Phi-4 converted for ExLlamaV2
ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs.
Filename | Quant type | File Size | Vram* |
---|---|---|---|
phi-4_hb8_3bpw | 3.00 bits per weight | 6.66 GB | 10,3 GB |
phi-4_hb8_4bpw | 4.00 bits per weight | 8.36 GB | 11,9 GB |
phi-4_hb8_5bpw | 5.00 bits per weight | 10.1 GB | 13,5 GB |
phi-4_hb8_6bpw | 6.00 bits per weight | 11.8 GB | 15,1 GB |
phi-4_hb8_7bpw | 7.00 bits per weight | 13.5 GB | 16,7 GB |
phi-4_hb8_8bpw | 8.00 bits per weight | 15.2 GB | 18,2 GB |
*at 16k context, FP16 cache.
Phi-4 Model Card
Model Summary
Developers | Microsoft Research |
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.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 |
Architecture | 14B parameters, dense decoder-only Transformer model |
Context length | 16384 tokens |
Usage
Input Formats
Given the nature of the training data, phi-4
is best suited for prompts using the chat format as follows:
<|im_start|>system<|im_sep|>
You are a medieval knight and must provide explanations to modern people.<|im_end|>
<|im_start|>user<|im_sep|>
How should I explain the Internet?<|im_end|>
<|im_start|>assistant<|im_sep|>
With ExUI:
Add Phi-4 prompt format:
Edit/replace exui/backend/prompts.py with https://huggingface.co/cmh/phi-4_exl2/raw/main/backend/prompts.py
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model has no library tag.
Model tree for cmh/phi-4_exl2
Base model
microsoft/phi-4