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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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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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tags: |
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- text-generation-inference |
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- Code |
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- Math |
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- RL |
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- CoT |
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--- |
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# **Monoceros-QwenM-1.5B** |
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> **Monoceros-QwenM-1.5B** is a **chain-of-thought reasoning model** fine-tuned from **Qwen-1.5B**, specifically designed for solving **mathematical problems** in both **English** and **Chinese**. It brings advanced reasoning and step-by-step problem-solving capabilities in a compact size, ideal for educational tools, tutoring systems, and math-focused assistants. |
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## **Key Features** |
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1. **Chain-of-Thought Math Reasoning** |
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Trained to produce intermediate steps for math problems, Monoceros-QwenM-1.5B enables interpretability and transparent logic in answers — critical for educational and verification purposes. |
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2. **Bilingual Proficiency (English + Chinese)** |
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Capable of understanding, reasoning, and explaining math problems fluently in **both English and Simplified Chinese**, making it suitable for multilingual learning environments. |
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3. **Compact yet Capable** |
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While only 1.5B parameters, this model delivers strong performance for arithmetic, algebra, geometry, word problems, and logical puzzles with minimal resource demands. |
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4. **Step-by-Step Computation** |
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Provides structured, multi-step answers that mirror human-like problem solving, making it easy to follow and learn from. |
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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/Monoceros-QwenM-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 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 Tutoring Bots**: Step-by-step solvers for students across basic to intermediate levels. |
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- **Bilingual Educational Apps**: Teaching math in **English** and **Chinese**, improving accessibility. |
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- **STEM Reasoning Tools**: Reasoning for science, engineering, and logic-based problems. |
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- **Lightweight LLM Applications**: Embedded use cases in browsers, mobile apps, or low-resource environments. |
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## **Limitations** |
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1. **Limited Domain Generalization**: |
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Optimized for math; performance may drop in creative writing, casual conversation, or unrelated topics. |
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2. **Parameter Scale**: |
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Though efficient, it may underperform compared to larger models on highly complex or abstract math. |
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3. **Bias from Base Model**: |
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Inherits any biases from Qwen-1.5B’s pretraining. Outputs should be validated in sensitive settings. |
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4. **Prompt Sensitivity**: |
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Precise, structured input yields better stepwise results. |
