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README.md
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---
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license: mit
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license_link: https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE
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language:
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- tr
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pipeline_tag: text-generation
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tags:
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- phi
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- nlp
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- instruction-tuning
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- turkish
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- chat
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- conversational
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inference:
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parameters:
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temperature: 0.7
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widget:
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- messages:
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- role: user
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content: "Internet'i nasıl açıklayabilirim?"
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library_name: transformers
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---
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# Phi-4 Turkish Instruction-Tuned Model
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This model is a fine-tuned version of Microsoft's **Phi-4** model for Turkish instruction-following tasks. It was trained on a **55,000-sample Turkish instruction dataset**, making it well-suited for generating helpful and coherent responses in Turkish.
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## Model Summary
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| | |
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|-------------------------|-----------------------------------------------|
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| **Developers** | Baran Bingöl (Hugging Face: [barandinho](https://huggingface.co/barandinho)) |
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| **Base Model** | [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) |
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| **Architecture** | 14B parameters, dense decoder-only Transformer|
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| **Training Data** | 55K Turkish instruction samples |
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| **Context Length** | 16K tokens |
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| **License** | MIT ([License Link](https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE)) |
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## Intended Use
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### Primary Use Cases
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- Turkish conversational AI systems
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- Chatbots and virtual assistants
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- Educational tools for Turkish users
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- General-purpose text generation in Turkish
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### Out-of-Scope Use Cases
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- High-risk domains (medical, legal, financial advice) without proper evaluation
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- Use in sensitive or safety-critical systems without safeguards
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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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Sen yardımsever bir yapay zekasın.<|im_end|>
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<|im_start|>user<|im_sep|>
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Kuantum hesaplama neden önemlidir?<|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="barandinho/phi4-turkish-instruct",
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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": "Sen yardımsever bir yapay zekasın."},
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{"role": "user", "content": "Kuantum hesaplama neden önemlidir?"},
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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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