---
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
---

<img src="https://huggingface.co/KOCDIGITAL/Kocdigital-LLM-8b-v0.1/resolve/main/icon.jpeg"
alt="KOCDIGITAL LLM" width="420"/>

# 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

```python

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 |