Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- CoT
+- reasoning
+license: apache-2.0
+datasets:
+- Lucid-Research/advanced-reasoning-v1-smol
+base_model:
+- ibm-granite/granite-4.0-micro-base
 ---
 
-# Model Card for Model ID
+# LucentLogico
 
-<!-- Provide a quick summary of what the model is/does. -->
+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 Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
+## Model Variants
 
-[More Information Needed]
+### LucentLogico-3B
+- Base: ibm-granite/granite-4.0-micro-base  
+- Parameter Class: ~3B  
+- Target Use: High-capacity compact reasoning systems  
 
-#### Summary
+### 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.
 
-## Model Examination [optional]
+---
 
-<!-- Relevant interpretability work for the model goes here -->
+## Training Dataset
 
-[More Information Needed]
+All LucentLogico models were fine-tuned on:
 
-## Environmental Impact
+**Lucid-Research/advanced-reasoning-v1-smol**  
+36,000 curated instruction–response pairs dedicated exclusively to advanced reasoning.
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+### Dataset Composition
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+The dataset is a balanced tri-domain blend.
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+#### Mathematical Reasoning (12,000 samples)  
+Source: MetaMathQA  
 
-## Technical Specifications [optional]
+- Multi-step mathematical derivations  
+- Symbolic manipulation  
+- Competition-style reasoning problems  
+- Explicit step-by-step solutions  
 
-### Model Architecture and Objective
+#### Code & Algorithmic Reasoning (12,000 samples)  
+Sources: Magicoder-OSS-Instruct-75K, CodeAlpaca-20k  
 
-[More Information Needed]
+- Natural language specification to code  
+- Algorithm design tasks  
+- Debugging and refinement examples  
+- Structured execution planning  
 
-### Compute Infrastructure
+#### Formal Logic & STEM Reasoning (12,000 samples)  
+Source: SlimOrca  
 
-[More Information Needed]
+- Logic puzzles  
+- Proof-style reasoning  
+- Scientific inference  
+- Multi-hop structured deduction  
 
-#### Hardware
+---
 
-[More Information Needed]
+## Design Principles
 
-#### Software
+The LucentLogico series was trained with the following priorities:
 
-[More Information Needed]
+- Explicit intermediate reasoning in every example  
+- Balanced cross-domain analytical capability  
+- Reduced reasoning drift  
+- Structured decomposition of complex problems  
+- Standardized instruction–response formatting  
 
-## Citation [optional]
+The training dataset deliberately excludes conversational and alignment-focused data in order to maintain strict specialization in reasoning performance.
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+---
 
-**BibTeX:**
+## Intended Use
 
-[More Information Needed]
+LucentLogico models are designed for:
 
-**APA:**
+- Step-by-step mathematical reasoning  
+- Algorithmic code synthesis  
+- Logical deduction and proof-style analysis  
+- Technical reasoning systems  
+- Educational analytical applications  
 
-[More Information Needed]
+---
 
-## Glossary [optional]
+## Limitations
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- 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  
 
-[More Information Needed]
+---
 
-## More Information [optional]
+## Attribution and Licensing
 
-[More Information Needed]
+LucentLogico models were fine-tuned on **Lucid-Research/advanced-reasoning-v1-smol**, which incorporates or derives from:
 
-## Model Card Authors [optional]
+- MetaMathQA  
+- Magicoder-OSS-Instruct-75K  
+- CodeAlpaca-20k  
+- SlimOrca  
 
-[More Information Needed]
+Users are responsible for complying with the original licenses of all upstream datasets.
 
-## Model Card Contact
+Each LucentLogico variant follows the licensing terms of its respective IBM Granite base model:
 
-[More Information Needed]
+- ibm-granite/granite-4.0-micro-base  
+- ibm-granite/granite-4.0-1b-base  
+- ibm-granite/granite-4.0-350m-base