Update README.md

README.md

@@ -19,8 +19,8 @@ tags:
 
 # **OpenScienceReasoning-Qwen-e10**
 
-> **OpenScienceReasoning-Qwen-e10** is a specialized reasoning model fine-tuned on **Qwen3-1.7B**, leveraging the [**nvidia/OpenScienceReasoning-2**](https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2) dataset.
-> This dataset contributes **10,000 carefully curated entries** focusing on **scientific reasoning** and **chain-of-thought problem solving**, enhancing the model’s ability to explain, deduce, and generate structured outputs across STEM domains.
+> OpenScienceReasoning-Qwen-e10 is a high-efficiency, science-focused reasoning model fine-tuned on **Qwen3-1.7B** using the [**nvidia/OpenScienceReasoning-2**](https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2) dataset. It incorporates **10,000 distinct entries** for scientific reasoning, chain-of-thought exploration, and analytical problem solving.
+> The model blends symbolic precision, scientific logic, and structured output fluency—making it an ideal tool for researchers, educators, and developers seeking advanced reasoning under constrained compute.
 
 > \[!note]
 > GGUF: [https://huggingface.co/prithivMLmods/OpenScienceReasoning-Qwen-e10-GGUF](https://huggingface.co/prithivMLmods/OpenScienceReasoning-Qwen-e10-GGUF)
@@ -29,23 +29,23 @@ tags:
 
 ## **Key Features**
 
-1. **Science-Centered Reasoning**
-   Fine-tuned on **10K entries from OpenScienceReasoning-2**, covering diverse scientific fields with explicit chain-of-thought annotations for robust logical inference.
+1. **Scientific Reasoning & Chain-of-Thought**
+   Fine-tuned on **10,000 curated entries** from the **OpenScienceReasoning-2** dataset, designed to enhance step-by-step analytical reasoning in science and mathematics.
 
-2. **Enhanced Analytical Tutoring**
-   Explains concepts in physics, chemistry, biology, and mathematics with structured, step-by-step clarity.
+2. **Advanced Code Reasoning & Generation**
+   Supports multi-language coding with explanations, optimization hints, and error detection—ideal for algorithm synthesis, debugging, and prototyping.
 
-3. **Advanced Code & Math Support**
-   Performs algorithmic reasoning, symbolic math derivations, and multi-language coding with debugging support.
+3. **Mathematical & Scientific Problem Solving**
+   Performs analytical reasoning in physics, biology, chemistry, and mathematics—explaining concepts, solving equations, and handling symbolic derivations.
 
-4. **Chain-of-Thought Mastery**
-   Prioritizes transparent reasoning by decomposing problems into logical steps, ensuring explainability and precision.
+4. **Hybrid Symbolic-AI Thinking**
+   Combines structured logic, chain-of-thought reasoning, and open-ended inference, delivering robust performance on STEM-related tasks.
 
-5. **Multi-Format Structured Output**
-   Natively produces **LaTeX**, **Markdown**, **JSON**, and **YAML**, making it ideal for technical reports and data pipelines.
+5. **Structured Output Mastery**
+   Seamlessly generates output in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, suited for technical documentation, research papers, and structured data.
 
-6. **Optimized for Mid-Resource Deployment**
-   Retains a lightweight footprint suitable for **mid-range GPUs**, **offline clusters**, and **edge AI systems** without sacrificing symbolic fidelity.
+6. **Optimized Lightweight Footprint for Versatile Deployment**
+   Balances performance and efficiency, making it deployable on **mid-range GPUs**, **offline clusters**, and **edge AI systems**.
 
 ---
 
@@ -63,10 +63,10 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-prompt = "Explain how photosynthesis works using step-by-step reasoning."
+prompt = "Explain the difference between Newtonian mechanics and quantum mechanics with examples."
 
 messages = [
-    {"role": "system", "content": "You are a scientific tutor skilled in reasoning, math, and STEM explanations."},
+    {"role": "system", "content": "You are a scientific tutor skilled in reasoning, math, and coding."},
     {"role": "user", "content": prompt}
 ]
 
@@ -94,15 +94,15 @@ print(response)
 
 ## **Intended Use**
 
-* Scientific education and tutoring across STEM fields
-* Chain-of-thought enhanced problem solving and explanation
-* Structured technical documentation and data generation
-* Algorithm synthesis, debugging, and mathematical derivations
-* Deployment in research assistants and educational APIs
+* Scientific tutoring, computational reasoning, and mathematical education
+* Research assistant for physics, chemistry, biology, and interdisciplinary domains
+* Structured technical data generation in multiple formats
+* STEM-focused chatbot or API for research and education tools
+* Deployment in mid-resource environments requiring high reasoning fidelity
 
 ## **Limitations**
 
-* Not designed for creative writing or casual conversation
-* Limited to context window constraints—multi-document reasoning may be hindered
-* Specialization in scientific reasoning may reduce performance on general-purpose chat
-* Focuses on structured reasoning, not emotional tone generation.
+* Not tuned for general-purpose or long-form creative writing
+* Context limitations may hinder multi-document or full codebase analysis
+* Specialized for scientific and technical reasoning—general chat may underperform
+* Prioritizes structured logic over casual or emotional tone generation