---
language:
- tr
license: apache-2.0
tags:
- llama
- causal-lm
- slm
- experimental
- turkish
- tr-llm
- AhiskaAI
datasets:
- BILGEM-AI/BILGE-Synthetic-Math
- BILGEM-AI/BILGE-Synthetic-Web
- BILGEM-AI/BILGE-Synthetic-Stories
pipeline_tag: text-generation
---

# 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):

| Data Source | Approx. Size | Primary Focus | Link |
| :--- | :--- | :--- | :--- |
| **BILGEM AI Synthetic Web** | ~2.0 GB | General Web, Knowledge & Grammar | [BILGEM-AI/BILGE-Synthetic-Web](https://huggingface.co/datasets/BILGEM-AI/BILGE-Synthetic-Web) |
| **BILGEM AI Synthetic Math** | ~750 MB | Mathematical Reasoning & Logic | [BILGEM-AI/BILGE-Synthetic-Math](https://huggingface.co/datasets/BILGEM-AI/BILGE-Synthetic-Math) |
| **BILGEM AI Synthetic Stories** | ~750 MB | Narrative Flow & Text Generation | [BILGEM-AI/BILGE-Synthetic-Stories](https://huggingface.co/datasets/BILGEM-AI/BILGE-Synthetic-Stories) |

## Usage

```python
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.