README.md

metadata

language: en
pipeline_tag: text-generation
library_name: transformers
tags:
  - llama
  - data-management
  - data-engineering
  - migration
  - sql
  - reasoning
  - grpo
  - rlhf
license: other
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B

Agentic Data 1

A specialized 8B reasoning model fine-tuned for Data Management, Data Engineering, and Migration tasks.

Model Details

• Base: DeepSeek-R1-Distill-Llama-8B
• Training: 3-stage pipeline (SFT QLoRA → Doc-Grounded SFT → GRPO Reinforcement Learning)
• Format: BF16 SafeTensors (PyTorch / HuggingFace Transformers compatible)
• Parameters: 8B

Training Pipeline

Stage	Method	Data	Hardware
Stage 1	QLoRA SFT (3 versions)	14,666 synthetic pairs + 7,558 doc-grounded chunks	Apple Silicon M-Series
Stage 2	GRPO Reinforcement Learning	100 reasoning prompts with reward functions	NVIDIA H100 80GB

Capabilities

• SQL Dialect Conversion: Oracle ↔ PostgreSQL ↔ T-SQL ↔ Snowflake ↔ BigQuery ↔ Databricks
• ETL Pipeline Migration: Informatica → dbt, DataStage → Spark, BODS → Airflow
• Legacy System Modernization: COBOL, JCL, SAS, ABAP → modern stacks
• Data Quality & Governance: Assessment, validation, and compliance
• Migration Lifecycle: Discovery → Risk → Planning → Conversion → Verification
• Step-by-Step Reasoning: Uses <think>...</think> tags for chain-of-thought reasoning

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "DataManagement-AI/Agentic-Data-1",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("DataManagement-AI/Agentic-Data-1")

messages = [
    {"role": "system", "content": "You are Agentic Data 1, an expert data management and migration reasoning model. Think step-by-step before answering."},
    {"role": "user", "content": "Convert this Oracle PL/SQL stored procedure to PostgreSQL PL/pgSQL."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=1500)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

Benchmarks (SFT V3)

Metric	Base Model	Agentic Data 1	Improvement
Overall Score	0.554	0.636	+14.8%
Implementation Quality	0.584	0.761	+30.3%
Think-Tag Rate	0%	100%	∞
Reasoning Quality	0.534	0.622	+16.5%

License

For research and educational purposes.