Instructions to use AhiskaAI/AhiskaAI-308m-Instruct-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AhiskaAI/AhiskaAI-308m-Instruct-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="AhiskaAI/AhiskaAI-308m-Instruct-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-308m-Instruct-v0.2")
model = AutoModelForCausalLM.from_pretrained("AhiskaAI/AhiskaAI-308m-Instruct-v0.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AhiskaAI/AhiskaAI-308m-Instruct-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-308m-Instruct-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-308m-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AhiskaAI/AhiskaAI-308m-Instruct-v0.2

SGLang

How to use AhiskaAI/AhiskaAI-308m-Instruct-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-308m-Instruct-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-308m-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-308m-Instruct-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-308m-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AhiskaAI/AhiskaAI-308m-Instruct-v0.2 with Docker Model Runner:
```
docker model run hf.co/AhiskaAI/AhiskaAI-308m-Instruct-v0.2
```

AhiskaAI-308m-IT-v0.2

AhiskaAI-308m-IT-v0.2 is the instruction-tuned version of our 308M parameter Small Language Model. Fine-tuned on 16,000+ curated Turkish instruction-response pairs, it is designed to provide stronger conversational ability and improved instruction following while remaining efficient enough to run on consumer hardware.

Base Model: AhiskaAI-308m-Base-v0.2

Model Details

Architecture: Llama-based architecture.
Fine-tuning: Supervised Fine-Tuning (SFT).
Format: ChatML.
Parameters: 308M.
Context Window: 1024 tokens.
Tokenizer: Custom BPE Tokenizer (Vocabulary Size: 32,000).
Training Framework: PyTorch & Transformers.
Hardware: NVIDIA RTX 4050 6GB Laptop GPU.

Fine-tuning Dataset

The model was fine-tuned using more than 16,000 carefully curated Turkish instruction-response pairs.

The dataset includes tasks such as:

Question answering
General conversation
Summarization
Text generation
Instruction following
Basic reasoning

Design Goal

The 308M-IT model serves as the flagship conversational model of the AhiskaAI v0.2 family.

Its primary objectives are:

Improved Turkish instruction following.
Better contextual understanding.
More natural conversational responses.
A strong research foundation for future preference alignment methods such as DPO.

Training Logs

The graph above demonstrates the supervised fine-tuning convergence of AhiskaAI-308m-IT-v0.2.

Usage (ChatML Format)

Recommended 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-308m-IT-v0.2")
tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-308m-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))

Known Limitations

Optimized primarily for Turkish.
Context window is limited to 1024 tokens.
Factual accuracy is still limited by model size and pretraining data.
May generate incorrect or incomplete responses on complex reasoning tasks.

Future Plans

Preference alignment using DPO.
Larger, higher-quality Turkish datasets.
Expanded evaluation benchmarks.
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.

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Safetensors

Model size

0.3B params

Tensor type

BF16

Collection including AhiskaAI/AhiskaAI-308m-Instruct-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 • 6 items • Updated 3 days ago • 1