duyntnet/phi-4-imatrix-GGUF

Quantizations of https://huggingface.co/microsoft/phi-4

Inference Clients/UIs

• llama.cpp
• KoboldCPP
• ollama
• jan
• text-generation-webui
• GPT4All

---

From original readme

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
Inputs	Text, best suited for prompts in the chat format
Context length	16K tokens
GPUs	1920 H100-80G
Training time	21 days
Training data	9.8T tokens
Outputs	Generated text in response to input
Dates	October 2024 – November 2024
Status	Static model trained on an offline dataset with cutoff dates of June 2024 and earlier for publicly available data
Release date	December 12, 2024
License	MIT

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 transformers

import transformers

pipeline = transformers.pipeline(
    "text-generation",
    model="microsoft/phi-4",
    model_kwargs={"torch_dtype": "auto"},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a medieval knight and must provide explanations to modern people."},
    {"role": "user", "content": "How should I explain the Internet?"},
]

outputs = pipeline(messages, max_new_tokens=128)
print(outputs[0]["generated_text"][-1])