prithivMLmods
/

Castula-U2-QwenRe-1.5B

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 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+tags:
+- text-generation-inference
+- code
+- math
+- distill
+- r1
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
 ---
+# **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.