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metadata
license: llama3
language:
  - tr
model-index:
  - name: Kocdigital-LLM-8b-v0.1
    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

KOCDIGITAL LLM

Kocdigital-LLM-8b-v0.1

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. The training process involved using the QLORA method.

Model Details

  • Base Model: Llama3 8B based LLM
  • Training Dataset: High Quality Turkish instruction sets
  • Training Method: SFT with QLORA

QLORA Fine-Tuning Configuration

  • lora_alpha: 128
  • lora_dropout: 0
  • r: 64
  • target_modules: "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"
  • bias: "none"

Usage Examples


from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"KOCDIGITAL/Kocdigital-LLM-8b-v0.1", 
max_seq_length=4096)
model = AutoModelForCausalLM.from_pretrained(
    "KOCDIGITAL/Kocdigital-LLM-8b-v0.1",
    load_in_4bit=True,
)

system = 'Sen Türkçe konuşan genel amaçlı bir asistansın. Her zaman kullanıcının verdiği talimatları doğru, kısa ve güzel bir gramer ile yerine getir.'

template = "{}\n\n###Talimat\n{}\n###Yanıt\n"
content = template.format(system, 'Türkiyenin 3 büyük ilini listeler misin.')

conv = []
conv.append({'role': 'user', 'content': content})
inputs = tokenizer.apply_chat_template(conv, 
                                       tokenize=False, 
                                       add_generation_prompt=True, 
                                       return_tensors="pt")

print(inputs)

inputs = tokenizer([inputs], 
                   return_tensors = "pt",
                   add_special_tokens=False).to("cuda")

outputs = model.generate(**inputs, 
                         max_new_tokens = 512, 
                         use_cache = True, 
                         do_sample = True, 
                         top_k = 50, 
                         top_p = 0.60, 
                         temperature = 0.3, 
                         repetition_penalty=1.1)

out_text = tokenizer.batch_decode(outputs)[0]
print(out_text)

[Open LLM Turkish Leaderboard v0.2 Evaluation Results]

Metric Value
Avg. 49.11
AI2 Reasoning Challenge_tr-v0.2 44.03
HellaSwag_tr-v0.2 46.73
MMLU_tr-v0.2 49.11
TruthfulQA_tr-v0.2 48.51
Winogrande _tr-v0.2 54.98
GSM8k_tr-v0.2 51.78