Instructions to use cjerzak/political-speeches-gpt2-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cjerzak/political-speeches-gpt2-finetune with Transformers:

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

pipe = pipeline("text-generation", model="cjerzak/political-speeches-gpt2-finetune")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cjerzak/political-speeches-gpt2-finetune")
model = AutoModelForCausalLM.from_pretrained("cjerzak/political-speeches-gpt2-finetune")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cjerzak/political-speeches-gpt2-finetune with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cjerzak/political-speeches-gpt2-finetune"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cjerzak/political-speeches-gpt2-finetune",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cjerzak/political-speeches-gpt2-finetune

SGLang

How to use cjerzak/political-speeches-gpt2-finetune 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 "cjerzak/political-speeches-gpt2-finetune" \
    --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": "cjerzak/political-speeches-gpt2-finetune",
		"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 "cjerzak/political-speeches-gpt2-finetune" \
        --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": "cjerzak/political-speeches-gpt2-finetune",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cjerzak/political-speeches-gpt2-finetune with Docker Model Runner:
```
docker model run hf.co/cjerzak/political-speeches-gpt2-finetune
```

Model Card for `cjerzak/trump-speeches-gpt2-finetune`

This is a fine-tuned version of the GPT-2 language model on a collection of 10 Donald Trump speeches. The original speeches are available at ryanmcdermott/trump-speeches. This model is intended for experimentation with text generation and for demonstration/educational purposes.

Model Details

Model Description

Developed by: Fine-tuning OpenAI's original GPT-2.
Model type: Causal Language Model (GPT-2).
Language(s) (NLP): English.
License: GPT-2’s license (MIT-based). The fine-tuning code is also available and may be under a different open-source license; consult the repository for details.
Finetuned from model: gpt2.
Shared by: cjerzak on Hugging Face.

Because it was fine-tuned on a small set of speeches, the model has learned stylistic patterns, phrases, and vocabulary frequently used in Donald Trump’s rhetoric. Note: This model is primarily for demonstration and to illustrate the use of GPT-2 fine-tuning. It should not be considered a comprehensive or robust representation of all of Donald Trump’s speeches.

Model Sources

Repository: cjerzak/trump-speeches-gpt2-finetune on Hugging Face
Training Data Source: ryanmcdermott/trump-speeches

Uses

Direct Use

Text Generation / Experimentation: You can use this model to generate text in a style that somewhat mimics Donald Trump's speeches. It is well-suited as a teaching or demonstration model for fine-tuning GPT-2.

Downstream Use

Creative Projects / Educational Examples: If you want to incorporate a “Trump-like” text generation style in creative applications or show how GPT-2 behaves when fine-tuned on a small, domain-specific dataset.

Bias, Risks, and Limitations

Because the training data consists of Donald Trump's speeches, the model may exhibit:

Stylistic Bias: The model might produce text with repetitive rhetorical patterns or phrases.
Political Bias / Offensiveness: The original speeches may contain language or statements that some users find offensive or controversial.
Limited Generalization: With only 10 speeches, the model’s language patterns are narrowly focused. It may produce text with limited variety or slightly nonsensical completions outside of the style/subject matter present in the training data.

Recommendations

Content Filtering: If deploying publicly, consider adding filters or moderation layers to avoid offensive content.
Awareness: Users should be aware of potential biases in the generated text. Given the small dataset, it can amplify certain topics or phrasings.
Educational Use: Primarily recommended for demonstration or educational projects, not as a production-level model.

How to Get Started with the Model

Install the necessary packages:

pip install transformers accelerate torch

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Safetensors

Model size

0.1B params

Tensor type

F32

Model Card for cjerzak/trump-speeches-gpt2-finetune