Instructions to use cansen88/PromptGenerator_5_topic_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cansen88/PromptGenerator_5_topic_finetuned with Transformers:

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

pipe = pipeline("text-generation", model="cansen88/PromptGenerator_5_topic_finetuned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cansen88/PromptGenerator_5_topic_finetuned")
model = AutoModelForCausalLM.from_pretrained("cansen88/PromptGenerator_5_topic_finetuned")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cansen88/PromptGenerator_5_topic_finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cansen88/PromptGenerator_5_topic_finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cansen88/PromptGenerator_5_topic_finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cansen88/PromptGenerator_5_topic_finetuned

SGLang

How to use cansen88/PromptGenerator_5_topic_finetuned 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 "cansen88/PromptGenerator_5_topic_finetuned" \
    --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": "cansen88/PromptGenerator_5_topic_finetuned",
		"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 "cansen88/PromptGenerator_5_topic_finetuned" \
        --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": "cansen88/PromptGenerator_5_topic_finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cansen88/PromptGenerator_5_topic_finetuned with Docker Model Runner:
```
docker model run hf.co/cansen88/PromptGenerator_5_topic_finetuned
```

PromptGenerator_5_topic_finetuned

This model is a fine-tuned version of kmkarakaya/turkishReviews-ds on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 1.6861
Train Sparse Categorical Accuracy: 0.8150
Validation Loss: 1.9777
Validation Sparse Categorical Accuracy: 0.7250
Epoch: 4

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: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Train Sparse Categorical Accuracy	Validation Loss	Validation Sparse Categorical Accuracy	Epoch
3.0394	0.5171	2.7152	0.5841	0
2.5336	0.6247	2.4440	0.6318	1
2.2002	0.6958	2.2557	0.6659	2
1.9241	0.7608	2.1059	0.6932	3
1.6861	0.8150	1.9777	0.7250	4

Framework versions

Transformers 4.21.1
TensorFlow 2.8.2
Datasets 2.4.0
Tokenizers 0.12.1

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