--- license: mit base_model: - microsoft/Phi-4-mini-instruct pipeline_tag: text-generation tags: - Phi - Phi-4 --- ## Model Summary Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures. πŸ“° [Phi-4-mini Microsoft Blog](https://aka.ms/phi4-feb2025)
πŸ“– [Phi-4-mini Technical Report](https://aka.ms/phi-4-multimodal/techreport)
πŸ‘©β€πŸ³ [Phi Cookbook](https://github.com/microsoft/PhiCookBook)
🏑 [Phi Portal](https://azure.microsoft.com/en-us/products/phi)
πŸ–₯️ Try It [Azure](https://aka.ms/phi-4-mini/azure), [Huggingface](https://huggingface.co/spaces/microsoft/phi-4-mini)
**Phi-4**: [[mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-mini-instruct-onnx)]; [multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct); [gguf](https://huggingface.co/Mungert/Phi-4-mini-instruct.gguf) ## Usage #### Chat format This format is used for general conversation and instructions: ```yaml <|system|>Insert System Message<|end|><|user|>Insert User Message<|end|><|assistant|> ``` #### Tool-Enabled Function-Calling Format This format is used when the user wants the model to provide function calls based on the given tools. The user should define the available tools in the system prompt, wrapped by `<|tool|>` and `<|/tool|>` tokens. The tools must be specified in JSON format using a structured JSON dump. ```plaintext <|system|> You are a helpful assistant with some tools. <|tool|> [ { "name": "get_weather_updates", "description": "Fetches weather updates for a given city using the RapidAPI Weather API.", "parameters": { "city": { "description": "The name of the city for which to retrieve weather information.", "type": "str", "default": "London" } } } ] <|/tool|> <|end|> <|user|> What is the weather like in Paris today? <|end|> <|assistant|> ``` --- # **Phi-4-mini-instruct GGUF Models** ## **Choosing the Right Model Format** Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**. ### **BF16 (Brain Float 16) – Use if BF16 acceleration is available** - A 16-bit floating-point format designed for **faster computation** while retaining good precision. - Provides **similar dynamic range** as FP32 but with **lower memory usage**. - Recommended if your hardware supports **BF16 acceleration** (check your device’s specs). - Ideal for **high-performance inference** with **reduced memory footprint** compared to FP32. πŸ“Œ **Use BF16 if:** βœ” Your hardware has native **BF16 support** (e.g., newer GPUs, TPUs). βœ” You want **higher precision** while saving memory. βœ” You plan to **requantize** the model into another format. πŸ“Œ **Avoid BF16 if:** ❌ Your hardware does **not** support BF16 (it may fall back to FP32 and run slower). ❌ You need compatibility with older devices that lack BF16 optimization. --- ### **F16 (Float 16) – More widely supported than BF16** - A 16-bit floating-point **high precision** but with less of range of values than BF16. - Works on most devices with **FP16 acceleration support** (including many GPUs and some CPUs). - Slightly lower numerical precision than BF16 but generally sufficient for inference. πŸ“Œ **Use F16 if:** βœ” Your hardware supports **FP16** but **not BF16**. βœ” You need a **balance between speed, memory usage, and accuracy**. βœ” You are running on a **GPU** or another device optimized for FP16 computations. πŸ“Œ **Avoid F16 if:** ❌ Your device lacks **native FP16 support** (it may run slower than expected). ❌ You have memory limtations. --- ### **Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference** Quantization reduces model size and memory usage while maintaining as much accuracy as possible. - **Lower-bit models (Q4_K)** β†’ **Best for minimal memory usage**, may have lower precision. - **Higher-bit models (Q6_K, Q8_0)** β†’ **Better accuracy**, requires more memory. πŸ“Œ **Use Quantized Models if:** βœ” You are running inference on a **CPU** and need an optimized model. βœ” Your device has **low VRAM** and cannot load full-precision models. βœ” You want to reduce **memory footprint** while keeping reasonable accuracy. πŸ“Œ **Avoid Quantized Models if:** ❌ You need **maximum accuracy** (full-precision models are better for this). ❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16). --- ### **Summary Table: Model Format Selection** | Model Format | Precision | Memory Usage | Device Requirements | Best Use Case | |--------------|------------|---------------|----------------------|---------------| | **BF16** | Highest | High | BF16-supported GPU/CPUs | High-speed inference with reduced memory | | **F16** | High | High | FP16-supported devices | GPU inference when BF16 isn’t available | | **Q4_K** | Low | Very Low | CPU or Low-VRAM devices | Best for memory-constrained environments | | **Q6_K** | Medium Low | Low | CPU with more memory | Better accuracy while still being quantized | | **Q8** | Medium | Moderate | CPU or GPU with enough VRAM | Best accuracy among quantized models | ## **Included Files & Details** ### `phi-4-mini-bf16.gguf` - Model weights preserved in **BF16**. - Use this if you want to **requantize** the model into a different format. - Best if your device supports **BF16 acceleration**. ### `phi-4-mini-f16.gguf` - Model weights stored in **F16**. - Use if your device supports **FP16**, especially if BF16 is not available. ### `phi-4-mini-bf16-q8.gguf` - **Output & embeddings** remain in **BF16**. - All other layers quantized to **Q8_0**. - Use if your device supports **BF16** and you want a quantized version. ### `phi-4-mini-f16-q8.gguf` - **Output & embeddings** remain in **F16**. - All other layers quantized to **Q8_0**. ### `phi-4-mini-q4_k_l.gguf` - **Output & embeddings** quantized to **Q8_0**. - All other layers quantized to **Q4_K**. - Good for **CPU inference** with limited memory. ### `phi-4-mini-q4_k_m.gguf` - Similar to Q4_K. - Another option for **low-memory CPU inference**. ### `phi-4-mini-q4_k_s.gguf` - Smallest **Q4_K** variant, using less memory at the cost of accuracy. - Best for **very low-memory setups**. ### `phi-4-mini-q6_k_l.gguf` - **Output & embeddings** quantized to **Q8_0**. - All other layers quantized to **Q6_K** . ### `phi-4-mini-q6_k_m.gguf` - A mid-range **Q6_K** quantized model for balanced performance . - Suitable for **CPU-based inference** with **moderate memory**. ### `phi-4-mini-q8.gguf` - Fully **Q8** quantized model for better accuracy. - Requires **more memory** but offers higher precision.