Under Active Development · Coming Soon

Bonacci MoE 9B

The first purpose-built mixture-of-experts model for Data Engineering & Data Science. Expert-level SQL generation, PySpark pipeline authoring, data modeling, and autonomous multi-step orchestration — all in a single 9B sparse model that activates only 2.6B parameters per token.

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9B

Total Params

2.6B

Active / Token

32K

Context

14

Capabilities

Architecture

Sparse MoE, Densely Capable

8 experts per layer, top-2 routing, GQA attention. Only 2.6B of 9B parameters fire per token — frontier-class quality at small-model cost.

Model Parameters

Model type	Sparse MoE
Total parameters	9B
Active parameters	2.6B / token
Experts per layer	8
Expert routing	Top-2
Context window	32,768
Vocabulary	49,152
License	Commercial

Transformer Block

Hidden dimension	2048
Layers	32
Attention heads	16
KV heads (GQA)	4
MLP dim / expert	5504
Positional encoding	RoPE
Normalization	RMSNorm
Activation	SwiGLU

14 Native Capabilities

Built for Data Work

Not a general-purpose code model bolted onto data tasks. Every capability is post-trained against execution-grounded rewards.

01

SQL Generation & Optimization

Production SQL from natural language. Optimize slow queries, explain execution behavior.

02

SQL Dialect Translation

BigQuery, Snowflake, Spark SQL, PostgreSQL, Trino — full semantic preservation.

03

PySpark Pipeline Authoring

Optimized batch & streaming pipelines with best-practice patterns built-in.

04

ThinkingLanguage Native

First-class understanding of the Bonacci platform's declarative pipeline DSL.

05

Pipeline Autopilot

Autonomous multi-step reasoning. Decompose, plan, iterate with execution feedback.

06

Execution Plan Generation

Structured JSON plans with cost estimates & optimization recommendations.

07

Data Security

PII detection, encryption strategy, RLS policies, GDPR/CCPA/HIPAA gap analysis.

08

Data Modeling

Star schema, Data Vault 2.0, Medallion, SCD Types 1/2/3/4.

09

Lakehouse Integration

Native knowledge of Apache Iceberg, Delta Lake, and Apache Hudi internals.

10

Tool Use & MCP

10+ tools via Model Context Protocol — DBs, storage, orchestration, catalogs.

11

RAG-Aware Generation

Consumes schema docs, table samples, and domain glossaries via the embedding model.

12

Extended Thinking

Surfaces multi-step reasoning for complex query & schema design decisions.

13

DQ & Observability

Data quality rules, freshness monitors, anomaly detection logic.

14

Streaming & CDC

Kafka, Flink, and CDC pipeline design with backward-compat checks.

In Practice

See It Work

SQL · TRANSLATION

Snowflake → BigQuery

Round-trip translation with full semantic preservation across five dialects: BigQuery, Snowflake, Spark SQL, PostgreSQL, Trino. Handles QUALIFY, OBJECT_CONSTRUCT, LISTAGG, window functions, and dialect-specific types.

Function-level dialect mapping
Window function rewrites
Type coercion safety

-- Snowflake (input):
SELECT
    DATEADD(day, -30, CURRENT_TIMESTAMP()) AS ago,
    OBJECT_CONSTRUCT('k', col1, 'v', col2) AS j,
    LISTAGG(item, ',') WITHIN GROUP (ORDER BY item) AS items
FROM my_table
QUALIFY ROW_NUMBER() OVER (PARTITION BY id ORDER BY ts DESC) = 1;

-- BigQuery (output):
SELECT
    TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY) AS ago,
    JSON_OBJECT('k', col1, 'v', col2) AS j,
    STRING_AGG(item, ',' ORDER BY item) AS items
FROM (
    SELECT *, ROW_NUMBER() OVER (PARTITION BY id ORDER BY ts DESC) AS rn
    FROM my_table
) WHERE rn = 1;
                

PIPELINE · AUTOPILOT

Autonomous Multi-Step Reasoning

Give the model a high-level goal — "monitor Postgres CDC, detect schema drift, alert on anomalies, sync to Snowflake" — and it decomposes the task, generates a plan, and emits the full pipeline.

