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

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.
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.
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.
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

Limited Domain Generalization:
Optimized for math; performance may drop in creative writing, casual conversation, or unrelated topics.
Parameter Scale:
Though efficient, it may underperform compared to larger models on highly complex or abstract math.
Bias from Base Model:
Inherits any biases from Qwen-1.5B’s pretraining. Outputs should be validated in sensitive settings.
Prompt Sensitivity:
Precise, structured input yields better stepwise results.