Instructions to use twdooley/breitbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use twdooley/breitbot with Transformers:

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

pipe = pipeline("text-generation", model="twdooley/breitbot")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("twdooley/breitbot")
model = AutoModelForCausalLM.from_pretrained("twdooley/breitbot", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use twdooley/breitbot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/twdooley/breitbot

SGLang

How to use twdooley/breitbot 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 "twdooley/breitbot" \
    --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": "twdooley/breitbot",
		"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 "twdooley/breitbot" \
        --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": "twdooley/breitbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use twdooley/breitbot with Docker Model Runner:
```
docker model run hf.co/twdooley/breitbot
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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Check out the documentation for more information.

BreitBot

Timothy W. Dooley

___________________________________________________

GitHub

The GitHub for the project can be found [here](https://github.com/twdooley/election_news)

Model

This model was trained on about 16,000 headlines from Breitbart.com spannning March 2019- 11 November 2020. The purpose of this project was to better understand how strongly polarized news crafts a narrative through Natural Language Processing. The BreitBot model was specifically created to understand the 'clickbaity' nature of a Breitbart headline. Many of the results are 'reasonable' within the scope of Breitbart's production. I will leave it to the user to make further interpretation. The full project noted that over 70% of Breitbart's articles from month to month have a negative sentiment score. Subjectively, I believe this is shown through the headlines generated.

Training

BreitBot is a finetuned on GPT2 with about 16,000 headlines. The maximum length allowed in the tokenizer was the length of the longest headline (~50 tokens). A huge credit goes to Richard Bownes, PhD whose article ["Fine Tuning GPT-2 for Magic the Gathering Flavour Text Generation"](https://medium.com/swlh/fine-tuning-gpt-2-for-magic-the-gathering-flavour-text-generation-3bafd0f9bb93) provided incredible direction and help in training this model. It was trained using a GPU on Google Colab.

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for twdooley/breitbot

Quantizations

2 models