Instructions to use AhiskaAI/AhiskaAI-65m-Instruct-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AhiskaAI/AhiskaAI-65m-Instruct-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="AhiskaAI/AhiskaAI-65m-Instruct-v0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-65m-Instruct-v0.2")
model = AutoModelForCausalLM.from_pretrained("AhiskaAI/AhiskaAI-65m-Instruct-v0.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AhiskaAI/AhiskaAI-65m-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-65m-Instruct-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-65m-Instruct-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AhiskaAI/AhiskaAI-65m-Instruct-v0.2

SGLang

How to use AhiskaAI/AhiskaAI-65m-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-65m-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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-65m-Instruct-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-65m-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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-65m-Instruct-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AhiskaAI/AhiskaAI-65m-Instruct-v0.2 with Docker Model Runner:
```
docker model run hf.co/AhiskaAI/AhiskaAI-65m-Instruct-v0.2
```

AhiskaAI-65m-IT-v0.2

AhiskaAI-65m-IT-v0.2 is the instruction-tuned version of our 65M parameter Small Language Model. Fine-tuned on a curated Turkish instruction dataset, it is designed to function as a lightweight conversational AI assistant while maintaining fast inference on resource-constrained hardware.

Base Model: AhiskaAI-65m-Base-v0.2

Model Details

Architecture: Llama-based architecture.
Fine-tuning: Supervised Fine-Tuning (SFT).
Format: ChatML.
Parameters: 65M.
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 a curated Turkish instruction dataset designed to improve conversational ability and instruction following.

The dataset focuses on:

Question answering
General conversation
Summarization
Text generation
Turkish instruction following

Design Goal

The 65M-IT model is designed as the lightweight conversational member of the AhiskaAI v0.2 family.

Its primary goals are:

Basic Turkish instruction following.
Fast conversational inference.
Low-resource deployment.
A compact research baseline for future alignment methods.

Training Logs

The graph above demonstrates the supervised fine-tuning convergence of AhiskaAI-65m-IT-v0.2.

Usage (ChatML)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("AhiskaAI/AhiskaAI-65m-IT-v0.2")
tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-65m-IT-v0.2")

SYSTEM_PROMPT = "Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın."

prompt = (
    f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
    f"<|im_start|>user\nMerhaba<|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

Limited factual knowledge due to model size.
Optimized primarily for Turkish.
Context window limited to 1024 tokens.
May generate inaccurate or incomplete responses on complex topics.

Future Plans

DPO preference alignment.
Improved instruction datasets.
Future AhiskaAI v0.3 releases.

About AhiskaAI

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

Downloads last month: -

Safetensors

Model size

70.7M params

Tensor type

BF16

Collection including AhiskaAI/AhiskaAI-65m-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