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--- |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- code |
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- math |
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- R1 |
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- distill |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-1.5B-Instruct |
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pipeline_tag: text-generation |
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--- |
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# **Castula-U2-QwenRe-1.5B** |
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> **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. |
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## **Key Features** |
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1. **Advanced Step-by-Step Reasoning** |
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Fine-tuned to produce intermediate steps for math, logic, and code problems, offering transparency and interpretability crucial for education, coding help, and diagnostics. |
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2. **Multilingual Proficiency (English + Chinese)** |
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Understands and solves problems in **both English and Simplified Chinese**, making it accessible in diverse learning and working environments. |
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3. **Compact Yet Versatile (1.5B Parameters)** |
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Small enough for **low-resource environments**, yet capable of **math**, **logical puzzles**, **basic coding tasks**, and general comprehension, balancing performance and efficiency. |
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4. **Structured Computation & Problem Solving** |
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Mirrors human-like multi-step problem-solving, making solutions easy to follow, debug, or verify. |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Castula-U2-QwenRe-1.5B" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Solve: A train travels 180 km in 3 hours. What is its average speed?" |
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messages = [ |
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{"role": "system", "content": "You are a helpful tutor skilled in solving math, logic, and code problems with step-by-step explanations."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## **Intended Use** |
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- **Math & Logic Tutoring**: Solves problems with explanations ideal for students and educators. |
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- **Code Assistant**: Helps with beginner-to-intermediate code generation and understanding. |
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- **Bilingual Apps**: Educational tools in **English** and **Chinese** for a global audience. |
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- **Lightweight Reasoning Systems**: Deployable in **mobile apps**, **browser extensions**, and **edge devices**. |
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## **Limitations** |
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1. **Domain Specialization**: |
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Best in math, logic, and code. Performance may degrade in highly creative or abstract language tasks. |
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2. **Compact Scale**: |
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While efficient, may underperform larger models in deeply complex reasoning or long-context tasks. |
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3. **Inherited Bias**: |
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May reflect biases from the base model (Qwen-1.5B); outputs should be verified for sensitive or critical uses. |
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4. **Prompt Sensitivity**: |
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Structured, clearly stated inputs produce significantly better outputs. |
