Instructions to use Metin/gemma-2b-tr-inst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Metin/gemma-2b-tr-inst with Transformers:

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

pipe = pipeline("text-generation", model="Metin/gemma-2b-tr-inst")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Metin/gemma-2b-tr-inst")
model = AutoModelForCausalLM.from_pretrained("Metin/gemma-2b-tr-inst")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Metin/gemma-2b-tr-inst with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Metin/gemma-2b-tr-inst

SGLang

How to use Metin/gemma-2b-tr-inst 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 "Metin/gemma-2b-tr-inst" \
    --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": "Metin/gemma-2b-tr-inst",
		"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 "Metin/gemma-2b-tr-inst" \
        --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": "Metin/gemma-2b-tr-inst",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Metin/gemma-2b-tr-inst with Docker Model Runner:
```
docker model run hf.co/Metin/gemma-2b-tr-inst
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

gemma-2b-tr fine-tuned with Turkish Instruction-Response pairs.

Model Details

Model Description

Language(s) (NLP): Turkish, English
License: Creative Commons Attribution Non Commercial 4.0
Finetuned from model [optional]: gemma-2b-tr (https://huggingface.co/Metin/gemma-2b-tr)

Uses

The model is designed for Turkish instruction following and question answering. Its current response quality is limited, likely due to the small instruction set and model size. It is not recommended for real-world applications at this stage.

Restrictions

Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms Please refer to the gemma use restrictions before start using the model. https://ai.google.dev/gemma/terms#3.2-use

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Metin/gemma-2b-tr-inst")
model = AutoModelForCausalLM.from_pretrained("Metin/gemma-2b-tr-inst")

system_prompt = "You are a helpful assistant. Always reply in Turkish."
instruction = "Ankara hangi ülkenin başkentidir?"
prompt = f"{system_prompt} [INST] {instruction} [/INST]"
input_ids = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

As it can be seen from the above example instructions should be framed within the following structure:

SYSTEM_PROMPT [INST] <Your instruction here> [/INST]

Training Details

Training Data

Dataset: Turkish instructions from the Aya dataset (https://huggingface.co/datasets/CohereForAI/aya_dataset)
Dataset size: ~550K Token or ~5K instruction-response pair.

Training Procedure

Training Hyperparameters

Adapter: QLoRA
Epochs: 1
Context length: 1024
LoRA Rank: 32
LoRA Alpha: 32
LoRA Dropout: 0.05

Downloads last month: 7

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Quantizations

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