scan bottlenecks, and suggests optimal composite/covering indexes.
3. Deterministic Schema Migration: Formulates backward-compatible DDL migrations (e.g. ADD COLUMN ... DEFAULT without exclusive table locks, concurrent index creation).
4. Data Infrastructure Sizing: Calculates memory allocations for work_mem, shared_buffers, and storage partitioning keys.

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🎯 Training Objectives & Intended Behavioral Specialization

Capability	Base Stock Qwen 3.5 4B	moe-expert-datainfra-4b (Distilled)
Query Tuning	Recommends generic indexes without column selectivity analysis	Targeted Composite / Covering Indexes (INCLUDE), partial indexes, and join order tuning
Schema Migrations	Employs destructive DDL (ALTER TABLE ... ADD CONSTRAINT with table locks)	Zero-Downtime DDL (CONCURRENTLY, NOT VALID followed by VALIDATE CONSTRAINT)
Analytical SQL	Prone to syntax hallucinations on OLAP-specific ClickHouse/DuckDB functions	Dialect-Specific Idioms (e.g. ClickHouse ARRAY JOIN, DuckDB Parquet scans)
Performance Modeling	Hand-waves execution costs	Concrete Cost Estimations mapped to buffer page hits and memory limits

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📊 Empirical Evaluation (Held-Out Benchmark Suite)

ℹ️ Evaluation Status: Evaluated on held-out validation splits ($N=1,000$, zero training contamination). Full cross-architecture ablation suites across Compound AI vs. Monolithic LLMs are undergoing active execution in the Sovereign Scientific Benchmark Suite v1.

Evaluated on a held-out benchmark suite of 1,000 data infrastructure & SQL optimization tasks executed directly against live PostgreSQL 16, DuckDB 1.1, and ClickHouse 24.8 engines with zero training overlap:

Evaluation Metric	Base Stock Qwen 3.5 4B	moe-expert-datainfra-4b (Distilled)	Delta ($\Delta$)
SQL Syntax Validity (Multi-Dialect)	76.4 %	99.2 %	+22.8 %
Executable SQL Rate (Schema-Compliant)	69.1 %	96.8 %	+27.7 %
Query Plan Optimization Win Rate	48.3 %	89.4 %	+41.1 %
Index Recommendation Precision	58.0 %	95.2 %	+37.2 %
Safe Schema Migration Invariant Hold	54.2 %	97.6 %	+43.4 %
Analytical Window Function Correctness	62.5 %	93.8 %	+31.3 %

Note: Evaluated at temperature=0.05 across 3 independent seeds. Query plan optimization win rate measures the percentage of suggested query rewrites that measurably reduced cost / buffer reads in EXPLAIN (ANALYZE, BUFFERS).

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🏋️ Training Setup & Distillation Methodology

+-----------------------------------------------------------------------------------+
|                            LUMI-G DISTILLATION PIPELINE                           |
|                                                                                   |
|  [ Teachers: DeepSeek-V3 + Qwen2.5-72B-Instruct ]                                 |
|                       |                                                           |
|                       v  (PostgreSQL/DuckDB Engine Execution + DDL Plan Check)    |
|  [ SFT Dataset: 33,200 Validated Database Engineering Trajectories ]              |
|                       |                                                           |
|                       v  (DeepSpeed ZeRO-2, ROCm 7.0, PyTorch 2.6, 8x MI250X)     |
|  [ Student: Qwen3.5-4B Hybrid Linear Attention + Mamba Base ]                     |
|                       |                                                           |
|                       v  (LoRA r=16, alpha=32, target_modules: q/k/v/o/gate/up/down)|
|  [ Output: final_adapter -> CPU-BF16 Merge -> GGUF Q4_K_M & Q8_0 ]                |
+-----------------------------------------------------------------------------------+

Hyperparameters:

• Compute Cluster: LUMI-G (8× AMD Instinct MI250X 128GB GPUs, Slurm Job #21189564)
• Base Architecture: Qwen3.5-4B (Hybrid Linear Attention + Mamba in BF16)
• Dataset Size: 33,200 database engineering trajectories
• Epochs: 3.0
• Effective Batch Size: 128 (Micro-batch 4 × 8 GPUs × Gradient Accumulation 4)
• Learning Rate: $1.5 \times 10^{-5}$ with Cosine Decay and Warmup
• LoRA Configuration: $r=16$, $\alpha=32$, Dropout $0.05$, Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Training Loss (Final): 0.0079
• Token Accuracy (Final): 99.82 %

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⚠️ Known Limitations & Failure Modes

1. Active Data Statistics Dependency: The model plans query rewrites based on relational algebra and standard query planner heuristics; real-world cardinality estimation requires active ANALYZE statistics.
2. Proprietary Vendor Extensions: Specialized features of proprietary database engines (e.g. Oracle PL/SQL packages) are out of scope; focus is on open enterprise standards (Postgres, DuckDB, ClickHouse, SQLite, MySQL).
3. Disaster Recovery Scripts: Production failover scripts should be reviewed by human database administrators prior to execution on production clusters.

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💻 Quickstart Guide (Ollama & Llama.cpp)

1. Ollama Modelfile

FROM ./moe-expert-datainfra-4b-Q4_K_M.gguf
PARAMETER num_ctx 262144
PARAMETER temperature 0.05
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""

2. Python Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "h3rb3rn/moe-expert-datainfra-4b"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "<|im_start|>user\nOptimize this slow PostgreSQL query containing a nested subquery and recommend a covering index for table 'order_items'.<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.05)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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📑 Citation

@misc{moe_sovereign_2026_datainfra4b,
  author = {Horn, Philipp and MoE Sovereign Core AI Team},
  title = {MoE Sovereign DataInfra Expert 4B: Query Optimization & Database Engineering SLM},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/h3rb3rn/moe-expert-datainfra-4b}},
  note = {Trained on the EuroHPC LUMI-G Supercomputer}
}