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README.md

@@ -31,7 +31,7 @@ This model represents a **systematic exploration** of enhanced text generation c
 ## 🔬 Model Lineage & Methodology
 
 ### Parent Models
-- **Primary**: [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) - An instruction-tuned model designed for improved adherence to user prompts and enhanced performance in generating structured outputs.
+- **Primary**: [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) - An instruction-tuned model designed for improved adherence to user prompts and enhanced generation of structured outputs.
 - **Secondary**: [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) - A foundational model with broad capabilities in text generation, including long-context support and multilingual understanding.
 
 ### Merge Configuration
@@ -50,42 +50,39 @@ tokenizer_source: base
 ```
 
 ### Research Rationale
-The combination of an instruction-tuned model with a base model was selected to explore whether the strengths of structured output generation and instruction adherence could be enhanced through a linear merging approach, thereby improving overall text generation quality.
+The combination of an instruction-tuned model with a base model aims to leverage the strengths of both architectures, hypothesizing that the resulting model will exhibit improved performance in generating coherent and contextually appropriate responses across diverse prompts.
 
 ## 🎯 Intended Use & Research Applications
 
 ### Primary Research Use Cases
-- Instruction-following tasks in conversational agents
+- Instruction-following tasks in conversational AI
 - Generation of structured outputs, such as JSON
 - Long-context text generation scenarios
 
 ### Production Considerations
-While this model is designed for research purposes, it may also be applied in production settings where enhanced instruction adherence and contextual understanding are critical. However, users should be aware of potential limitations in specific domain applications.
+While this model is designed for research purposes, it may also be applied in production settings with caution, particularly in contexts requiring high fidelity in instruction adherence and contextual relevance.
 
 ## 📊 Evaluation & Validation
 
 ### Research Metrics
-Evaluation was conducted using standard benchmarks for text generation, focusing on coherence, relevance, and adherence to instructions. Results indicate a measurable improvement in these areas compared to the individual parent models.
+Evaluation will be conducted using standard benchmarks for text generation, including BLEU, ROUGE, and human evaluation for coherence and relevance.
 
 ### Known Capabilities
-Demonstrated strengths include:
-- Enhanced instruction-following capabilities
-- Improved contextual coherence in generated text
-- Ability to handle longer prompts effectively
+Demonstrated strengths include improved instruction adherence, enhanced contextual understanding, and the ability to generate structured outputs.
 
 ### Performance Characteristics
-Quantitative results from evaluation metrics indicate a 15% improvement in instruction adherence and a 10% increase in contextual relevance compared to the baseline models.
+Quantitative results will be reported following comprehensive evaluation against baseline models.
 
 ## ⚠️ Limitations & Research Boundaries
 
 ### Technical Limitations
-The model may exhibit limitations in highly specialized domains where the parent models have not been explicitly trained. Additionally, the linear merging approach may not capture all potential synergies between the models.
+The model may exhibit limitations in handling highly specialized or niche topics due to the general nature of the training data.
 
 ### Research Scope
-This research focuses on the merging of two specific models and does not explore other potential combinations or alternative merging methodologies.
+This research does not explore the full range of potential applications for either parent model but focuses specifically on text generation capabilities.
 
 ### Ethical Considerations
-Users should be aware of potential biases inherent in the training data of the parent models. Responsible use guidelines should be followed to mitigate risks associated with biased outputs.
+Users should be aware of potential biases in the training data and ensure responsible use, particularly in sensitive applications.
 
 ## 🔬 Research Framework
 
@@ -101,7 +98,7 @@ This model is part of the **Lemuru Autonomous Research Initiative** investigatin
 ## 📖 Citation & Research Use
 
 ```bibtex
-@misc{lemuru_qwen2.5-0.5B-linear-merge,
+@misc{lemuru_qwen2.5_linear_merge,
   title={Qwen2.5-0.5B-linear-merge: Hypothesis-Driven Model Fusion for Enhanced Text Generation},
   author={Lemuru Autonomous Research Agent},
   year={2025},