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license: other |
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license_name: lfm1.0 |
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license_link: https://huggingface.co/LiquidAI/LFM2-2.6B/blob/main/LICENSE |
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metrics: |
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- Synthetic Data Expansion Benchmark |
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base_model: |
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- LiquidAI/LFM2-2.6B |
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tags: |
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- lmstudio |
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- madlabOSS |
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- synthetic data generator |
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--- |
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# Madlab Synthetic Data Generator |
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## 🧠 Overview |
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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. |
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This model was trained on a closed-source dataset created through a multi-stage synthetic data generation process using a modified Madlab training pipeline. |
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## 🚀 Intended Use |
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This model is optimized for: |
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- Madlab synthetic data generation |
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It is **not** intended as a general-purpose chatbot. |
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--- |
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## 🧩 Model Details |
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**Base Model:** LFM2-2.6B |
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**Parameter Count:** 2.6 Billion |
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**Training Type:** Supervised fine-tuning |
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**Sequence Length:** 1024 |
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**Precision:** FP16 |
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**Framework:** PyTorch / Transformers |
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## 📦 Training Data |
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The model was trained on: |
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- **1444 compressed and encoded dataset pairs** |
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- High variation in output |
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- Preservation of semantic meaning |
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- Data entirely generated with Madlab |
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## 🏋️ Training Procedure |
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### **Hyperparameters** |
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- Epochs: 6 |
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- Batch size: 48 |
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- Learning rate: cosine schedule, peak ~4e-5 |
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- Optimizer: AdamW |
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- Gradient clipping: 1.0 |
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- Gradient accumulation: 1 |
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### **Hardware** |
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Training was performed on: |
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- RTX 6000 Blackwell (96GB) |
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--- |
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## 📊 Evaluation |
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### **Synthetic Data Expansion Benchmark** |
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A curated set of 30 input/target pairs was programmatically expanded using a Python script. |
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Metrics include seed pairs covered, total variation count, and semantic quality. |
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The task is to generate 5 variations of each incoming pair. |
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| Run | Model | Semantic Quality | Variations | Seeds Covered | Efficiency (Variations/Param) | Dataset | |
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|-----|------------|---------------|------------|---------------|-------------------------------|--------------| |
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| 1 | LFM2-350M-16 | 6.5 | 94 | 23 | 268.57 | Madlab sdg small | |
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| 2 | LFM2-350M-16 | 3.5 | 46 | 11 | 131.43 | base model | |
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| 3 | LFM2-350M-f16 | 6.5 | 97 | 22 | 277.14 | Madlab sdg small | |
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| 4 | Qwen3-coder-30B-instruct-q8 | 8.2 | 149 | 26 | 4.97 | base model | |
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| 5 | LFM2-350M-f16 | 7.5 | 136 | 21 | 388.57 | Madlab sdg medium | |
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| 6 | LFM2-2.6B-f16 | 9.0 | 137 | 25 | 52.69 | Madlab sdg medium | |
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| 7 | LFM2-2.6B-f16 | 9.9 | 180 | 25 | 69.23 | Madlab sdg large | |
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| 8 | LFM2-2.6B-f16 | 6.2 | 157 | 20 | 60.38 | Madlab sdg test | |
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| 9 | LFM2-2.6B-f16 | 10.0 | 248 | 27 | 95.38 | Madlab sdg large | |
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| 10 | Qwen3-235B-q3-k_m | 9.5 | 150 | 27 | 0.64 | base model | |
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| 11 | LFM2.5-1.2B-instruct-f16 | 9.1 | 244 | 30 | 203.33 | Madlab sdg large | |
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### **Qualitative Behavior** |
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- Overperforms in variation count |
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- Maintains strict semantic correctness |
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--- |
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## 🔒 Safety |
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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. |
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It is **not** designed for: |
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- Political advice |
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- Medical advice |
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- Legal advice |
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- General-purpose conversation |
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## ⚠️ Limitations |
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- Not a general assistant |
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- Not trained for coding, math, or open-domain reasoning |
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- May refuse tasks outside the Madlab SDG scope |
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