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Model: prithivMLmods/rStar-Coder-Qwen3-0.6B Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- microsoft/rStar-Coder
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language:
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- en
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base_model:
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- Qwen/Qwen3-0.6B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- chain-of-thought
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- trl
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- coder
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- code
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- core
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- python
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- math
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- gspo
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---
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# **rStar-Coder-Qwen3**
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> rStar-Coder-Qwen3 is a high-efficiency, multi-domain model fine-tuned on **Qwen-0.6B** using the **rStar-Coder** dataset enhanced with **code 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.
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> \[!note]
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> GGUF: [https://huggingface.co/prithivMLmods/rStar-Coder-Qwen3-GGUF](https://huggingface.co/prithivMLmods/rStar-Coder-Qwen3-0.6B-GGUF)
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---
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## **Key Features**
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1. **Unified Reasoning Across Code, Math & Science**
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Fine-tuned on **expert clusters** spanning programming, mathematics, and scientific logic, alongside an **open code reasoning dataset**, boosting multi-modal symbolic reasoning.
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2. **Advanced Code Reasoning & Generation**
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Supports multi-language coding with explanations, optimization hints, and error detection—ideal for full-stack prototyping, algorithm synthesis, and debugging workflows.
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3. **Scientific Problem Solving**
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Performs analytical reasoning in physics, biology, and chemistry—explaining concepts, solving equations, and handling symbolic derivations step-by-step.
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4. **Hybrid Symbolic-AI Thinking**
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Combines structured logic, chain-of-thought reasoning, and open-ended inference, delivering robust performance on STEM tasks and complex prompt decomposition.
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5. **Structured Output Mastery**
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Seamlessly generates output in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, suited for research reports, technical documentation, and data formats.
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6. **Optimized Lightweight Footprint for Versatile Deployment**
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Strikes a balance between performance and efficiency, making it deployable on **mid-range GPUs**, **offline clusters**, and advanced **edge AI systems**.
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---
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## Dataset Seed
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```python
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from datasets import load_dataset
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# Load the reasoning dataset
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reasoning_dataset = load_dataset(
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"microsoft/rStar-Coder",
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data_files="seed_sft/data-00001-of-00020.parquet",
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split="train"
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)
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```
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---
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## **Quickstart with Transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/rStar-Coder-Qwen3"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Explain the difference between Newtonian mechanics and quantum mechanics with examples."
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messages = [
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{"role": "system", "content": "You are a scientific tutor skilled in code, math, and reasoning."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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---
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## **Intended Use**
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* Scientific tutoring, computational logic, and mathematical education
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* Advanced coding assistant for algorithm design, code reviews, and documentation
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* Structured technical data generation across formats and fields
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* STEM-focused chatbot or API for research and education tools
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* Mid-resource deployment requiring high symbolic fidelity
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## **Limitations**
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* Not tuned for general-purpose or long-form creative writing
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* Context limitations may hinder multi-document or full codebase analysis
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* Specialized in technical and symbolic tasks—general chat may underperform
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* Prioritizes structured reasoning over emotional or casual tone generation
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## **References**
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1. [Qwen2.5 Technical Report (2024)](https://arxiv.org/pdf/2412.15115)
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2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
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3. [microsoft/rStar-Coder Dataset](https://huggingface.co/datasets/microsoft/rStar-Coder)
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