The best small sized LLM, phi-4 multimodal supports audio & vision, open-sourced
So after some many releases this month, including Grok3, Claude 3.7 Sonnet, The tech giant, Microsoft has come out with the sequel of phi3.5 i.e. phi-4. The model, according to the benchmarks, is looking great and is easily the best small sized model for now alongside a multi-modal version as well, Phi-4 multimodal that supports audio, vision and text
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What is Phi-4?
Phi-4 is a next-generation language model developed by Microsoft Research. With a rich training methodology combining synthetic datasets and carefully selected real-world data, it focuses on delivering powerful reasoning, logic, and understanding. It was trained with the aim of providing solutions for memory/compute-constrained environments, low-latency applications, and advanced reasoning scenarios.
Key Features and Architecture
- Model Architecture: Phi-4 is a 14 billion parameter dense decoder-only Transformer model. Its design is optimized to handle large-scale language processing tasks while being efficient enough to run in resource-constrained environments.
- Training & Hardware: Phi-4 was trained using 1920 H100–80G GPUs over a span of 21 days, processing 9.8 trillion tokens of data. The model is fine-tuned to prioritize high-quality outputs and advanced reasoning.
- Context Length: One of Phi-4’s standout features is its 16K token context length, enabling it to handle extensive conversations or long-form content more effectively than many other models.
- Training Data: Its data comes from a blend of publicly available documents, synthetic data, and academic books. It also includes 8% multilingual data, though its primary focus remains on English.
- The model is completely open-sourced
Performance Benchmarks
Phi-4 has been evaluated against various benchmarks to gauge its capabilities in multiple domains:
MMLU (Multitask Language Understanding): 84.8 (compared to Phi-3’s 77.9).
Mathematical Reasoning: Strong performance on MATH and MGSM tests, with scores surpassing many other leading models.
Code Generation: Phi-4 shows impressive proficiency in HumanEval, scoring 82.6, which is among the best in the industry.
Factual Knowledge: On SimpleQA, it lags behind some competitors but still performs well with a score of 3.0.
Reasoning and Comprehension: The DROP benchmark score of 75.5 demonstrates Phi-4’s solid grasp of logical reasoning.
Safety and Ethical Considerations
Phi-4 comes with robust safety mechanisms in place, leveraging both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). The model was subjected to multiple safety tests, including adversarial simulations and collaborations with Microsoft’s AI Red Team (AIRT). These measures ensure that the model minimizes harmful outputs, such as misinformation and biased content, though developers are encouraged to take additional safety precautions for specific use cases.
Challenges and Limitations
Despite its impressive capabilities, Phi-4 is not without its challenges. Some of its limitations include:
Multilingual Support: While it incorporates some multilingual data, Phi-4 is not ideal for non-English tasks.
Representation and Bias: As with any AI trained on publicly available data, there is the potential for biases in terms of how certain groups or ideas are represented.
Reliability: Language models like Phi-4 may sometimes generate inaccurate or nonsensical content, especially in high-risk domains.
Before we end,
Microsoft Phi-4 Multimodal
Microsoft’s Phi-4 Multimodal LLM builds on the success of the base Phi-4 model, adding new capabilities to process not just text, but also multimodal inputs. This extension allows Phi-4 to handle a wider variety of data types, such as images and other non-textual forms of information, alongside its core strength in natural language processing. Here’s a concise overview of its multimodal features.
Key Features of Phi-4 Multimodal LLM
- Multimodal Input Processing: Unlike the base Phi-4, which operates solely on text inputs, the multimodal variant expands to include images and possibly other data types. This allows the model to engage in tasks that require understanding and generating responses based on multiple forms of input.
- Unified Model for Text and Image: Phi-4’s multimodal version is designed to interpret and generate content that combines both textual and visual information. This opens up new use cases, including tasks like:
Image Captioning: Generating accurate and contextually relevant captions for images.
Visual Question Answering: Answering questions based on the content of images.
Cross-Modal Reasoning: Combining information from text and images to form coherent responses or insights.
3. Contextual Understanding Across Modalities: The model can utilize its 16K token context length for understanding and generating responses that draw on both visual and textual context. This capacity allows for deeper reasoning and more nuanced outputs in tasks that involve complex relationships between text and images.
4. Training Methodology: Phi-4’s multimodal capabilities are built on the same core principles as the original model but are trained with additional image-text pairs and multimodal datasets. This training ensures that the model can align and integrate information from both modalities effectively.
5. Performance Benchmarks: As the multimodal extension is still a relatively new advancement, performance benchmarks for this version are still emerging. However, given the model’s core capabilities and large training data set, it’s expected to excel in tasks requiring both text comprehension and visual processing.
How to use Phi-4?
Both Phi-4 and Phi-4 Multimodal are open-sourced and the weights with codes are available on HuggingFace
Conclusion
Microsoft’s Phi-4 and Phi-4 Multimodal LLM represent significant advances in AI, offering powerful language understanding and multimodal capabilities. Phi-4 excels in reasoning, logic, and safety across tasks like math, code generation, and science. The multimodal version integrates text and image inputs, enabling more context-aware responses. Both models are built for efficiency and responsibility, setting a new standard for AI-driven solutions across industries.
Microsoft Phi-4: The small sized LLM King is back was originally published in Data Science in your pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
