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---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- phi-4
---
Quantizations of https://huggingface.co/microsoft/phi-4
### Inference Clients/UIs
* [llama.cpp](https://github.com/ggerganov/llama.cpp)
* [KoboldCPP](https://github.com/LostRuins/koboldcpp)
* [ollama](https://github.com/ollama/ollama)
* [jan](https://github.com/janhq/jan)
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [GPT4All](https://github.com/nomic-ai/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.<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 |
| **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:
```bash
<|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`
```python
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])
``` |