metadata
license: llama3
language:
- tr
CERE V2 -LLMA-3.1-8b-TR
This model is an fine-tuned version of a Llama3.1 8b Large Language Model (LLM) for Turkish. It was trained on a high quality Turkish instruction sets created from various open-source and internal resources. Turkish Instruction dataset carefully annotated to carry out Turkish instructions in an accurate and organized manner.
Model Details
- Base Model: LLMA 3.1 8B 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
Benchmark Results
- Winogrande_tr: 56.16
- TruthfulQA_tr_v0.2: 47.46
- Mmlu_tr_v0.2: 46.46
- HellaSwag_tr_v0.2: 48.87
- GSM8k_tr_v0.2: 25.43
- Arc_tr_v0.2: 41.97
Usage Examples
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Cerebrum/cere-llama-3.1-8B-tr",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Cerebrum/cere-llama-3.1-8B-tr")
prompt = "Python'da ekrana 'Merhaba Dünya' nasıl yazılır?"
messages = [
{"role": "system", "content": "Sen, Cerebrum Tech tarafından üretilen ve verilen talimatları takip ederek en iyi cevabı üretmeye çalışan yardımcı bir yapay zekasın."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
temperature=0.3,
top_k=50,
top_p=0.9,
max_new_tokens=512,
repetition_penalty=1,
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]