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---
license: mit
language:
- en
base_model:
- microsoft/phi-4
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- math
---
Here's the updated `README.md` with the requested changes:
---
# **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
```python
# 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:
```python
# 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. |