---
library_name: transformers
license: mit
datasets:
- kuotient/gsm8k-ko
- lilacai/glaive-function-calling-v2-sharegpt
- >-
  Saxo/en_ko_translation_social_science_linkbricks_single_dataset_with_prompt_text_huggingface
base_model:
- microsoft/phi-4
pipeline_tag: text-generation
---

# AXCXEPT/EZO-phi-4-sft7_12000

<!-- Provide a quick summary of what the model is/does. -->

## Usage

### 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": "あなたは優秀なAIです。丁寧な日本で、よく考えたうえで回答してください。"},
    {"role": "user", "content": "太郎くんはりんごを5つ持っています。彼はさらに2つのりんごの箱を買いました。1つの箱には3つのりんごが入っています。太郎くんは何個のりんごを持っていますか？"},
]

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