Instructions to use umarigan/Trendyol-LLM-7b-chat-v0.1-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umarigan/Trendyol-LLM-7b-chat-v0.1-DPO with Transformers:

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

pipe = pipeline("text-generation", model="umarigan/Trendyol-LLM-7b-chat-v0.1-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("umarigan/Trendyol-LLM-7b-chat-v0.1-DPO")
model = AutoModelForCausalLM.from_pretrained("umarigan/Trendyol-LLM-7b-chat-v0.1-DPO")
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 umarigan/Trendyol-LLM-7b-chat-v0.1-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "umarigan/Trendyol-LLM-7b-chat-v0.1-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umarigan/Trendyol-LLM-7b-chat-v0.1-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/umarigan/Trendyol-LLM-7b-chat-v0.1-DPO

SGLang

How to use umarigan/Trendyol-LLM-7b-chat-v0.1-DPO 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 "umarigan/Trendyol-LLM-7b-chat-v0.1-DPO" \
    --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": "umarigan/Trendyol-LLM-7b-chat-v0.1-DPO",
		"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 "umarigan/Trendyol-LLM-7b-chat-v0.1-DPO" \
        --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": "umarigan/Trendyol-LLM-7b-chat-v0.1-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use umarigan/Trendyol-LLM-7b-chat-v0.1-DPO with Docker Model Runner:
```
docker model run hf.co/umarigan/Trendyol-LLM-7b-chat-v0.1-DPO
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Umar Igan
Model type: LLama-2-7B-chat
Language(s) (NLP): Turkish
Finetuned from model: Trendyol-LLM-7b-chat-v0.1

How to Get Started with the Model

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

pipe = pipeline("text-generation", model="umarigan/Trendyol-LLM-7b-chat-v0.1-DPO")

# Generate text
sequences = pipe(
    "büyük dil modellerinin finans alanındaki kullanımları nelerdir",
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    num_return_sequences=1,
    max_length=200,
)
print(sequences[0]['generated_text'])

Question: büyük dil modellerinin finans alanındaki kullanımları nelerdir?
Answer: Çok büyük dil modelleri, özellikle de Transformer gibi, karmaşık dil görevlerinin üstesinden gelmek için tasarlanmışlardır. Bu, finansal piyasalardaki veri işleme, fiyat tahmini ve analizleri, finansal haberler ve raporlama gibi süreçleri içerir. Ayrıca, büyük dil modelleri, doğal dil işleme, metin sınıflandırma ve soru cevaplama gibi görevlerin yanı sıra, müşteri hizmetleri gibi insan etkileşimi gerektiren finansal hizmetlerde de kullanılmaktadır.

Training Details

Training Data

This model trained on falcon instruction dataset that translated to Turkis language Dataset: https://huggingface.co/datasets/umarigan/falcon_feedback_instraction_Turkish

Training Hyperparameters

Some training arguments are as follow:
max_prompt_length=1024,
max_length=1536,
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
gradient_checkpointing=True,
learning_rate=5e-5,
lr_scheduler_type="cosine",
max_steps=200,
save_strategy="no",
logging_steps=1,
output_dir=new_model,
optim="paged_adamw_32bit",
warmup_steps=100,
fp16=True,

wandb results: https://api.wandb.ai/links/umar-i-gan/0hnrvrdq

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Safetensors

Model size

7B params

Tensor type

F16

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