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Phi-4 o1 [ Responsible Mathematical Problem Solving & Reasoning Capabilities ]

Phi-4 o1 [ Responsible Mathematical Problem Solving & Reasoning Capabilities ] is a state-of-the-art open model fine-tuned on advanced reasoning tasks. It is based on Microsoft’s Phi-4, built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The primary focus is to create a small, capable model that excels in responsible reasoning and mathematical problem-solving with high-quality data.

The Phi-4 o1 model has undergone robust safety post-training using a combination of SFT (Supervised Fine-Tuning) and iterative DPO (Direct Preference Optimization) techniques. The safety alignment process includes publicly available datasets and proprietary synthetic datasets to improve helpfulness, harmlessness, and responsible AI usage.

Dataset Info

Phi-4 o1 ft is fine-tuned on a synthetic dataset curated through a specially designed pipeline. The dataset leverages the Math IO (Input-Output) methodology and step-by-step problem-solving approaches. This ensures the model is highly effective in:

• Responsible mathematical problem-solving
• Logical reasoning
• Stepwise breakdowns of complex tasks

The dataset design focuses on enabling the model to generate detailed, accurate, and logically coherent solutions for mathematical and reasoning-based tasks.

Run with Transformers

To use Phi-4 o1 ft for text generation tasks, follow the example below:

Example Usage

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Phi-4-Math-IO")
model = AutoModelForCausalLM.from_pretrained(
    "prithivMLmods/Phi-4-Math-IO",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

# Input prompt
input_text = "Solve the equation: 2x + 3 = 11. Provide a stepwise solution."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

# Generate output
outputs = model.generate(**input_ids, max_new_tokens=64)
print(tokenizer.decode(outputs[0]))

For structured dialogue generation, you can apply the chat template as follows:

# Structured input for chat-style interaction
messages = [
    {"role": "user", "content": "Explain Pythagoras’ theorem with an example."},
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")

# Generate response
outputs = model.generate(**input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

Intended Use

Phi-4 o1 ft is designed for a wide range of reasoning-intensive and math-focused applications. Below are some key use cases:

1. Responsible Mathematical Problem Solving

• Solving complex mathematical problems with detailed, step-by-step solutions.
• Assisting students, educators, and researchers in understanding advanced mathematical concepts.

2. Reasoning and Logical Problem Solving

• Breaking down intricate problems in logic, science, and other fields into manageable steps.
• Providing responsible and accurate reasoning capabilities for critical applications.

3. Educational Tools

• Supporting educational platforms with explanations, tutoring, and Q&A support.
• Generating practice problems and solutions for students.

4. Content Creation

• Assisting content creators in generating accurate and logical educational content.
• Helping with technical documentation by providing precise explanations.

5. Customer Support

• Automating responses to technical queries with logical stepwise solutions.
• Providing accurate, responsible, and coherent information for complex questions.

Limitations

While Phi-4 o1 ft is highly capable in reasoning and mathematics, users should be aware of its limitations:

1. Bias and Fairness

• Despite rigorous training, the model may still exhibit biases from its training data. Users are encouraged to carefully review outputs, especially for sensitive topics.

2. Contextual Understanding

• The model may sometimes misinterpret ambiguous or complex prompts, leading to incorrect or incomplete responses.

3. Real-Time Knowledge

• The model’s knowledge is static, reflecting only the data it was trained on. It does not have real-time information about current events or post-training updates.

4. Safety and Harmlessness

• Although safety-aligned, the model may occasionally generate responses that require human oversight. Regular monitoring is recommended when deploying it in sensitive domains.

5. Resource Requirements

• Due to its size, running the model efficiently may require high-end computational resources, particularly for large-scale or real-time applications.

6. Ethical Considerations

• The model must not be used for malicious purposes, such as generating harmful content, misinformation, or spam. Users are responsible for ensuring ethical use.

7. Domain-Specific Limitations

• Although effective in general-purpose reasoning and math tasks, the model may require further fine-tuning for highly specialized domains such as medicine, law, or finance.