AhiskaAI-308m-Base-v0.2

AhiskaAI-308m-Base-v0.2

AhiskaAI-308m-Base-v0.2 is a 308 million parameter Small Language Model (SLM) built entirely from scratch. As the largest model in the AhiskaAI v0.2 family, it is designed to deliver stronger Turkish language understanding while maintaining efficient deployment on consumer hardware.

Model Details

• Architecture: Llama-based architecture.
• Parameters: 308M.
• Context Window: 1024 tokens.
• Tokenizer: Custom BPE Tokenizer (Vocabulary Size: 32,000).
• Training Framework: PyTorch & Transformers.

---

Data Curation (The "Quality over Quantity" Approach)

The v0.2 release adopts a data-centric training strategy, prioritizing corpus quality over dataset size.

• Raw Data (v0.1): 5GB of raw Turkish corpus.
• Curated Data (v0.2): 1.2GB of carefully filtered, high-quality Turkish text.
• Process: Approximately 75% of noisy, duplicated, and low-quality samples were removed to improve linguistic quality and training efficiency.

---

Key Improvements from v0.1

• Architecture Shift: Migrated from GPT-2 to a modern Llama-based architecture.
• Normalization: RMSNorm.
• Positional Encoding: RoPE (Rotary Positional Embeddings).
• Activation: SiLU.
• Precision: Trained using bfloat16 for efficient consumer GPU training.

---

Design Goal

The 308M model serves as the flagship base model of the AhiskaAI v0.2 family.

Its primary objectives are:

• Strong Turkish language modeling.
• Improved semantic understanding.
• Better contextual consistency.
• A research foundation for future instruction tuning and alignment.

---

Training Logs

The graph above demonstrates the training convergence of AhiskaAI-308m-Base-v0.2. The stable decline in loss indicates effective optimization throughout pretraining.

---

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("AhiskaAI/AhiskaAI-308m-Base-v0.2")
tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-308m-Base-v0.2")

text = "Türkiye Cumhuriyeti"

inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=50)

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

---

Training Hardware

Trained on NVIDIA RTX 4050 6GB Laptop GPU.

---

Future Plans

• Instruction-tuned (IT) version.
• Preference alignment with DPO.
• Larger and more diverse Turkish datasets.
• Future AhiskaAI v0.3 model family.

---

About AhiskaAI

AhiskaAI is an independent open-source initiative dedicated to developing efficient Turkish Small Language Models trained completely from scratch.

Follow us on Hugging Face for updates and future releases.