README.md

---
license: llama3
language:
- tr
model-index:
- name: cere-llama-3-8b-tr
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge TR
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc
      value: 44.03
      name: accuracy
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag TR
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc
      value: 46.73
      name: accuracy
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU TR
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 49.11
      name: accuracy
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA TR
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: acc
      name: accuracy
      value: 48.21
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande TR
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc
      value: 54.98
      name: accuracy
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k TR
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 51.78
      name: accuracy
---
# CERE-LLMA-3-8b-TR

This model is an fine-tuned version of a Llama3 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 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

[Open LLM Turkish Leaderboard v0.2 Evaluation Results]

Metric	Value
Avg.	
AI2 Reasoning Challenge_tr	
HellaSwag_tr
MMLU_tr	
TruthfulQA_tr
Winogrande _tr
GSM8k_tr

## Usage Examples

```python

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "Cerebrum/cere-llama-3-8b-tr",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Cerebrum/cere-llama-3-8b-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]
```