Decomposition & planning
Execution-feedback iteration
End-to-end YAML emission

User: "Build a pipeline that monitors Postgres CDC,
       detects schema drift, alerts on anomalies,
       and syncs to Snowflake"

Step 1/6  [ Plan ]
  • Source: Postgres WAL via Debezium
  • Schema registry: Confluent
  • Drift detection: Avro schema diff
  • Anomaly: null rate + cardinality
  • Alert: Slack + PagerDuty
  • Target: Snowpipe Streaming

Step 2/6  CDC source connector...
Step 3/6  Schema drift logic...
Step 4/6  12 statistical checks generated...
Step 5/6  Alert routing with severity tiers...
Step 6/6  MERGE statement + SCD Type 2...

✓ Pipeline complete — 847 lines emitted.
  $ bonacci deploy --pipeline cdc_snowflake_sync
                

THINKINGLANGUAGE · NATIVE

Declarative Bonacci Pipelines

The model has first-class understanding of ThinkingLanguage — the Bonacci platform's declarative pipeline DSL. It composes sources, transforms, and sinks with awareness of the execution model.

Source / transform / sink composition
Dependency & schedule awareness
Multi-sink fan-out (warehouse + feature store)

pipeline: customer_ltv_360
schedule: "0 3 * * *"

sources:
  - id: orders
    type: jdbc
    query: "SELECT * FROM orders WHERE updated_at > {{ last_run }}"

transforms:
  - id: compute_ltv
    type: sql
    dialect: spark
    sql: |
      SELECT c.customer_id, SUM(o.total_amount) AS ltv
      FROM customers c LEFT JOIN orders o USING (customer_id)
      GROUP BY c.customer_id

sinks:
  - type: bigquery
    write_mode: merge
  - type: redis
    key_template: "ltv:{customer_id}"
                

Training

122B+ Tokens, Execution-Grounded

Two-stage pre-training on TPU v6e-16 with MaxText/JAX, followed by SFT and GRPO with code-execution rewards.

PRE-TRAIN · STAGE 1

General Corpus

32B

Broad language understanding, code, and reasoning foundations.

PRE-TRAIN · STAGE 2

FineWeb-Edu

90B

High-quality educational and technical web content.

POST-TRAIN · SFT

Supervised Fine-Tuning

15K+

Curated DE/DS pairs — SQL, PySpark, ThinkingLanguage, pipeline blueprints, schemas.

POST-TRAIN · RL

GRPO + Code Execution

230

Reward-shaped on SQL correctness, query plan quality, and PySpark execution — evaluated across 230 test cases across 6 benchmarks.

Hardware

TPU v6e-16

Google Cloud

Framework

MaxText / JAX

FSDP parallelism

Long Context

32K · YaRN

RoPE scaling extension

Deployment

Run It Anywhere

Safetensors, GGUF quantizations, OpenAI-compatible vLLM, Ollama, llama.cpp, and Docker.

🤗 HuggingFace ⚡ vLLM 🦙 Ollama 🐳 Docker 📦 llama.cpp · GGUF

vLLM · Production

OpenAI-Compatible API

$ vllm serve thinkingdbx/bonacci-moe-9b \
    --dtype bfloat16 \
    --max-model-len 32768 \
    --tensor-parallel-size 2 \
    --gpu-memory-utilization 0.90

# Query via OpenAI-compatible endpoint
$ curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
      "model": "thinkingdbx/bonacci-moe-9b",
      "messages": [{"role":"user","content":"..."}],
      "temperature": 0.1
    }'
                    

HuggingFace · Transformers

Python (bfloat16)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "thinkingdbx/bonacci-moe-9b"

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

messages = [
  {"role": "user", "content": "Optimize this BigQuery..."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=1024, temperature=0.1)
                    

The Bonacci Platform

Where the Model Lives

Bonacci MoE 9B is the intelligence layer powering Bonacci Studio and Bonacci Flow.

BONACCI · STUDIO

Bonacci Studio

Conversational data engineering. Chat with the model, get production SQL, PySpark, and ThinkingLanguage pipelines back.

Explore Studio

BONACCI · FLOW

Bonacci Flow

Pipeline Autopilot at platform scale — autonomous orchestration, scheduling, and self-healing for your data infrastructure.

Explore Flow

Get Started

Bonacci MoE 9B is Coming Soon

The model is currently under active development. Leave us a note and we'll let you know the moment weights and APIs are available — on HuggingFace, vLLM, and embedded inside the Bonacci platform.

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In Active Development Commercial License (planned) Self-hostable (planned)