---
library_name: transformers
tags:
- text-generation-inference
- code
- math
- R1
- distill
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
---
![PPP.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/7xnCRDtZ0T3-oWnCrw-Rf.png)

# **Castula-U2-QwenRe-1.5B**

> **Castula-U2-QwenRe-1.5B** is a **compact, multilingual reasoning model** fine-tuned from **Qwen-1.5B**, excelling in **mathematical problem solving**, **logical reasoning**, **code generation**, and **general-purpose tasks**. Its step-by-step reasoning and bilingual fluency make it ideal for educational systems, coding assistants, and lightweight reasoning applications.

## **Key Features**

1. **Advanced Step-by-Step Reasoning**  
   Fine-tuned to produce intermediate steps for math, logic, and code problems, offering transparency and interpretability crucial for education, coding help, and diagnostics.

2. **Multilingual Proficiency (English + Chinese)**  
   Understands and solves problems in **both English and Simplified Chinese**, making it accessible in diverse learning and working environments.

3. **Compact Yet Versatile (1.5B Parameters)**  
   Small enough for **low-resource environments**, yet capable of **math**, **logical puzzles**, **basic coding tasks**, and general comprehension, balancing performance and efficiency.

4. **Structured Computation & Problem Solving**  
   Mirrors human-like multi-step problem-solving, making solutions easy to follow, debug, or verify.

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Castula-U2-QwenRe-1.5B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Solve: A train travels 180 km in 3 hours. What is its average speed?"
messages = [
    {"role": "system", "content": "You are a helpful tutor skilled in solving math, logic, and code problems with step-by-step explanations."},
    {"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**

- **Math & Logic Tutoring**: Solves problems with explanations ideal for students and educators.
- **Code Assistant**: Helps with beginner-to-intermediate code generation and understanding.
- **Bilingual Apps**: Educational tools in **English** and **Chinese** for a global audience.
- **Lightweight Reasoning Systems**: Deployable in **mobile apps**, **browser extensions**, and **edge devices**.

## **Limitations**

1. **Domain Specialization**:  
   Best in math, logic, and code. Performance may degrade in highly creative or abstract language tasks.

2. **Compact Scale**:  
   While efficient, may underperform larger models in deeply complex reasoning or long-context tasks.

3. **Inherited Bias**:  
   May reflect biases from the base model (Qwen-1.5B); outputs should be verified for sensitive or critical uses.

4. **Prompt Sensitivity**:  
   Structured, clearly stated inputs produce significantly better outputs.