Instructions to use TahaCakir/enhanced_turkishReviews-generativeAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TahaCakir/enhanced_turkishReviews-generativeAI with Transformers:

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

pipe = pipeline("text-generation", model="TahaCakir/enhanced_turkishReviews-generativeAI")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TahaCakir/enhanced_turkishReviews-generativeAI")
model = AutoModelForCausalLM.from_pretrained("TahaCakir/enhanced_turkishReviews-generativeAI")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TahaCakir/enhanced_turkishReviews-generativeAI with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TahaCakir/enhanced_turkishReviews-generativeAI

SGLang

How to use TahaCakir/enhanced_turkishReviews-generativeAI 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 "TahaCakir/enhanced_turkishReviews-generativeAI" \
    --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": "TahaCakir/enhanced_turkishReviews-generativeAI",
		"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 "TahaCakir/enhanced_turkishReviews-generativeAI" \
        --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": "TahaCakir/enhanced_turkishReviews-generativeAI",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TahaCakir/enhanced_turkishReviews-generativeAI with Docker Model Runner:
```
docker model run hf.co/TahaCakir/enhanced_turkishReviews-generativeAI
```

enhanced_turkishReviews-generativeAI

This model is a fine-tuned version of gpt2-large on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 9.7770
Validation Loss: 9.7482
Epoch: 9

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-07, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-07, 'decay_steps': -293, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.09}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
training_precision: mixed_float16

Training results

Train Loss	Validation Loss	Epoch
10.1112	9.8654	0
9.8034	9.7482	1
9.7773	9.7482	2
9.7774	9.7482	3
9.7772	9.7482	4
9.7770	9.7482	5
9.7770	9.7482	6
9.7774	9.7482	7
9.7776	9.7482	8
9.7770	9.7482	9

Framework versions

Transformers 4.35.2
TensorFlow 2.14.0
Datasets 2.15.0
Tokenizers 0.15.0

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