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, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("barandinho/phi4-turkish-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.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

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
```

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`

Below code uses 4-bit quantization (INT4) to run the model more efficiently with lower memory usage, which is especially useful for environments with limited GPU memory like Google Colab. Keep in mind that the model will take some time to download initially.

Check this notebook for interactive usage of the model.

import os
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
import torch

model_name = "barandinho/phi4-turkish-instruct"

quant_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True)

os.makedirs("offload", exist_ok=True)

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.float16,
    quantization_config=quant_config,
    offload_folder="offload"
)

messages = [ 
    {"role": "system", "content": "Sen yardımsever bir yapay zekasın."}, 
    {"role": "user", "content": "Kuantum hesaplama neden önemlidir, basit terimlerle açıklayabilir misin?"}, 
] 

pipe = pipeline( 
    "text-generation", 
    model=model, 
    tokenizer=tokenizer
) 

generation_args = { 
    "max_new_tokens": 500, 
    "return_full_text": False, 
    "temperature": 0.0, 
    "do_sample": False, 
} 

output = pipe(messages, **generation_args) 
print(output[0]['generated_text'])

Downloads last month: 13

Safetensors

Model size

15B params

Tensor type

BF16