---
library_name: peft
model_name: lora_1B_TR
tags:
- meta-llama/Llama-3.2-1B-Instruct
- lora
- sft
- transformers
- trl
- unsloth
licence: license
pipeline_tag: text-generation
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets:
- kadirnar/combined-turkish-datasets-v5
language:
- tr
- en
---



# Model Card for Lora_TR_1B

This is a Lora Adaptor of 'meta-llama/Llama-3.2-1B-Instruct'.
The main goal of this adapter is to obtain an Llama who speaks Turkish better.
>(r=32, lora_alpha=64, lora_dropout=0.005)

## Quick start

```python
from unsloth import FastLanguageModel
from peft import PeftModel
from transformers import AutoTokenizer

BASE = "meta-llama/Llama-3.2-1B-Instruct"
ADAPTER = "Codex07/Lora_1B_TR"

# Load Model
model, tok = FastLanguageModel.from_pretrained(
    model_name=BASE, max_seq_length=2048, load_in_4bit=False, dtype=None, device_map="auto"
)
# Load Adaptor
model = PeftModel.from_pretrained(model, ADAPTER)  # adapter’ı Unsloth modeline tak
FastLanguageModel.for_inference(model)

# Test
messages = [
    {"role":"system","content":"You are AI assistant. Give user answers"},# Sen bir Yapay Zeka Asistanısısın. kullanıcıdan gelen sorulara resmi cevap ver.
    {"role":"user","content":"Selam!"}
]
prompt = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(prompt, max_new_tokens=2048)
print(tok.decode(out[0, prompt.shape[-1]:], skip_special_tokens=True))
```

## Training procedure
Half of 'kadirnar/combined-turkish-datasets-v5' Turkish dataset used. 
Dataset divided into chunks by size 65k.

```bibtex
1> 2:50:33 / 2.746500 -> 1.771400 / 5.1.0
2> 3:00:00 / 1.7 -> 1.7           / 5.1.1
3> 2:18:19 / 1.859100 -> 1.474300 / 5.1.2
4> 3:15:13 / 1.421800 -> 1.122000 / 5.1.3
5> 2:50:00 / 1.746600 -> 1.629600 / 5.1.0
6> 2:44:46 / 1.745000 -> 1.653300 / 5.1.1
7> 2:07:00 / 1.478200 -> 1.357400 / 5.1.2
8> 3:11:54 / 1.174700 -> 1.046100 / 5.1.3
9> 3:12:39 / 1.117600 -> 0.796700 / 5.2.0
10>1:00:57 / 2.217400 -> 1.741400 / 5.2.1
11>1:30:04 / 2.919900 -> 2.534300 / 5.2.2
12>1:30:05 / 2.534300 -> 2.320100 / 5.2.2 
```

This model was trained with SFT.

### Framework versions

- PEFT 0.17.1
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0
- Datasets: 4.3.0
- Tokenizers: 0.22.1

## Citations

Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```