Instructions to use Passpass119/videogame-title-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Passpass119/videogame-title-generator with Transformers:

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

pipe = pipeline("text-generation", model="Passpass119/videogame-title-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Passpass119/videogame-title-generator")
model = AutoModelForCausalLM.from_pretrained("Passpass119/videogame-title-generator")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Passpass119/videogame-title-generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Passpass119/videogame-title-generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Passpass119/videogame-title-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Passpass119/videogame-title-generator

SGLang

How to use Passpass119/videogame-title-generator 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 "Passpass119/videogame-title-generator" \
    --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": "Passpass119/videogame-title-generator",
		"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 "Passpass119/videogame-title-generator" \
        --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": "Passpass119/videogame-title-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Passpass119/videogame-title-generator with Docker Model Runner:
```
docker model run hf.co/Passpass119/videogame-title-generator
```

videogame-title-generator

This model is a fine-tuned version of distilbert/distilgpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.6795

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:

learning_rate: 0.0005
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss
5.0014	0.3870	500	4.9245
4.7997	0.7740	1000	4.7515
4.2551	1.1610	1500	4.6960
4.2236	1.5480	2000	4.6795

Framework versions

Transformers 5.6.2
Pytorch 2.11.0+cu130
Datasets 4.8.4
Tokenizers 0.22.2

Downloads last month: 656

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for Passpass119/videogame-title-generator

Base model

distilbert/distilgpt2

Finetuned

(1455)

this model