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README.md
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[ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs.](https://github.com/turboderp-org/exllamav2)
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| Filename | Quant type | File Size |
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| -------- | ---------- | --------- |
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| [phi-4_hb8_3bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_3bpw) | 3.00 bits per weight | 6.66 GB |
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| [phi-4_hb8_4bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_4bpw) | 4.00 bits per weight | 8.36 GB |
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| [phi-4_hb8_5bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_5bpw) | 5.00 bits per weight | 10.1 GB |
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| [phi-4_hb8_6bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_6bpw) | 6.00 bits per weight | 11.8 GB |
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| [phi-4_hb8_7bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_7bpw) | 7.00 bits per weight | 13.5 GB |
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| [phi-4_hb8_8bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_8bpw) | 8.00 bits per weight | 15.2 GB |
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# Phi-4 Model Card
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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** | 16K tokens |
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## Usage
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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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[ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs.](https://github.com/turboderp-org/exllamav2)
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| Filename | Quant type | File Size | Vram at 16k context|
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| -------- | ---------- | --------- |
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| [phi-4_hb8_3bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_3bpw) | 3.00 bits per weight | 6.66 GB | 10,3 GB |
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| [phi-4_hb8_4bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_4bpw) | 4.00 bits per weight | 8.36 GB | 11,9 GB |
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| [phi-4_hb8_5bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_5bpw) | 5.00 bits per weight | 10.1 GB | 13,5 GB |
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| [phi-4_hb8_6bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_6bpw) | 6.00 bits per weight | 11.8 GB | 15,1 GB |
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| [phi-4_hb8_7bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_7bpw) | 7.00 bits per weight | 13.5 GB | 16,7 GB |
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| [phi-4_hb8_8bpw](https://huggingface.co/cmh/phi-4_exl2/tree/hb8_8bpw) | 8.00 bits per weight | 15.2 GB | 18,2 GB |
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# Phi-4 Model Card
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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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<|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 ExUI:
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edit exui/backend/prompts.py
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```
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class PromptFormat_phi4(PromptFormat):
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description = "Phi-4 format"
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def __init__(self):
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super().__init__()
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pass
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def is_instruct(self):
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return True
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def stop_conditions(self, tokenizer, settings):
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return \
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[tokenizer.eos_token_id,
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"""<|im_end|>"""]
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def format(self, prompt, response, system_prompt, settings):
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text = ""
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if system_prompt and system_prompt.strip() != "":
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text += "<|im_start|>system\n"
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text += system_prompt
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text += "\n<|im_end|>\n"
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text += "<|im_start|>user\n"
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text += prompt
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text += "<|im_end|>\n"
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text += "<|im_start|>assistant\n"
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if response:
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text += response
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text += "<|im_end|>\n"
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return text
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def context_bos(self):
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return True
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prompt_formats = \
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{
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"Chat-RP": PromptFormat_raw,
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"Llama-chat": PromptFormat_llama,
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"Llama3-instruct": PromptFormat_llama3,
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"ChatML": PromptFormat_chatml,
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"TinyLlama-chat": PromptFormat_tinyllama,
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"MistralLite": PromptFormat_mistrallite,
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"Phind-CodeLlama": PromptFormat_phind_codellama,
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"Deepseek-chat": PromptFormat_deepseek_chat,
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"Deepseek-instruct": PromptFormat_deepseek_instruct,
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"OpenChat": PromptFormat_openchat,
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"Gemma": PromptFormat_gemma,
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"Cohere": PromptFormat_cohere,
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"Phi3-instruct": PromptFormat_phi3,
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"Phi4": PromptFormat_phi4,
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"Granite": PromptFormat_granite,
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"Mistral V1": PromptFormat_mistralv1,
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"Mistral V2/V3": PromptFormat_mistralv2v3,
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"Mistral V3 (Tekken)": PromptFormat_mistralTekken,
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}
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```
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