---
license: apache-2.0
pipeline_tag: text-generation
datasets:
- madrylab/gsm8k-platinum
tags:
- sft
- text-generation-inference
- math
- vLLM
- trl
library_name: transformers
language:
- en
base_model:
- prithivMLmods/Porpoise-Opus-14B-Exp
---

![zsdxvdsfvzds.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/pMkSKkDe-VPmXA00ZkGdc.png)

# **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:  

```python
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.