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

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.