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---
library_name: transformers
tags:
- unsloth
language:
- tr
---
# Model Card for Model ID
Fine-tuned Llama3-8b model with Lora (trained 1 epoch on colap A100 for experimental purposes)
Base Model: unsloth/llama-3-8b-bnb-4bit
Fine-tuning process video: https://www.youtube.com/watch?v=pK8u4QfdLx0&ab_channel=DavidOndrej
Turkish Fine-tune notebook: https://github.com/yudumpacin/LLM/blob/main/Alpaca_%2B_Llama_3_8b_full_Turkish.ipynb
Original unsloth notebook: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing
Fine-tuning data :
- Yudum/turkish-instruct-dataset which includes;
* open question category of atasoglu/databricks-dolly-15k-tr
* parsak/alpaca-tr-1k-longest
* TFLai/Turkish-Alpaca
* umarigan/GPTeacher-General-Instruct-tr
# Usage
```python
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "Yudum/llama3-lora-turkish",
max_seq_length = 2048,
dtype = None,
load_in_4bit = True,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
alpaca_prompt = """Altta bir görevi tanımlayan bir talimat ile daha fazla bilgi sağlayan bir girdi bulunmaktadır. İsteği uygun şekilde tamamlayan bir yanıt yazın.
### Talimat:
{}
### Girdi:
{}
### Yanıt:
{}
"""
inputs = tokenizer(
[
alpaca_prompt.format(
"Paris'teki meşhur kulenin ismi nedir?", # instruction
"", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
tokenizer.batch_decode(outputs)
``` |