This model is a fine-tuned version of google/gemma-3-1b-it on a custom Kazakh dataset of short story sentences from ertegiler.kz, augmented with spelling and grammar mistakes. It was trained to correct noisy Kazakh sentences in an instruction-following format.

🔍 Model Details

Attribute	Value
Base Model	`google/gemma-3-1b-it` (Decoder-only, instruction-tuned)
Fine-tuning Method	LoRA (via PEFT + QLoRA)
Dataset	`sauatai-ertegiler-kz-misspellings-kk-s170-len60-n6-mprob-v1`
Language	Kazakh (`kk`)
Training Examples	12,000 (training), 3,200 (validation)
Epochs	3
Learning Rate	2e-4
Sentence Sorting	Shortest sentences selected first
Output Token	`<fix>` used as end-of-response token

💡 How to Use

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# ⚙️ Configs
BASE = "google/gemma-3-1b-it"
ADAPTER = "alphazhan/sauatai-gemma-3-1b-it-kk-s170-len60-n6-mprob-ntrain12k-shortfirst-e3-lr2e4-v1"
device = "cuda" if torch.cuda.is_available() else "cpu"

# 🧠 Load tokenizer & base model
tokenizer = AutoTokenizer.from_pretrained(ADAPTER)
model = AutoModelForCausalLM.from_pretrained(BASE, device_map="auto")
model.resize_token_embeddings(len(tokenizer))
model = PeftModel.from_pretrained(model, ADAPTER).to(device)

# ✏️ Inference
sentence_to_correct = "Ол досм еді"
prompt = f"Correct this Kazakh sentence.\nInput: {sentence_to_correct}\nOutput:"
inputs = tokenizer(prompt, return_tensors="pt").to(device)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=64)

print(tokenizer.decode(output[0], skip_special_tokens=True))

📉 Training and Validation Loss

Step	Training Loss	Validation Loss
500	2.4877	2.5578
1000	2.3317	2.4297
1500	2.2257	2.3402
2000	2.1642	2.2790
2500	2.1078	2.2513
3000	2.1189	2.2300

Although the model demonstrates a consistent decline in both training and validation loss—indicating effective learning and no signs of overfitting—the curve suggests that three epochs may not have been sufficient to reach convergence. Both metrics were still steadily improving at the end of training, implying that additional epochs could further reduce the loss and enhance the model’s generalization ability. This is especially relevant for instruction-tuned SLMs like Gemma 3 (1B), which typically benefit from longer training durations when fine-tuned on domain-specific or low-resource languages like Kazakh. Extending training slightly—while monitoring for plateauing or divergence—could yield a more refined and performant model (I hope so).

🧾 Prompt Format

text

Correct this Kazakh sentence.
Input: Ол досм еді
Output: Ол досым еді<fix>

🔗 Credits

Developed by @alphazhan