Codex07/Lora-3B-TR

Model Card for Lora_TR_3B

This is a Lora Adaptor of 'meta-llama/Llama-3.2-3B-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

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

BASE = "meta-llama/Llama-3.2-3B-Instruct"
ADAPTER = "Codex07/Lora_3B_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":"Merhaba!"},
]
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.

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:

@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}}
}