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---
license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-1.2B-instruct/blob/main/LICENSE
metrics:
- Synthetic Data Expansion Benchmark
base_model:
- LiquidAI/LFM2.5-1.2B-Instruct
tags:
- lmstudio
- madlabOSS
- synthetic data generator
---
# Madlab Synthetic Data Generator
## 🧠 Overview
The **Madlab SDG 1.2B** 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.
It represents the first in its family to be built upon the cutting-edge **LFM2.5-instruct** foundation, marking a significant advancement over previous iterations.
---
## 🚀 Intended Use
This model is optimized for:
- Madlab synthetic data generation
It is **not** intended as a general-purpose chatbot.
---
## 🧩 Model Details
**Base Model:** LFM2.5-1.2B-instruct
**Parameter Count:** 1.2 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.
note: run numbers not aligned with multi_model_dashboard
| Run | Model | Semantic Quality | Variations | Seeds Covered | Efficiency (Variations/Param) | Dataset |
|-----|------------|---------------|------------|---------------|-------------------------------|--------------|
| 1 | LFM2-350M-16 | 6.5 | 94 | 23 | 268.57 | Madlab sdg small |
| 2 | LFM2-350M-16 | 3.5 | 46 | 11 | 131.43 | base model |
| 3 | LFM2-350M-f16 | 6.5 | 97 | 22 | 277.14 | Madlab sdg small |
| 4 | Qwen3-coder-30B-instruct-q8 | 8.2 | 149 | 26 | 4.97 | base model |
| 5 | LFM2-350M-f16 | 7.5 | 136 | 21 | 388.57 | Madlab sdg medium |
| 6 | LFM2-2.6B-f16 | 9.0 | 137 | 25 | 52.69 | Madlab sdg medium |
| 7 | LFM2-2.6B-f16 | 9.9 | 180 | 25 | 69.23 | Madlab sdg large |
| 8 | LFM2-2.6B-f16 | 6.2 | 157 | 20 | 60.38 | Madlab sdg test |
| 9 | LFM2-2.6B-f16 | 10.0 | 248 | 27 | 95.38 | Madlab sdg large |
| 10 | Qwen3-235B-q3-k_m | 9.5 | 150 | 27 | 0.64 | base model |
| 11 | LFM2.5-1.2B-instruct-f16 | 9.1 | 244 | 30 | 203.33 | Madlab sdg large |
### **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
--- |