---
license: apache-2.0
language:
- tr
pipeline_tag: text-generation
tags:
- gpt2
- slm
- base-model
- causal-lm
- pre-trained
- tr-llm
- Ahıska
- AhiskaTurks
- MeskhetianTurks
- AhıskaTürkleri
library_name: transformers
---

# AhiskaAI-134m-IT-v0.2

AhiskaAI-134m-IT-v0.2 is the instruction-tuned version of our 134M parameter Small Language Model. This model has been fine-tuned on 16,000+ high-quality, curated Turkish instruction-response pairs to function as a helpful and conversational AI assistant.

**Base Model:** [AhiskaAI-134m-Base-v0.2](https://huggingface.co/AhiskaAI/AhiskaAI-134m-Base-v0.2)

## Model Details
- **Architecture:** Llama-based architecture.
- **Fine-tuning:** Supervised Fine-Tuning (SFT) on 16k+ instruction pairs.
- **Format:** ChatML.
- **Parameters:** 134M.
- **Hardware:** Trained on NVIDIA RTX 4050 Laptop GPU.

## Training Logs 
![Training Loss](training_loss.png)

## Usage (ChatML Format)
This model is optimized for chat interactions. Please use the following ChatML structure for best results:

## Recommended System Prompt
To get the best performance, use the following system prompt:
"Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın."

## Example Usage
```
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("AhiskaAI/AhiskaAI-134m-IT-v0.2")
tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-134m-IT-v0.2")

SYSTEM_PROMPT = "Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın."
user_query = "Ahıska Türkleri hakkında bilgi verir misin?"

prompt = (
    f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
    f"<|im_start|>user\n{user_query}<|im_end|>\n"
    f"<|im_start|>assistant\n"
)

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```