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LFM2.5-1.2B-Instruct-SDG
MadlabOSS/LFM2.5-1.2B-Instruct-SDG
LFM2.5-1.2B-Instruct-SDG is a machine learning model from MadlabOSS. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
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 traine…
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At a glance
- License
- other
- Model type
- lfm2
- Access
- Public
- Created
- Jan 7, 2026
- Updated
- Jan 8, 2026
- SHA
- 356fa762
Base models
Model card
From the Hugging Face model README
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
- Type
- lfm2
- License
- other
- Created
- Jan 7, 2026
- Updated
- Jan 8, 2026