Update README.md

README.md

@@ -2,4 +2,107 @@
 license: apache-2.0
 datasets:
 - nvidia/OpenScienceReasoning-2
----
+language:
+- en
+base_model:
+- Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- trl
+- medical
+- biology
+- science
+- chemistry
+---
+
+# **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.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/OpenScienceReasoning-Qwen-e10-GGUF](https://huggingface.co/prithivMLmods/OpenScienceReasoning-Qwen-e10-GGUF)
+
+---
+
+## **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.
+
+2. **Enhanced Analytical Tutoring**
+   Explains concepts in physics, chemistry, biology, and mathematics with structured, step-by-step clarity.
+
+3. **Advanced Code & Math Support**
+   Performs algorithmic reasoning, symbolic math derivations, and multi-language coding with debugging support.
+
+4. **Chain-of-Thought Mastery**
+   Prioritizes transparent reasoning by decomposing problems into logical steps, ensuring explainability and precision.
+
+5. **Multi-Format Structured Output**
+   Natively produces **LaTeX**, **Markdown**, **JSON**, and **YAML**, making it ideal for technical reports and data pipelines.
+
+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.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/OpenScienceReasoning-Qwen-e10"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain how photosynthesis works using step-by-step reasoning."
+
+messages = [
+    {"role": "system", "content": "You are a scientific tutor skilled in reasoning, math, and STEM explanations."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+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
+
+## **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.