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--- |
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license: apache-2.0 |
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datasets: |
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- nvidia/OpenScienceReasoning-2 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen3-1.7B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- trl |
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- text-generation-inference |
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- medical |
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- science |
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--- |
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# **OpenScienceReasoning-Qwen-e10** |
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> 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. |
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> 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. |
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> \[!note] |
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> GGUF: [https://huggingface.co/prithivMLmods/OpenScienceReasoning-Qwen-e10-GGUF](https://huggingface.co/prithivMLmods/OpenScienceReasoning-Qwen-e10-GGUF) |
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--- |
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## **Key Features** |
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1. **Scientific Reasoning & Chain-of-Thought** |
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Fine-tuned on **10,000 curated entries** from the **OpenScienceReasoning-2** dataset, designed to enhance step-by-step analytical reasoning in science and mathematics. |
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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 algorithm synthesis, debugging, and prototyping. |
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3. **Mathematical & Scientific Problem Solving** |
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Performs analytical reasoning in physics, biology, chemistry, and mathematics—explaining concepts, solving equations, and handling symbolic derivations. |
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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-related tasks. |
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5. **Structured Output Mastery** |
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Seamlessly generates output in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, suited for technical documentation, research papers, and structured data. |
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6. **Optimized Lightweight Footprint for Versatile Deployment** |
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Balances performance and efficiency, making it deployable on **mid-range GPUs**, **offline clusters**, and **edge AI systems**. |
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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/OpenScienceReasoning-Qwen-e10" |
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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 reasoning, math, and coding."}, |
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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 reasoning, and mathematical education |
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* Research assistant for physics, chemistry, biology, and interdisciplinary domains |
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* Structured technical data generation in multiple formats |
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* STEM-focused chatbot or API for research and education tools |
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* Deployment in mid-resource environments requiring high reasoning 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 for scientific and technical reasoning—general chat may underperform |
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* Prioritizes structured logic over casual or emotional tone generation |
