Instructions to use PingVortex/Youtube-shorts-comment-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PingVortex/Youtube-shorts-comment-generator with Transformers:

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

pipe = pipeline("text-generation", model="PingVortex/Youtube-shorts-comment-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PingVortex/Youtube-shorts-comment-generator")
model = AutoModelForCausalLM.from_pretrained("PingVortex/Youtube-shorts-comment-generator")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PingVortex/Youtube-shorts-comment-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/PingVortex/Youtube-shorts-comment-generator

SGLang

How to use PingVortex/Youtube-shorts-comment-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 "PingVortex/Youtube-shorts-comment-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": "PingVortex/Youtube-shorts-comment-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 "PingVortex/Youtube-shorts-comment-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": "PingVortex/Youtube-shorts-comment-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PingVortex/Youtube-shorts-comment-generator with Docker Model Runner:
```
docker model run hf.co/PingVortex/Youtube-shorts-comment-generator
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Youtube shorts comment generator

Base model: distilgpt2
Trained on: YouTube Shorts Comments Dataset

Model Details

Parameters: 82M (DistilGPT2 architecture)
Training Data: 1,475,500 YouTube Shorts comments

Usage Example

from transformers import pipeline

brainrot = pipeline('text-generation', model='PingVortex/Youtube-shorts-comment-generator')

output = brainrot("When you see a Sigma edit:", max_length=50)
print(output[0]['generated_text'])

Sample output:
"When you see a Sigma edit: 😂😂😂😂 The white one on the last pic?😂😂😂😅😅😅😊😊😊😅😮😮😅"

Training Info

Epochs: 1
Batch Size: 8
Hardware: Google Colab T4 GPU
Training Time: ~2 hours
Loss: 0.24

Downloads last month: 12

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for PingVortex/Youtube-shorts-comment-generator

Base model

distilbert/distilgpt2

Finetuned

(1477)

this model

Quantizations

1 model

PingVortex
/

Youtube-shorts-comment-generator

Youtube shorts comment generator

Model Details

Usage Example

Training Info

Model tree for PingVortex/Youtube-shorts-comment-generator

Dataset used to train PingVortex/Youtube-shorts-comment-generator