Instructions to use AhiskaAI/AhiskaAI-134m-Base-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AhiskaAI/AhiskaAI-134m-Base-v0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AhiskaAI/AhiskaAI-134m-Base-v0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AhiskaAI/AhiskaAI-134m-Base-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AhiskaAI/AhiskaAI-134m-Base-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-134m-Base-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AhiskaAI/AhiskaAI-134m-Base-v0.2

SGLang

How to use AhiskaAI/AhiskaAI-134m-Base-v0.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AhiskaAI/AhiskaAI-134m-Base-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-134m-Base-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AhiskaAI/AhiskaAI-134m-Base-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-134m-Base-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AhiskaAI/AhiskaAI-134m-Base-v0.2 with Docker Model Runner:
```
docker model run hf.co/AhiskaAI/AhiskaAI-134m-Base-v0.2
```

AhiskaAI-134m-Base-v0.2

AhiskaAI-134m-Base-v0.2 is a 134 million parameter Small Language Model (SLM) built from scratch. It represents the second generation of the AhiskaAI organization's mission to develop efficient, high-quality, native Turkish language models.

Model Details

Architecture: Llama-based architecture.
Parameters: 134M.
Context Window: 1024 tokens.
Tokenizer: Custom BPE Tokenizer (Vocabulary Size: 32,000).
Training Framework: PyTorch & Transformers.

Data Curation (The "Quality over Quantity" Approach)

A major leap in the v0.2 release is the transition to data-centric AI.

Raw Data (v0.1): 5GB of raw corpus.
Curated Data (v0.2): 1.2GB of meticulously filtered, high-quality Turkish text.
Process: Approximately 75% of low-quality, noisy, and redundant data was eliminated to ensure the model focuses on grammatical correctness and linguistic density.

Key Improvements from v0.1

Architecture Shift: Migrated from standard GPT-2 to a modern Llama-based architecture for superior sequence modeling.
Normalization: Implemented RMSNorm for training stability.
Positional Encoding: Adopted RoPE (Rotary Positional Embeddings).
Activation: Switched to SiLU activation function.
Precision: Trained in bfloat16 for efficiency on consumer-grade hardware.

Training Logs

The graph above demonstrates the training convergence of AhiskaAI-134m-Base-v0.2. The stable decline in loss confirms the effective alignment of the model architecture with the curated Turkish dataset.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

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

Instruct Version: AhiskaAI-134m-IT-v0.2 (Fine-tuned for chat and instructions).

Preference Alignment: v0.2.1 DPO (Direct Preference Optimization) is currently under development to refine response quality.

About AhiskaAI

AhiskaAI is an independent initiative dedicated to pushing the boundaries of Small Language Models in the Turkish language. Follow us on Hugging Face for updates and new releases.

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Safetensors

Model size

0.1B params

Tensor type

F32

Collection including AhiskaAI/AhiskaAI-134m-Base-v0.2

AhıskaAI-v0.2-Collection

Collection

The v0.2 release of AhiskaAI models, trained from scratch on curated Turkish datasets. This series features a Llama-based architecture, optimized data • 2 items • Updated 1 day ago