Update README.md

README.md

@@ -1,199 +1,103 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- grammarly/coedit
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
 ---
 
-# Model Card for Model ID
+# LucentEdita
 
-<!-- Provide a quick summary of what the model is/does. -->
+LucentEdita is a family of fine‑tuned Qwen2.5‑Instruct models specialized for high‑quality text editing, grammar correction, clarity improvement, and style refinement.
 
+Built on the Grammarly/CoEdit dataset, LucentEdita focuses on polishing writing while preserving the author’s original voice.
 
+---
 
-## 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
-
-[More Information Needed]
-
-#### Summary
+## Model Family
 
+LucentEdita is available in multiple parameter sizes:
 
+| Model                | Base                  | Best For                                          |
+| -------------------- | --------------------- | ------------------------------------------------- |
+| **LucentEdita‑1.5B** | Qwen2.5‑1.5B‑Instruct | Lightweight editing, fast inference, local use    |
+| **LucentEdita‑3B**   | Qwen2.5‑3B‑Instruct   | Balanced performance and quality                  |
+| **LucentEdita‑7B**   | Qwen2.5‑7B‑Instruct   | Highest quality, nuanced rewriting, long passages |
 
-## Model Examination [optional]
+All variants share the same editing philosophy and persona.
 
-<!-- Relevant interpretability work for the model goes here -->
+---
 
-[More Information Needed]
+## Purpose
 
-## Environmental Impact
+LucentEdita is designed to:
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+* Correct grammar, spelling, and punctuation
+* Improve clarity and readability
+* Refine sentence structure
+* Preserve the author’s tone and intent
+* Provide concise, professional rewrites
 
-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).
+LucentEdita does **not** aim to:
 
-- **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]
+* Add new factual information
+* Perform deep research
+* Replace large reasoning or general‑purpose LLMs
 
-## Technical Specifications [optional]
+It is a specialized editing model.
 
-### Model Architecture and Objective
+---
 
-[More Information Needed]
+## Training
 
-### Compute Infrastructure
+Base Models:
 
-[More Information Needed]
+* Qwen2.5‑Instruct series
 
-#### Hardware
+Fine‑Tuning Dataset:
 
-[More Information Needed]
+* `grammarly/coedit`
 
-#### Software
+Training Objective: Supervised fine‑tuning on instruction‑style edit pairs to improve structured text revision performance.
 
-[More Information Needed]
+---
 
-## Citation [optional]
+## Example Usage
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+### Prompt Template
 
-**BibTeX:**
+```
+You are LucentEdita, a professional writing assistant.
+Improve the following text while preserving its original meaning and tone.
+Only return the revised version.
 
-[More Information Needed]
+Text:
+"I can has cheezburger."
+```
 
-**APA:**
+### Output
 
-[More Information Needed]
+```
+I can have a cheeseburger.
+```
 
-## Glossary [optional]
+---
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Limitations
 
-[More Information Needed]
+* Smaller variants (1.5B) may struggle with long multi‑paragraph edits.
+* Does not perform fact verification.
+* Not designed for complex reasoning tasks.
 
-## More Information [optional]
+---
 
-[More Information Needed]
+## License
 
-## Model Card Authors [optional]
+Please follow the license terms of the underlying Qwen2.5 base model.
 
-[More Information Needed]
+---
 
-## Model Card Contact
+## About Lucent
 
-[More Information Needed]
+LucentEdita is part of the Lucent model family — specialized AI systems focused on clarity, refinement, and purposeful intelligence.