初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/OpenScienceReasoning-Qwen-e10
Source: Original Platform

README.md

@@ -0,0 +1,108 @@
+---
+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
+---
+
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/8pw-8QKAN4gLRJLy2k9Nc.png)
+
+# **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