README.md

metadata

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

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]