Create README.md

README.md

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+---
+library_name: transformers
+tags:
+- CoT
+- reasoning
+license: apache-2.0
+datasets:
+- Lucid-Research/advanced-reasoning-v1-smol
+base_model:
+- ibm-granite/granite-4.0-1b-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