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)

# pip install -U transformers accelerate
# 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=256)
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
```

Kumru-2B-EPDK-Instruct / README.md

ogulcanakca

Update README.md

49f853e verified 10 months ago

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	---
	base_model: vngrs-ai/Kumru-2B-Base
	license: apache-2.0
	tags:
	- transformers
	- dpo
	- dapt
	- kumru
	- epdk
	- causal-lm
	- text-generation-inference
	- unsloth
	- mistral
	- trl
	language:
	- tr
	datasets:
	- ogulcanakca/epdk_dpo
	- ogulcanakca/epdk_corpus
	pipeline_tag: text-generation
	library_name: transformers
	model-index:
	- name: Kumru-2B-EPDK-Instruct
	results:
	- task:
	name: EPDK Domain Instruction Following
	type: text-generation
	args:
	num_samples: 60
	evaluation_method: "LLM-as-a-Judge (Gemini 2.5 Flash)"
	metrics:
	- name: ogulcanakca/Kumru-2B-EPDK-Instruct Answer Win Rate
	type: win_rate
	value: 0.717
	- name: vngrs-ai/Kumru-2B Answer Average
	type: custom_metric
	value: 0.367
	- name: ogulcanakca/Kumru-2B-EPDK-Instruct Answer Average
	type: custom_metric
	value: 0.643

	---

	# 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`](https://huggingface.co/ogulcanakca/Kumru-2B-EPDK-Instruct-GGUF) reposunda GGUF formatında da mevcuttur.

	```python
	!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](https://huggingface.co/ogulcanakca/Kumru-2B-EPDK-Instruct-GGUF)

	## WandB

	[WandB report](https://api.wandb.ai/links/ogulcanakca-none/z06caml6)

	```json
	{
	"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."
	}
	```