metadata
license: mit
license_link: https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE
language:
- tr
pipeline_tag: text-generation
tags:
- phi
- nlp
- instruction-tuning
- turkish
- chat
- conversational
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: Internet'i nasıl açıklayabilirim?
library_name: transformers
Phi-4 Turkish Instruction-Tuned Model
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.
Model Summary
Developers | Baran Bingöl (Hugging Face: barandinho) |
Base Model | microsoft/phi-4 |
Architecture | 14B parameters, dense decoder-only Transformer |
Training Data | 55K Turkish instruction samples |
Context Length | 16K tokens |
License | MIT (License Link) |
Intended Use
Primary Use Cases
- Turkish conversational AI systems
- Chatbots and virtual assistants
- Educational tools for Turkish users
- General-purpose text generation in Turkish
Out-of-Scope Use Cases
- High-risk domains (medical, legal, financial advice) without proper evaluation
- Use in sensitive or safety-critical systems without safeguards
Usage
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|>
Sen yardımsever bir yapay zekasın.<|im_end|>
<|im_start|>user<|im_sep|>
Kuantum hesaplama neden önemlidir?<|im_end|>
<|im_start|>assistant<|im_sep|>
With transformers
import transformers
pipeline = transformers.pipeline(
"text-generation",
model="barandinho/phi4-turkish-instruct",
model_kwargs={"torch_dtype": "auto"},
device_map="auto",
)
messages = [
{"role": "system", "content": "Sen yardımsever bir yapay zekasın."},
{"role": "user", "content": "Kuantum hesaplama neden önemlidir?"},
]
outputs = pipeline(messages, max_new_tokens=128)
print(outputs[0]["generated_text"][-1])