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license: llama3
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language:
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- tr
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- en
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model-index:
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---
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license: llama3
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language:
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- tr
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model-index:
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- name: Kocdigital-LLM-8b-v0.1
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge TR
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc
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value: 44.03
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name: accuracy
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag TR
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc
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value: 46.73
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name: accuracy
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU TR
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 49.11
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name: accuracy
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA TR
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: acc
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name: accuracy
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value: 48.21
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande TR
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc
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value: 54.98
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name: accuracy
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k TR
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 51.78
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name: accuracy
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---
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<img src="https://huggingface.co/KOCDIGITAL/Kocdigital-LLM-8b-v0.1/resolve/main/icon.jpeg"
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alt="KOCDIGITAL LLM" width="420"/>
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# Kocdigital-LLM-8b-v0.1
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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.
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## Model Details
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- **Base Model**: Llama3 8B based LLM
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- **Training Dataset**: High Quality Turkish instruction sets
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- **Training Method**: SFT with QLORA
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### QLORA Fine-Tuning Configuration
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- `lora_alpha`: 128
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- `lora_dropout`: 0
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- `r`: 64
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- `target_modules`: "q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj"
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- `bias`: "none"
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## Usage Examples
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"KOCDIGITAL/Kocdigital-LLM-8b-v0.1",
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max_seq_length=4096)
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model = AutoModelForCausalLM.from_pretrained(
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"KOCDIGITAL/Kocdigital-LLM-8b-v0.1",
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load_in_4bit=True,
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)
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system = 'Sen Türkçe konuşan genel amaçlı bir asistansın. Her zaman kullanıcının verdiği talimatları doğru, kısa ve güzel bir gramer ile yerine getir.'
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template = "{}\n\n###Talimat\n{}\n###Yanıt\n"
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content = template.format(system, 'Türkiyenin 3 büyük ilini listeler misin.')
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conv = []
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conv.append({'role': 'user', 'content': content})
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inputs = tokenizer.apply_chat_template(conv,
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tokenize=False,
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add_generation_prompt=True,
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return_tensors="pt")
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print(inputs)
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inputs = tokenizer([inputs],
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return_tensors = "pt",
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add_special_tokens=False).to("cuda")
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outputs = model.generate(**inputs,
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max_new_tokens = 512,
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use_cache = True,
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do_sample = True,
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top_k = 50,
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top_p = 0.60,
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temperature = 0.3,
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repetition_penalty=1.1)
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out_text = tokenizer.batch_decode(outputs)[0]
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print(out_text)
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```
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# [Open LLM Turkish Leaderboard v0.2 Evaluation Results]
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| Metric | Value |
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|---------------------------------|------:|
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| Avg. | 49.11 |
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| AI2 Reasoning Challenge_tr-v0.2 | 44.03 |
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| HellaSwag_tr-v0.2 | 46.73 |
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| MMLU_tr-v0.2 | 49.11 |
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| TruthfulQA_tr-v0.2 | 48.51 |
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| Winogrande _tr-v0.2 | 54.98 |
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| GSM8k_tr-v0.2 | 51.78 |
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