Instructions to use ogulcanakca/Kumru-2B-EPDK-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ogulcanakca/Kumru-2B-EPDK-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ogulcanakca/Kumru-2B-EPDK-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ogulcanakca/Kumru-2B-EPDK-Instruct")
model = AutoModelForCausalLM.from_pretrained("ogulcanakca/Kumru-2B-EPDK-Instruct", device_map="auto")
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 ogulcanakca/Kumru-2B-EPDK-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ogulcanakca/Kumru-2B-EPDK-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": "ogulcanakca/Kumru-2B-EPDK-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ogulcanakca/Kumru-2B-EPDK-Instruct

SGLang

How to use ogulcanakca/Kumru-2B-EPDK-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 "ogulcanakca/Kumru-2B-EPDK-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": "ogulcanakca/Kumru-2B-EPDK-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 "ogulcanakca/Kumru-2B-EPDK-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": "ogulcanakca/Kumru-2B-EPDK-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use ogulcanakca/Kumru-2B-EPDK-Instruct with Docker Model Runner:
```
docker model run hf.co/ogulcanakca/Kumru-2B-EPDK-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card

vngrs-ai/Kumru-2B-Base modelinin iki aşamalı (DAPT + DPO) bir fine-tuning sürecinden geçirilmesiyle oluşturulmuş nihai modelidir. Model, Enerji Piyasası Düzenleme Kurumu (EPDK) mevzuatları konusunda uzmanlaşmış bir instruct modelidir.

Bu model, llama.cpp ve Ollama gibi platformlarda kullanılmak üzere ogulcanakca/Kumru-2B-EPDK-Instruct-GGUF reposunda GGUF formatında da mevcuttur.

!pip install -q \
    "transformers" \
    "peft" \
    "accelerate" \
    "bitsandbytes" \
    "trl" \
    "datasets"

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

model_name = "ogulcanakca/Kumru-2B-EPDK-Instruct"

# 4-bit QLoRA ile yükleme
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16 # T4 için
)

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)
model.eval()

prompt_soru = "2007 yılına ait Türkiye Ortalama Elektrik Toptan Satış Fiyatının (TORETOSAF) değeri nedir?"

messages = [
    {"role": "user", "content": prompt_soru}
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

inputs.pop("token_type_ids", None)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        temperature=0.2,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response_start_index = inputs.input_ids.shape[1]
print(tokenizer.decode(outputs[0][response_start_index:], skip_special_tokens=True))

GGUF (llama.cpp)

GGUF Reposu: 👉 ogulcanakca/Kumru-2B-EPDK-Instruct-GGUF

WandB

WandB report

{
  "prompt": "2007 yılına ait Türkiye Ortalama Elektrik Toptan Satış Fiyatının (TORETOSAF) değeri nedir?",
  "Instruct Model": "TORETOSAF, 2013 yılı için, 12 aylık TÜFE ile TEFE arasındaki farktır. 2013 yılı için % 10,11’dir."
}