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 Settings

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
```

PingVortex

FlameF0X commited on Apr 27, 2025

Commit

bf5ecd1

verified ·

1 Parent(s): c44fe79

Update README.md (#1)

Browse files

- Update README.md (99ae7d6991205a917ff0ead144c7841f0c6ebe3b)

Co-authored-by: Daniel <FlameF0X@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +6 -0

README.md CHANGED Viewed

@@ -12,6 +12,12 @@ tags:
 pretty_name: DistilGPT2 fine-tuned on YouTube Shorts comments
 size_categories:
 - 10M<n<100M
 ---
 # Youtube shorts comment generator 🧠 (I couldn't come up with a more original name)

 pretty_name: DistilGPT2 fine-tuned on YouTube Shorts comments
 size_categories:
 - 10M<n<100M
+datasets:
+- PingVortex/Youtube_shorts_comments
+base_model:
+- distilbert/distilgpt2
+pipeline_tag: text-generation
+library_name: transformers
 ---
 # Youtube shorts comment generator 🧠 (I couldn't come up with a more original name)