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---
license: llama3.1
language:
- tr
base_model: meta-llama/Meta-Llama-3.1-8B
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/6639e48c27ef2d37a71eb4aa/Ds_KOVYwhRQ1FQY8S4WqO.png"
alt="CEREBRUM LLM" width="420"/>
# 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
```python
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]
``` |