Instructions to use barandinho/phi4-turkish-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use barandinho/phi4-turkish-instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="barandinho/phi4-turkish-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("barandinho/phi4-turkish-instruct", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("barandinho/phi4-turkish-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use barandinho/phi4-turkish-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "barandinho/phi4-turkish-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "barandinho/phi4-turkish-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/barandinho/phi4-turkish-instruct

SGLang

How to use barandinho/phi4-turkish-instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "barandinho/phi4-turkish-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "barandinho/phi4-turkish-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "barandinho/phi4-turkish-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "barandinho/phi4-turkish-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use barandinho/phi4-turkish-instruct with Docker Model Runner:
```
docker model run hf.co/barandinho/phi4-turkish-instruct
```

barandinho commited on Jan 17, 2025

Commit

878cd6d

verified ·

1 Parent(s): fcab1a8

Create README.md

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	@@ -0,0 +1,84 @@

+---
+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](https://huggingface.co/barandinho)) |
+| **Base Model**          | [microsoft/phi-4](https://huggingface.co/microsoft/phi-4)                              |
+| **Architecture**        | 14B parameters, dense decoder-only Transformer|
+| **Training Data**       | 55K Turkish instruction samples              |
+| **Context Length**      | 16K tokens                                   |
+| **License**             | MIT ([License Link](https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE)) |
+## 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:
+```bash
+<|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`
+```python
+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])
+```