Instructions to use Eurdem/Defne-llama3.1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Eurdem/Defne-llama3.1-8B with Transformers:

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

pipe = pipeline("text-generation", model="Eurdem/Defne-llama3.1-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Eurdem/Defne-llama3.1-8B")
model = AutoModelForCausalLM.from_pretrained("Eurdem/Defne-llama3.1-8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Eurdem/Defne-llama3.1-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Eurdem/Defne-llama3.1-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Eurdem/Defne-llama3.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Eurdem/Defne-llama3.1-8B

SGLang

How to use Eurdem/Defne-llama3.1-8B 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 "Eurdem/Defne-llama3.1-8B" \
    --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": "Eurdem/Defne-llama3.1-8B",
		"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 "Eurdem/Defne-llama3.1-8B" \
        --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": "Eurdem/Defne-llama3.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Eurdem/Defne-llama3.1-8B with Docker Model Runner:
```
docker model run hf.co/Eurdem/Defne-llama3.1-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct, trained on Turkish dataset (~4 mio tokens). Then it is merged with VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct.

💻 Kullanım/How to Use

!pip install -qU transformers bitsandbytes accelerate

import transformers
import torch

model_id = "Eurdem/Defne-llama3.1-8B"

pipeline = transformers.pipeline(
                                "text-generation",
                                model=model_id,
                                model_kwargs={"torch_dtype": torch.bfloat16, "load_in_8bit": True},
                                device_map="auto",
                                )

## For English
messages = [{"role": "system", "content": "You are a helpful chatbot, named Defne, who always responds friendly."},
            {"role": "user", "content": "Answer the questions: 1) Who are you? 2) f(x)=3x^2+4x+12 so what is f(3)?"},
]

## For Turkish
messages = [{"role": "system", "content": "Sen, Defne isimli Türkçe konuşan bir chatbotsun."},
            {"role": "user", "content": "Soruları numaralandırarak cevapla. 1) Sen kimsin?  2) f(x)=3x^2+4x+12 ise f(3) kaçtır?"}
]

outputs = pipeline(
                  messages,
                  max_new_tokens=1024,
                  do_sample=True,
                  temperature=0.5,
                  top_p=0.5,
                  top_k=100,
                )

print(outputs[0]["generated_text"][-1]["content"])

English Output

Hello there! I'm Defne, a friendly chatbot here to help you with any questions or tasks you might have.

Now, let's answer your questions:

1. I am Defne, a helpful chatbot designed to provide friendly and informative responses to your queries. I'm always happy to assist you with anything you need help with!

2. To find f(3) for the given function f(x) = 3x^2 + 4x + 12, we simply need to substitute x with 3 in the function.

f(3) = 3(3)^2 + 4(3) + 12
     = 3(9) + 12 + 12
     = 27 + 12 + 12
     = 51

So, f(3) equals 51.

Türkçe Çıktı

1) Ben Defne, Türkçe konuşan bir chatbotum. İnsanlarla sohbet etmek ve onlara yardımcı olmak için tasarlanmış bir yapay zekâ uygulamasıyım.

2) f(x) = 3x^2 + 4x + 12 formülüne x = 3 değerini koyarsak:
f(3) = 3(3)^2 + 4(3) + 12
f(3) = 3(9) + 12 + 12
f(3) = 27 + 12 + 12
f(3) = 51
Sonuç olarak, f(3) = 51'dir.

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Safetensors

Model size

8B params

Tensor type

BF16

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Quantizations

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Eurdem
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Defne-llama3.1-8B

💻 Kullanım/How to Use

English Output

Türkçe Çıktı

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