---
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.