This model is an extended version of a Mistral-based Large Language Model (LLM) for Turkish. It was trained on a cleaned Turkish raw dataset containing 5 billion tokens. The training process involved using the DORA method initially. Following this, we utilized Turkish instruction sets created from various open-source and internal resources for fine-tuning with the LORA method.

Model Details

Base Model: Mistral 7B based LLM
Tokenizer Extension: Specifically extended for Turkish
Training Dataset: Cleaned Turkish raw data with 5 billion tokens, custom Turkish instruction sets
Training Method: Initially with DORA, followed by fine-tuning with LORA

DORA Configuration

lora_alpha: 128
lora_dropout: 0.05
r: 64
target_modules: "all-linear"

LORA Fine-Tuning Configuration

lora_alpha: 128
lora_dropout: 0.05
r: 256
target_modules: "all-linear"

Usage Examples

python

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

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

messages = [
    {"role": "user", "content": "Türkiye'nin başkenti neresidir?"},
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

eos_token = tokenizer("<|im_end|>",add_special_tokens=False)["input_ids"][0]

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, 
                               max_new_tokens=1024, 
                               do_sample=True, 
                               eos_token_id=eos_token)
                               
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

RichardErkhov/TURKCELL_-_Turkcell-LLM-7b-v1-8bits

Original model description:

Turkcell-LLM-7b-v1

Model Details

DORA Configuration

LORA Fine-Tuning Configuration

Usage Examples

RichardErkhov/
TURKCELL_-_Turkcell-LLM-7b-v1-8bits