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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

Unsloth Studio new

How to use atasoglu/gemma-2-2b-tr with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for atasoglu/gemma-2-2b-tr to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for atasoglu/gemma-2-2b-tr to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for atasoglu/gemma-2-2b-tr to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="atasoglu/gemma-2-2b-tr",
    max_seq_length=2048,
)

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

Uploaded model

Developed by: atasoglu
License: apache-2.0
Finetuned from model : unsloth/gemma-2-2b-bnb-4bit

This gemma2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Usage

from unsloth import FastLanguageModel
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "atasoglu/gemma-2-2b-tr",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

FastLanguageModel.for_inference(model) # Enable native 2x faster inference

alpaca_prompt = """Aşağıda verilen talimat ve giriş ifadelerine uygun bir cevap yaz.

### Talimat:
Aşağıdaki programlama dillerinden hangisi yapay zeka çalışmak için daha uygundur?
Sebebini açıkla.

### Giriş:
Python, C++, Java, Rust

### Cevap:
"""
inputs = tokenizer(alpaca_prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    **inputs,
    max_new_tokens=128,
    do_sample=True,
    temperature=0.2,
    repetition_penalty=1.15,
    top_k=20,
    top_p=0.7,
)

generated_tokens = outputs[:, inputs.input_ids.shape[1]:]
response = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(response)
# Output: ['C++ veya Python en iyi seçenektir çünkü bu iki dilde çok sayıda yapay zeka araçları vardır. Bu araçlar, veri analizi, öğrenme algoritmaları ve karar verme süreçlerini kolaylaştırır. Ayrıca, her ikisinin de güçlü bir kütüphanesi olması da önemlidir.']

Downloads last month: 7

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for atasoglu/gemma-2-2b-tr

Base model

google/gemma-2-2b

Quantized

unsloth/gemma-2-2b-bnb-4bit

Finetuned

(257)

this model

Quantizations

1 model

atasoglu
/

gemma-2-2b-tr

Uploaded model

Usage

Model tree for atasoglu/gemma-2-2b-tr

Dataset used to train atasoglu/gemma-2-2b-tr