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---
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
---
# **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. |