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", device_map="auto")

# 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", device_map="auto")

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

YunusEmreSaidoglu

Update README.md

0c58c40 verified 6 days ago

preview code

Raw

History Blame Contribute Delete

1.94 kB

	---
	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](https://huggingface.co/AhiskaAI/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
	![Training Loss](training_loss.png)

	## 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))
	```