Update README.md

README.md

@@ -15,4 +15,116 @@ tags:
 - gspo
 - coder
 - code
----
+---
+
+# **rStar-Coder-Qwen3(exp)**
+
+> *rStar-Coder-Qwen3* is a high-efficiency, multi-domain model fine-tuned on *Qwen3-4B* using the **rStar-Coder** dataset enhanced with **code, math, and science expert clusters** and an extended **open code reasoning dataset**. This model blends symbolic precision, scientific logic, and structured output fluency—making it an ideal tool for developers, educators, and researchers seeking advanced reasoning under constrained compute.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/rStar-Coder-Qwen3-GGUF](https://huggingface.co/prithivMLmods/rStar-Coder-Qwen3-GGUF)
+
+---
+
+## **Key Features**
+
+1. **Unified Reasoning Across Code, Math & Science**
+   Fine-tuned on **expert clusters** spanning programming, mathematics, and scientific logic, alongside an **open code reasoning dataset**, boosting multi-modal symbolic reasoning.
+
+2. **Advanced Code Reasoning & Generation**
+   Supports multi-language coding with explanations, optimization hints, and error detection—ideal for full-stack prototyping, algorithm synthesis, and debugging workflows.
+
+3. **Scientific Problem Solving**
+   Performs analytical reasoning in physics, biology, and chemistry—explaining concepts, solving equations, and handling symbolic derivations step-by-step.
+
+4. **Hybrid Symbolic-AI Thinking**
+   Combines structured logic, chain-of-thought reasoning, and open-ended inference, delivering robust performance on STEM tasks and complex prompt decomposition.
+
+5. **Structured Output Mastery**
+   Seamlessly generates output in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, suited for research reports, technical documentation, and data formats.
+
+6. **Optimized 4B Footprint for Versatile Deployment**
+   Strikes a balance between performance and efficiency, making it deployable on **mid-range GPUs**, **offline clusters**, and advanced **edge AI systems**.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/rStar-Coder-Qwen3"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain the difference between Newtonian mechanics and quantum mechanics with examples."
+
+messages = [
+    {"role": "system", "content": "You are a scientific tutor skilled in code, math, and reasoning."},
+    {"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)
+```
+
+---
+
+## **Dataset Seed**
+
+```python
+from datasets import load_dataset
+
+# Load the reasoning dataset
+reasoning_dataset = load_dataset(
+    "microsoft/rStar-Coder",
+    data_files="seed_sft/data-00001-of-00020.parquet",
+    split="train"
+)
+```
+
+---
+
+## **Intended Use**
+
+* Scientific tutoring, computational logic, and mathematical education
+* Advanced coding assistant for algorithm design, code reviews, and documentation
+* Structured technical data generation across formats and fields
+* STEM-focused chatbot or API for research and education tools
+* Mid-resource deployment requiring high symbolic fidelity
+
+## **Limitations**
+
+* Not tuned for general-purpose or long-form creative writing
+* Context limitations may hinder multi-document or full codebase analysis
+* Specialized in technical and symbolic tasks—general chat may underperform
+* Prioritizes structured reasoning over emotional or casual tone generation
+
+---
+
+## **References**
+
+1. [Qwen2.5 Technical Report (2024)](https://arxiv.org/pdf/2412.15115)
+2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
+3. [rStar-Coder Dataset](https://huggingface.co/datasets/microsoft/rStar-Coder)