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--- |
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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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- unsloth/phi-4 |
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- microsoft/phi-4 |
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pipeline_tag: text-generation |
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--- |
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# Phi-4 converted for ExLlamaV3 |
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[ExLlamaV3 is an optimized quantization and inference library for running LLMs locally on modern consumer-class GPUs.](https://github.com/turboderp-org/exllamav3) |
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**This is an early preview release of ExLlamaV3.** |
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| | Quant type | File Size | Vram*| |
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| -------- | ---------- | --------- | -------- | |
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| [phi-4_3bpw](https://huggingface.co/cmh/phi-4_exl3/tree/3bpw) | 3 bits per weight | 6.53 GB | **9.4 GB** | |
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| [phi-4_4bpw](https://huggingface.co/cmh/phi-4_exl3/tree/4bpw) | 4 bits per weight | 8.24 GB | **11.0 GB** | |
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| [phi-4_5bpw](https://huggingface.co/cmh/phi-4_exl3/tree/5bpw) | 5 bits per weight | 9.94 GB | **12,6 GB** | |
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| [phi-4_6bpw](https://huggingface.co/cmh/phi-4_exl3/tree/6bpw) | 6 bits per weight | 11.65 GB | **14,2 GB** | |
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| [phi-4_7bpw](https://huggingface.co/cmh/phi-4_exl3/tree/7bpw) | 7 bits per weight | 13.35 GB | **15,8 GB** | |
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| [phi-4_8bpw](https://huggingface.co/cmh/phi-4_exl3/tree/8bpw) | 8 bits per weight | 15.05 GB | **17,3 GB** | |
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<sub>*approximate value at **16k context**.<sup> |
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--------------------------------------------- |
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# Phi-4 Model Card |
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[Phi-4 Technical Report](https://arxiv.org/pdf/2412.08905) |
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## Model Summary |
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|-------------------------|-------------------------------------------------------------------------------| |
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| **Developers** | Microsoft Research | |
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| **Description** | `phi-4` is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.<br><br>`phi-4` underwent a rigorous enhancement and alignment process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures | |
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| **Architecture** | 14B parameters, dense decoder-only Transformer model | |
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| **Context length** | 16384 tokens | |
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## Usage |
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### Input Formats |
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Given the nature of the training data, `phi-4` is best suited for prompts using the chat format as follows: |
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```bash |
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<|im_start|>system<|im_sep|> |
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You are a medieval knight and must provide explanations to modern people.<|im_end|> |
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<|im_start|>user<|im_sep|> |
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How should I explain the Internet?<|im_end|> |
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<|im_start|>assistant<|im_sep|> |
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``` |
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### With exllamav3's chat.py: |
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python examples\chat.py -m models\phi-4_exl3\4bpw -mode raw |
