Instructions to use TURKCELL/Turkcell-LLM-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TURKCELL/Turkcell-LLM-7b-v1 with Transformers:

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

pipe = pipeline("text-generation", model="TURKCELL/Turkcell-LLM-7b-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TURKCELL/Turkcell-LLM-7b-v1")
model = AutoModelForCausalLM.from_pretrained("TURKCELL/Turkcell-LLM-7b-v1")
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 TURKCELL/Turkcell-LLM-7b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TURKCELL/Turkcell-LLM-7b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TURKCELL/Turkcell-LLM-7b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TURKCELL/Turkcell-LLM-7b-v1

SGLang

How to use TURKCELL/Turkcell-LLM-7b-v1 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 "TURKCELL/Turkcell-LLM-7b-v1" \
    --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": "TURKCELL/Turkcell-LLM-7b-v1",
		"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 "TURKCELL/Turkcell-LLM-7b-v1" \
        --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": "TURKCELL/Turkcell-LLM-7b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TURKCELL/Turkcell-LLM-7b-v1 with Docker Model Runner:
```
docker model run hf.co/TURKCELL/Turkcell-LLM-7b-v1
```

Add 4-bit quantization and automatic device mapping for improved performance.

by notbdq - opened Apr 6, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+41

-0

notbdq

Apr 6, 2024

Merhabalar, öncelikle tebrik ederim mükemmel bir çalışma olmuş, pull request olarak readme’e inference için 4 bit quantization ve modeli sistemdeki tüm ekran kartlarına ve rama otomatik yükleme kodu ekledim bu sayede kullanıcılar performans azalmadan daha hızlı ve verimli bir şekilde kullanabilirler.

Add 4-bit quantization and automatic device mapping for improved performance.509e8049

mertzkul

Apr 6, 2024

Elinize sağlık fakat modelin verdiği cevaplar quantization'a çok uygun olmadığı için önemli ölçüde etkileniyor, deneyen arkadaşlarımızın bilgisi olsun. Tekrardan hem Turkcell AI ekibine ve size teşekkür ederim...

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