Nu2-Lupi-Qwen-14B

Nu2-Lupi-Qwen-14B is based on the Qwen 2.5 14B modality architecture, designed to enhance mathematical reasoning capabilities. This model is optimized for complex problem-solving, logical deduction, and multi-step mathematical reasoning. It has been fine-tuned using the gsm8k-platinum dataset to improve accuracy, structured responses, and contextual understanding in mathematical domains.

Key Improvements

1. Enhanced Mathematical Proficiency: The model excels in solving complex mathematical problems, including algebra, calculus, and number theory.
2. Advanced Reasoning Capabilities: Optimized for step-by-step problem-solving, enabling clear and logical explanations for mathematical queries.
3. Improved Instruction Following: Capable of understanding and executing multi-step instructions with precision, ensuring structured and coherent outputs.
4. Long-Context Support: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed problem breakdowns.
5. Multilingual Mathematical Reasoning: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

Quickstart with transformers

Here is a code snippet with apply_chat_template to show you how to load the tokenizer and model and generate content:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Nu2-Lupi-Qwen-14B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Solve the equation: 3x + 5 = 14."
messages = [
    {"role": "system", "content": "You are a mathematical reasoning assistant."},
    {"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

1. Mathematical Reasoning and Problem-Solving:
   Fine-tuned for high-precision mathematical problem-solving, including algebra, geometry, calculus, and logic puzzles.

2. Educational and Academic Assistance:
   Ideal for students, educators, and researchers looking for structured explanations and step-by-step solutions.

3. Conversational AI with Mathematical Focus:
   Supports intelligent chatbot applications that require mathematical comprehension and dynamic response generation.

4. Data Science and Analytical Processing:
   Capable of analyzing mathematical datasets, generating structured numerical insights, and assisting with automation.

5. Long-Form Mathematical Content Generation:
   Can generate detailed problem breakdowns, mathematical reports, and research-based content with high coherence.

Limitations

1. Hardware Requirements:
   Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.

2. Potential Bias in Responses:
   While fine-tuned for accuracy, outputs may still reflect biases present in training data.

3. Inconsistent Creative Outputs:
   May generate varying results when handling abstract or theoretical mathematical concepts.

4. Limited Real-World Awareness:
   Does not have access to real-time mathematical discoveries beyond its training cutoff.

5. Error Propagation in Extended Outputs:
   Minor calculation errors in early steps may affect overall problem solutions in long-form responses.

6. Prompt Sensitivity:
   The effectiveness of responses may depend on how well the mathematical problem is structured within the input prompt.