初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Omega-Qwen3-Atom-8B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen3-8B
+datasets:
+- prithivMLmods/Open-Omega-Atom-1.5M
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- code
+- science
+- 'Thinking: Enabled'
+- math
+- mot
+- moe
+- stem
+---
+
+![11.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/V26CJSyLm0ixHwNZQLlc_.png)
+
+# **Omega-Qwen3-Atom-8B**
+
+> **Omega-Qwen3-Atom-8B** is a powerful 8B-parameter model fine-tuned on **Qwen3-8B** using the curated **Open-Omega-Atom-1.5M** dataset, optimized for **math and science reasoning**. It excels at symbolic processing, scientific problem-solving, and structured output generation—making it a high-performance model for researchers, educators, and technical developers working in computational and analytical domains.
+
+## **Key Features**
+
+1. **Math & Science-Centric Reasoning**
+   Fine-tuned on the **Open-Omega-Atom-1.5M** dataset, built from high-quality math, science, and symbolic reasoning tasks—ideal for analytical domains including algebra, calculus, physics, and chemistry.
+
+2. **Scientific Concept Breakdown**
+   Explains theories, derivations, and concepts across STEM fields with clarity—solves equations step-by-step, handles formula-based questions, and provides interpretive insights.
+
+3. **Symbolic Computation & Chain-of-Thought**
+   Supports multi-step reasoning, symbolic derivations, and proof-based problem solving with a strong focus on accuracy and transparency.
+
+4. **Structured Output Generation**
+   Outputs precise formats in **LaTeX**, **Markdown**, **JSON**, and **YAML** for scientific writing, educational materials, and data pipeline integration.
+
+5. **Optimized for Efficient Scientific Workflows**
+   While based on an 8B model, it is optimized for **offline inference**, **research clusters**, and **GPU workstations** that need high symbolic precision and performance.
+
+---
+
+## Quick Start with Hugging Face Transformers🤗
+
+```py
+!pip install transformers huggingface_hub accelerate
+```
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Omega-Qwen3-Atom-8B"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+prompt = "A alone can do a piece of work in 6 days and B alone in 8 days. A and B undertook to do it for Rs. 3200. With the help of C, they completed the work in 3 days. How much is to be paid to C?"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32768
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+# parsing thinking content
+try:
+    # rindex finding 151668 (</think>)
+    index = len(output_ids) - output_ids[::-1].index(151668)
+except ValueError:
+    index = 0
+
+thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
+content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
+
+print("thinking content:", thinking_content)
+print("content:", content)
+```
+
+## Answer
+
+```
+thinking content: <think>
+Okay, let's see. So the problem is about A, B, and C working together to complete a piece of work. We need to figure out how much money C should get for his help. The total payment is Rs. 3200, and we have to divide that among A, B, and C based on their contributions. Let me try to break this down step by step.
+
+First, let's understand the work rates of A and B. A can finish the work in 6 days, so his work rate is 1/6 of the work per day. Similarly, B can finish it in 8 days, so his work rate is 1/8 per day. When they work together, their combined work rate would be 1/6 + 1/8. Let me calculate that:
+
+1/6 + 1/8. To add these, find a common denominator, which is 24. So, 4/24 + 3/24 = 7/24. So together, A and B can do 7/24 of the work in one day.
+
+But the problem says that with the help of C, they completed the work in 3 days. That means all three working together finished the job in 3 days. Let's denote C's work rate as 1/x per day, where x is the number of days C would take alone. So, the combined work rate of A, B, and C is 1/6 + 1/8 + 1/x.
+
+Since they completed the work in 3 days, their combined work rate multiplied by 3 should equal 1 (the whole work). So:
+
+(1/6 + 1/8 + 1/x) * 3 = 1
+
+Let me solve for 1/x first. Let's compute 1/6 + 1/8:
+
+As before, 1/6 is 4/24 and 1/8 is 3/24, so together they are 7/24. So:
+
+(7/24 + 1/x) * 3 = 1
+
+Divide both sides by 3:
+
+7/24 + 1/x = 1/3
+
+Subtract 7/24 from both sides:
+...
+
+$$
+\boxed{400}
+$$
+```
+
+---
+
+## **Intended Use**
+
+* Math and science tutoring, equation solving, and symbolic reasoning
+* Educational tools for high-school to postgraduate-level STEM
+* Research-grade assistant for physics, chemistry, and applied math
+* Structured technical content generator for papers, lab work, and datasets
+* STEM-focused chatbot/API for integration into science education platforms
+
+## **Limitations**
+
+* Not trained for open-domain chat or emotional dialogue
+* May struggle with very large codebases or long multi-part tasks
+* Best suited for STEM fields—general language understanding may vary
+* Prioritizes correctness and formality over conversational tone.