File size: 5,792 Bytes
f9d9cc1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
---
license: mit
language:
- en
base_model:
- microsoft/phi-4
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- llama
- phi3
- phi
---
# **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. |