README.md

---
license: mit
language:
- en
base_model:
- microsoft/phi-4
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- llama
- phi3
- phi
---
![3.png](https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/UV-0cgT9xB7-l0GZIiZv7.png)

# **Phi-4-QwQ [ Responsible Problem Solving & Advanced Reasoning ]**

`[Phi-4-QwQ finetuned]` from Microsoft's Phi-4 is a state-of-the-art open model developed with a focus on **responsible problem solving** and **advanced reasoning capabilities**. Built upon a diverse blend of synthetic datasets, carefully filtered public domain websites, and high-quality academic books and Q&A datasets, Phi-4-QwQ ensures that small, capable models are trained with datasets of exceptional depth and precision.

Phi-4-QwQ adopts a robust **safety post-training approach** using open-source and in-house synthetic datasets. This involves a combination of **SFT (Supervised Fine-Tuning)** and iterative **DPO (Direct Preference Optimization)** techniques, ensuring helpful and harmless outputs across various safety categories.

---

# **Dataset Info**

Phi-4-QwQ is fine-tuned on a carefully curated synthetic dataset generated using an advanced pipeline optimized for **Chain of Thought (CoT)** reasoning and **Responsible Problem Breakdown (RPB)** methodologies. This ensures that the model excels at:

- **Logical reasoning**  
- **Step-by-step problem-solving**  
- **Breaking down complex tasks into manageable parts**  

The dataset also emphasizes responsible decision-making and fairness in generating solutions.

---

# **Run with Transformers**

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

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Phi-4-QwQ")
model = AutoModelForCausalLM.from_pretrained(
    "prithivMLmods/Phi-4-QwQ",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Explain the concept of black holes."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

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

For chat-style interactions, use `tokenizer.apply_chat_template`:

```python
messages = [
    {"role": "user", "content": "Explain the concept of black holes."},
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")

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

# **Intended Use**

Phi-4-QwQ is tailored for a wide range of applications, especially those involving **advanced reasoning**, **multilingual capabilities**, and **responsible problem-solving**. Its primary use cases include:

1. **Responsible Problem Solving**  
   - Breaking down complex problems into logical, actionable steps.  
   - Offering ethical, well-rounded solutions in academic and professional contexts.  

2. **Advanced Reasoning Tasks**  
   - Excelling in mathematics, logic, and scientific reasoning.  
   - Providing detailed explanations and systematic answers.  

3. **Content Generation**  
   - Assisting in generating high-quality content for various domains, including creative writing and technical documentation.  
   - Supporting marketers, writers, and educators with detailed and well-structured outputs.  

4. **Educational Support**  
   - Acting as a virtual tutor for students by generating practice questions, answers, and detailed explanations.  
   - Helping educators design learning material that promotes critical thinking and step-by-step problem-solving.  

5. **Customer Support & Dialogue Systems**  
   - Enabling chatbots and virtual assistants to provide accurate, helpful, and responsible responses.  
   - Enhancing customer service with reasoning-driven automation.  

6. **Multilingual Capabilities**  
   - Supporting multilingual communication and content generation while maintaining contextual accuracy.  
   - Assisting in translations with a focus on retaining meaning and nuance.

7. **Safety-Critical Applications**  
   - Ensuring safe and harmless outputs, making it suitable for sensitive domains.  
   - Providing aligned interactions with human oversight for critical systems.

---

# **Limitations**

Despite its strengths, Phi-4-QwQ has some limitations that users should be aware of:

1. **Bias and Fairness**  
   - While great effort has been made to minimize biases, users should critically assess the model’s output in sensitive scenarios to avoid unintended bias.  

2. **Contextual Interpretation**  
   - The model may occasionally misinterpret highly nuanced prompts or ambiguous contexts, leading to suboptimal responses.  

3. **Knowledge Cutoff**  
   - Phi-4-QwQ’s knowledge is static and based on the data available at the time of training. It does not include real-time updates or information on recent developments.  

4. **Safety and Harmlessness**  
   - Despite post-training safety alignment, inappropriate or harmful outputs may still occur. Continuous monitoring and human oversight are advised when using the model in critical contexts.  

5. **Computational Requirements**  
   - Deploying Phi-4-QwQ efficiently may require substantial computational resources, particularly for large-scale deployments or real-time applications.  

6. **Ethical Considerations**  
   - Users are responsible for ensuring that the model is not employed for malicious purposes, such as spreading misinformation, generating harmful content, or facilitating unethical behavior.  

7. **Domain-Specific Expertise**  
   - While the model is versatile, it may not perform optimally in highly specialized domains (e.g., law, medicine, finance) without further domain-specific fine-tuning.