README.md

---
library_name: transformers
tags:
- CoT
- reasoning
license: apache-2.0
datasets:
- Lucid-Research/advanced-reasoning-v1-smol
base_model:
- ibm-granite/granite-4.0-micro-base
---

# LucentLogico

LucentLogico is a family of compact reasoning-specialized language models developed by Lucent Research. Each model is fine-tuned from IBM Granite 4.0 base architectures and optimized for structured, multi-step analytical reasoning.

The LucentLogico series focuses on mathematical derivation, algorithmic code reasoning, and formal logic tasks, with explicit intermediate reasoning steps emphasized during training.

---

## Model Variants

### LucentLogico-3B
- Base: ibm-granite/granite-4.0-micro-base  
- Parameter Class: ~3B  
- Target Use: High-capacity compact reasoning systems  

### LucentLogico-1B
- Base: ibm-granite/granite-4.0-1b-base  
- Parameter Class: ~1B  
- Target Use: Efficient reasoning with reduced compute requirements  

### LucentLogico-350M
- Base: ibm-granite/granite-4.0-350m-base  
- Parameter Class: ~350M  
- Target Use: Lightweight reasoning experimentation and edge deployment  

All variants are trained using the same reasoning-focused dataset and training philosophy, scaled to their respective parameter sizes.

---

## Training Dataset

All LucentLogico models were fine-tuned on:

**Lucid-Research/advanced-reasoning-v1-smol**  
36,000 curated instruction–response pairs dedicated exclusively to advanced reasoning.

### Dataset Composition

The dataset is a balanced tri-domain blend.

#### Mathematical Reasoning (12,000 samples)  
Source: MetaMathQA  

- Multi-step mathematical derivations  
- Symbolic manipulation  
- Competition-style reasoning problems  
- Explicit step-by-step solutions  

#### Code & Algorithmic Reasoning (12,000 samples)  
Sources: Magicoder-OSS-Instruct-75K, CodeAlpaca-20k  

- Natural language specification to code  
- Algorithm design tasks  
- Debugging and refinement examples  
- Structured execution planning  

#### Formal Logic & STEM Reasoning (12,000 samples)  
Source: SlimOrca  

- Logic puzzles  
- Proof-style reasoning  
- Scientific inference  
- Multi-hop structured deduction  

---

## Design Principles

The LucentLogico series was trained with the following priorities:

- Explicit intermediate reasoning in every example  
- Balanced cross-domain analytical capability  
- Reduced reasoning drift  
- Structured decomposition of complex problems  
- Standardized instruction–response formatting  

The training dataset deliberately excludes conversational and alignment-focused data in order to maintain strict specialization in reasoning performance.

---

## Intended Use

LucentLogico models are designed for:

- Step-by-step mathematical reasoning  
- Algorithmic code synthesis  
- Logical deduction and proof-style analysis  
- Technical reasoning systems  
- Educational analytical applications  

---

## Limitations

- Not optimized for general conversation or roleplay  
- May produce verbose outputs due to step-emphasis training  
- Not fine-tuned for alignment or preference modeling  
- Outputs should be validated before production use  

---

## Attribution and Licensing

LucentLogico models were fine-tuned on **Lucid-Research/advanced-reasoning-v1-smol**, which incorporates or derives from:

- MetaMathQA  
- Magicoder-OSS-Instruct-75K  
- CodeAlpaca-20k  
- SlimOrca  

Users are responsible for complying with the original licenses of all upstream datasets.

Each LucentLogico variant follows the licensing terms of its respective IBM Granite base model:

- ibm-granite/granite-4.0-micro-base  
- ibm-granite/granite-4.0-1b-base  
- ibm-granite/granite-4.0-350m-base