README.md

---
language:
- en
license: apache-2.0
tags:
- transformers
- unsloth
- llama
- trl
- sft
- peft
base_model: unsloth/llama-3-8b-bnb-4bit
library_name: peft
datasets:
- myzens/alpaca-turkish-combined
---
# Llama 3-8B Turkish Model

This repo contains the experimental-educational fine-tuned model for the Turkish Llama 3 Project and its variants that can be used for different purposes.

The actual trained model is an adapter model of [Unsloth's Llama 3-8B quantized model](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit), which is then converted into .gguf format using llama.cpp and into .bin format for vLLM.

You can access the fine-tuning code [here](https://colab.research.google.com/drive/1QRaqYxjfnFvwA_9jb7V0Z5bJr-PuHH7w?usp=sharing).

Trained with NVIDIA L4 with 150 steps, took around 8 minutes.

## Example Usage
You can use the adapter model with PEFT.
```py
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "myzens/llama3-8b-tr-finetuned")
tokenizer = AutoTokenizer.from_pretrained("myzens/llama3-8b-tr-finetuned")

alpaca_prompt = """
Instruction:
{}

Input:
{}

Response:
{}"""

inputs = tokenizer([
    alpaca_prompt.format(
        "",
        "Ankara'da gezilebilecek 3 yeri söyle ve ne olduklarını kısaca açıkla.",
        "",
)], return_tensors = "pt").to("cuda")


outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Output:
```
Instruction:


Input:
Ankara'da gezilebilecek 3 yeri söyle ve ne olduklarını kısaca açıkla.

Response:
1. Anıtkabir - Mustafa Kemal Atatürk'ün mezarı
2. Gençlik ve Spor Sarayı - spor etkinliklerinin yapıldığı yer
3. Kızılay Meydanı - Ankara'nın merkezinde bulunan bir meydan
```
### **Important Notes** 
- We recommend you to use an Alpaca Prompt Template or another template, otherwise you can see generations with no meanings or repeating the same sentence constantly. 
- Use the model with a CUDA supported GPU.

Fine-tuned by [emre570](https://github.com/emre570).