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Instructions to use PingVortex/Youtube-shorts-comment-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 1,215 Bytes
104d4ea bf5ecd1 104d4ea 1a76289 104d4ea 1a76289 104d4ea 1a76289 104d4ea 1a76289 104d4ea 1a76289 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | ---
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
datasets:
- PingVortex/Youtube_shorts_comments
base_model:
- distilbert/distilgpt2
pipeline_tag: text-generation
library_name: transformers
---
# Youtube shorts comment generator
- Base model: [distilgpt2](https://huggingface.co/distilgpt2)
- Trained on: [YouTube Shorts Comments Dataset](https://huggingface.co/datasets/PingVortex/Youtube_shorts_comments)
## Model Details
- **Parameters**: 82M (DistilGPT2 architecture)
- **Training Data**: 1,475,500 YouTube Shorts comments
## 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: ๐๐๐๐ The white one on the last pic?๐๐๐๐
๐
๐
๐๐๐๐
๐ฎ๐ฎ๐
"`
## Training Info
- **Epochs**: 1
- **Batch Size**: 8
- **Hardware**: Google Colab T4 GPU
- **Training Time**: ~2 hours
- **Loss**: 0.24 |