初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Horologium-QwenC-1.5B
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
+tags:
+- text-generation-inference
+- code
+- math
+- RL
+---
+
+![C.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/F_eWj0Fsf87Gcwz8-ftIJ.png)
+
+# **Horologium-QwenC-1.5B**
+
+> **Horologium-QwenC-1.5B** is a **reasoning-focused language model** trained extensively on both **coding** and **mathematics** problems using **reinforcement learning (RL)**. It is designed to provide intelligent, step-by-step solutions to structured tasks that require logical precision, algorithmic thought, and symbolic computation.
+
+## **Key Features**
+
+1. **Unified Reasoning for Code & Math**  
+   Tailored to perform both **code understanding/generation** and **mathematical problem-solving**, with a consistent focus on clarity and logic.
+
+2. **Reinforcement Learning Fine-Tuning**  
+   Trained with **reinforcement learning** to improve reward-aligned behaviors in complex problem-solving scenarios—especially in debugging, proof validation, and computational tasks.
+
+3. **Symbolic and Numerical Proficiency**  
+   Capable of handling **symbolic math**, **algebra**, **calculus**, and **discrete mathematics**, while also excelling at code logic, syntax validation, and API usage.
+
+4. **Compact yet Powerful**  
+   At **1.5B parameters**, this model provides **strong reasoning capabilities** while remaining efficient for edge devices and local deployment.
+
+5. **Structured Output**  
+   Produces high-quality, structured results in Markdown, JSON, and annotated code blocks with contextual explanations.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Horologium-QwenC-1.5B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve this: A function f is defined as f(x) = x^2 + 2x + 1. Find f(5) and explain the steps. Then write equivalent Python code."
+
+messages = [
+    {"role": "system", "content": "You are an expert in math and coding. Solve problems step-by-step and explain clearly."},
+    {"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]
+```
+
+## **Intended Use**
+
+- **Educational Tutoring Systems**  
+  For students and learners exploring both programming and mathematics.
+
+- **Coding & Algorithmic Interview Prep**  
+  Useful for solving DSA questions, algorithmic challenges, and leetcode-style problems.
+
+- **Math & Code Co-Pilots**  
+  Integrated into coding environments to explain both logic and formulas used in implementations.
+
+- **Data Analysis & Scientific Computing**  
+  Aids in writing and verifying data-centric scripts and computational logic.
+
+## **Limitations**
+
+1. **Scope of Accuracy**  
+   May occasionally produce mathematically sound but **over-explained or verbose** solutions.
+
+2. **Complex Multistep Problems**  
+   Performance may degrade slightly on **very long multi-turn** symbolic derivations or nested algorithms.
+
+3. **Limited Real-Time Adaptation**  
+   No awareness of real-time data or updates beyond training scope.
+
+4. **Security & Logic Bugs**  
+   Always audit generated code or logic for real-world use.