MadlabOSS/LFM2-2.6B-SDG-GGUF

Madlab Synthetic Data Generator

🧠 Overview

The Madlab SDG 2.6B is part of the MadlabOSS Synthetic Data Generator family — a suite of small, efficient synthetic data generators designed for rule‑consistent, semantically coherent variation. This model was trained on a closed-source dataset created through a multi-stage synthetic data generation process using a modified Madlab training pipeline.

🚀 Intended Use

This model is optimized for:

• —Madlab synthetic data generation

It is not intended as a general-purpose chatbot.

🧩 Model Details

Base Model: LFM2-2.6B Parameter Count: 2.6 Billion Training Type: Supervised fine-tuning Sequence Length: 1024 Precision: FP16 Framework: PyTorch / Transformers

📦 Training Data

The model was trained on:

• —1444 compressed and encoded dataset pairs
• —High variation in output
• —Preservation of semantic meaning
• —Data entirely generated with Madlab

🏋️ Training Procedure

Hyperparameters

• —Epochs: 6
• —Batch size: 48
• —Learning rate: cosine schedule, peak ~4e-5
• —Optimizer: AdamW
• —Gradient clipping: 1.0
• —Gradient accumulation: 1

Hardware

Training was performed on:

• —RTX 6000 Blackwell (96GB)

📊 Evaluation

Synthetic Data Expansion Benchmark

A curated set of 30 input/target pairs was programmatically expanded using a Python script. Metrics include seed pairs covered, total variation count, and semantic quality. The task is to generate 5 variations of each incoming pair.

Qualitative Behavior

• —Overperforms in variation count
• —Maintains strict semantic correctness

🔒 Safety

This model is a synthetic data generator. It is not designed for conversational use and is not suitable for anything other than generating synthetic datasets.

It is not designed for:

• —Political advice
• —Medical advice
• —Legal advice
• —General-purpose conversation

⚠️ Limitations

• —Not a general assistant
• —Not trained for coding, math, or open-domain reasoning
• —May refuse tasks outside the Madlab SDG scope