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---
library_name: transformers
license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
tags:
- text-generation-inference
- Code
- Math
- RL
- CoT
---
# **Monoceros-QwenM-1.5B**
> **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.
## **Key Features**
1. **Chain-of-Thought Math Reasoning**
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.
2. **Bilingual Proficiency (English + Chinese)**
Capable of understanding, reasoning, and explaining math problems fluently in **both English and Simplified Chinese**, making it suitable for multilingual learning environments.
3. **Compact yet Capable**
While only 1.5B parameters, this model delivers strong performance for arithmetic, algebra, geometry, word problems, and logical puzzles with minimal resource demands.
4. **Step-by-Step Computation**
Provides structured, multi-step answers that mirror human-like problem solving, making it easy to follow and learn from.
## **Quickstart with Transformers**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Monoceros-QwenM-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 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 Tutoring Bots**: Step-by-step solvers for students across basic to intermediate levels.
- **Bilingual Educational Apps**: Teaching math in **English** and **Chinese**, improving accessibility.
- **STEM Reasoning Tools**: Reasoning for science, engineering, and logic-based problems.
- **Lightweight LLM Applications**: Embedded use cases in browsers, mobile apps, or low-resource environments.
## **Limitations**
1. **Limited Domain Generalization**:
Optimized for math; performance may drop in creative writing, casual conversation, or unrelated topics.
2. **Parameter Scale**:
Though efficient, it may underperform compared to larger models on highly complex or abstract math.
3. **Bias from Base Model**:
Inherits any biases from Qwen-1.5B’s pretraining. Outputs should be validated in sensitive settings.
4. **Prompt Sensitivity**:
Precise, structured input yields better stepwise results. |