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---
license: apache-2.0
datasets:
- nvidia/OpenScienceReasoning-2
language:
- en
base_model:
- Qwen/Qwen3-1.7B
pipeline_tag: text-generation
library_name: transformers
tags:
- trl
- text-generation-inference
- medical
- science
---
# **OpenScienceReasoning-Qwen-e10**
> 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)
---
## **Key Features**
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. **Advanced Code Reasoning & Generation**
Supports multi-language coding with explanations, optimization hints, and error detection—ideal for algorithm synthesis, debugging, and prototyping.
3. **Mathematical & Scientific Problem Solving**
Performs analytical reasoning in physics, biology, chemistry, and mathematics—explaining concepts, solving equations, and handling symbolic derivations.
4. **Hybrid Symbolic-AI Thinking**
Combines structured logic, chain-of-thought reasoning, and open-ended inference, delivering robust performance on STEM-related tasks.
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 Lightweight Footprint for Versatile Deployment**
Balances performance and efficiency, making it deployable on **mid-range GPUs**, **offline clusters**, and **edge AI systems**.
---
## **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 the difference between Newtonian mechanics and quantum mechanics with examples."
messages = [
{"role": "system", "content": "You are a scientific tutor skilled in reasoning, math, and coding."},
{"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 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 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 |