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license: mit |
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language: |
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- en |
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base_model: |
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- microsoft/phi-4 |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- math |
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--- |
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Here's the updated `README.md` with the requested changes: |
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--- |
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# **Phi-4 o1 [ Responsible Mathematical Problem Solving & Reasoning Capabilities ]** |
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`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. |
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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**. |
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--- |
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## **Dataset Info** |
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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: |
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- **Responsible mathematical problem-solving** |
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- **Logical reasoning** |
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- **Stepwise breakdowns of complex tasks** |
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The dataset design focuses on enabling the model to generate detailed, accurate, and logically coherent solutions for mathematical and reasoning-based tasks. |
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--- |
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## **Run with Transformers** |
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To use Phi-4 o1 ft for text generation tasks, follow the example below: |
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### Example Usage |
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```python |
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# pip install accelerate |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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# Load tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Phi-4-Math-IO") |
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model = AutoModelForCausalLM.from_pretrained( |
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"prithivMLmods/Phi-4-Math-IO", |
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device_map="auto", |
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torch_dtype=torch.bfloat16, |
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) |
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# Input prompt |
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input_text = "Solve the equation: 2x + 3 = 11. Provide a stepwise solution." |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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# Generate output |
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outputs = model.generate(**input_ids, max_new_tokens=64) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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For structured dialogue generation, you can apply the chat template as follows: |
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```python |
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# Structured input for chat-style interaction |
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messages = [ |
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{"role": "user", "content": "Explain Pythagoras’ theorem with an example."}, |
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] |
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input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda") |
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# Generate response |
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outputs = model.generate(**input_ids, max_new_tokens=256) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## **Intended Use** |
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Phi-4 o1 ft is designed for a wide range of **reasoning-intensive** and **math-focused** applications. Below are some key use cases: |
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### 1. **Responsible Mathematical Problem Solving** |
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- Solving complex mathematical problems with detailed, step-by-step solutions. |
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- Assisting students, educators, and researchers in understanding advanced mathematical concepts. |
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### 2. **Reasoning and Logical Problem Solving** |
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- Breaking down intricate problems in logic, science, and other fields into manageable steps. |
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- Providing responsible and accurate reasoning capabilities for critical applications. |
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### 3. **Educational Tools** |
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- Supporting educational platforms with explanations, tutoring, and Q&A support. |
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- Generating practice problems and solutions for students. |
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### 4. **Content Creation** |
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- Assisting content creators in generating accurate and logical educational content. |
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- Helping with technical documentation by providing precise explanations. |
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### 5. **Customer Support** |
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- Automating responses to technical queries with logical stepwise solutions. |
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- Providing accurate, responsible, and coherent information for complex questions. |
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--- |
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## **Limitations** |
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While Phi-4 o1 ft is highly capable in reasoning and mathematics, users should be aware of its limitations: |
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### 1. **Bias and Fairness** |
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- Despite rigorous training, the model may still exhibit biases from its training data. Users are encouraged to carefully review outputs, especially for sensitive topics. |
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### 2. **Contextual Understanding** |
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- The model may sometimes misinterpret ambiguous or complex prompts, leading to incorrect or incomplete responses. |
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### 3. **Real-Time Knowledge** |
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- 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. |
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### 4. **Safety and Harmlessness** |
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- Although safety-aligned, the model may occasionally generate responses that require human oversight. Regular monitoring is recommended when deploying it in sensitive domains. |
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### 5. **Resource Requirements** |
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- Due to its size, running the model efficiently may require high-end computational resources, particularly for large-scale or real-time applications. |
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### 6. **Ethical Considerations** |
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- The model must not be used for malicious purposes, such as generating harmful content, misinformation, or spam. Users are responsible for ensuring ethical use. |
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### 7. **Domain-Specific Limitations** |
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- 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. |
