AhiskaAI-110M-Experimental-v0.1-Base

AhıskaAI-110M-Experimental-v0.1

AhıskaAI-110M-Experimental-v0.1 is a 110M parameter Small Language Model (SLM) trained from scratch as part of the experimental release series by AhıskaAI. This series focuses on evaluating early-stage convergence and reasoning behavior on synthetic Turkish datasets (covering math, web content, and narrative stories).

Experimental Setup & Scope

• Objective: Test synthetic data density (Math + Web + Stories) and sequence modeling performance on a compact 110M Llama architecture.
• Training Epochs: Pre-trained for 0.3 epochs across a ~3.5 GB synthetic Turkish corpus.
• Context Length: 1024 tokens.

Model Details

• Architecture: LlamaForCausalLM (Standard MHA)
• Parameters: ~110M
• Hidden Size: 768
• Intermediate Size: 2048
• Attention Heads: 12 (Query) / 12 (Key/Value)
• Layers: 12
• Context Length: 1024 tokens
• Vocabulary Size: 32,000
• Precision: float32 / float16

Pre-training Dataset Mixture

The model was pre-trained on a synthetic Turkish corpus (~3.5 GB total):

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AhıskaAI/AhıskaAI-110M-Experimental-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"
)

prompt = "Bir zamanlar uzak bir ülkede"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

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

About AhıskaAI

AhıskaAI is an independent initiative dedicated to developing efficient, high-performance Small Language Models (SLMs) tailored for the Turkish language ecosystem.