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 commited on Mar 26, 2025

Commit

104d4ea

verified ·

1 Parent(s): b6680bb

Update README.md

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@@ -1,3 +1,71 @@
----
-license: apache-2.0
----

+---
+license: cc0-1.0
+language:
+- en
+- fr
+- tr
+tags:
+- art
+- emoji
+- brainrot
+- text-generation
+pretty_name: DistilGPT2 fine-tuned on YouTube Shorts comments
+size_categories:
+- 10M<n<100M
+---
+# Youtube-shorts-comment-generator 🧠🌀
+A **fine-tuned DistilGPT2 model** trained on 1.4M+ YouTube Shorts comments - the perfect language model for generating cursed internet humor, emoji spam, and authentic YouTube degeneracy.
+- Base model: [distilgpt2](https://huggingface.co/distilgpt2)
+- Trained on: [YouTube Shorts Comments Dataset](https://huggingface.co/datasets/PingVortex/Youtube_shorts_comments)
+- Creator: [PingVortex](https://github.com/PingVortex)
+## Model Details 🔥
+- **Parameters**: 82M (DistilGPT2 architecture)
+- **Training Data**: 1,475,500 YouTube Shorts comments
+- **Special Skills**: Emoji generation, broken English, random character generation
+## Usage Example 🐍
+```python
+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
+## Ethical Considerations ⚠️
+This model may generate:
+- Extreme emoji spam (🔥💀🤣)
+- Nonsensical combinations
+- Mild brain damage
+- Occasional coherent text
+Use responsibly (or irresponsibly, we don't judge).
+## License 📜
+**CC0 1.0 Universal** (Public Domain)
+*Go nuts - no restrictions*
+## Shoutouts 🔊
+- Subscribe to [FaceDev](https://youtube.com/@FaceDevStuff)
+- Join my [Discord](https://discord.gg/At3CcCqcR2)