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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AhiskaAI/AhiskaAI-134m-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-134m-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-134m-Instruct-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use AhiskaAI/AhiskaAI-134m-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-134m-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-134m-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-134m-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-134m-Instruct-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

AhiskaAI-134m-Instruct-v0.2 / README.md

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metadata

license: apache-2.0
language:
  - tr
pipeline_tag: text-generation
tags:
  - gpt2
  - slm
  - base-model
  - causal-lm
  - pre-trained
  - tr-llm
  - Ahıska
  - AhiskaTurks
  - MeskhetianTurks
  - AhıskaTürkleri
library_name: transformers

AhiskaAI-134m-IT-v0.2

AhiskaAI-134m-IT-v0.2 is the instruction-tuned version of our 134M parameter Small Language Model. This model has been fine-tuned on 16,000+ high-quality, curated Turkish instruction-response pairs to function as a helpful and conversational AI assistant.

Base Model: AhiskaAI-134m-Base-v0.2

Model Details

Architecture: Llama-based architecture.
Fine-tuning: Supervised Fine-Tuning (SFT) on 16k+ instruction pairs.
Format: ChatML.
Parameters: 134M.
Hardware: Trained on NVIDIA RTX 4050 Laptop GPU.

Training Logs

Usage (ChatML Format)

This model is optimized for chat interactions. Please use the following ChatML structure for best results:

Recommended System Prompt

To get the best performance, use the following 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-134m-IT-v0.2")
tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-134m-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))