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--- |
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library_name: transformers |
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license: mit |
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datasets: |
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- kuotient/gsm8k-ko |
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- lilacai/glaive-function-calling-v2-sharegpt |
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- >- |
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Saxo/en_ko_translation_social_science_linkbricks_single_dataset_with_prompt_text_huggingface |
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base_model: |
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- microsoft/phi-4 |
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pipeline_tag: text-generation |
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--- |
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# AXCXEPT/EZO-phi-4-sft5_3500 |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Usage |
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### Input Formats |
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Given the nature of the training data, `phi-4` is best suited for prompts using the chat format as follows: |
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```bash |
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<|im_start|>system<|im_sep|> |
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You are a medieval knight and must provide explanations to modern people.<|im_end|> |
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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 `transformers` |
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```python |
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import transformers |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model="microsoft/phi-4", |
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model_kwargs={"torch_dtype": "auto"}, |
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device_map="auto", |
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) |
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messages = [ |
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{"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。"}, |
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{"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？"}, |
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] |
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outputs = pipeline(messages, max_new_tokens=128) |
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print(outputs[0]["generated_text"][-1]) |
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``` |
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