Instructions to use GPT-JF/Model_1A_Clinton with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GPT-JF/Model_1A_Clinton with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="GPT-JF/Model_1A_Clinton")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("GPT-JF/Model_1A_Clinton") model = AutoModelForCausalLM.from_pretrained("GPT-JF/Model_1A_Clinton") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use GPT-JF/Model_1A_Clinton with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GPT-JF/Model_1A_Clinton" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GPT-JF/Model_1A_Clinton", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/GPT-JF/Model_1A_Clinton
- SGLang
How to use GPT-JF/Model_1A_Clinton 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 "GPT-JF/Model_1A_Clinton" \ --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": "GPT-JF/Model_1A_Clinton", "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 "GPT-JF/Model_1A_Clinton" \ --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": "GPT-JF/Model_1A_Clinton", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use GPT-JF/Model_1A_Clinton with Docker Model Runner:
docker model run hf.co/GPT-JF/Model_1A_Clinton
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("GPT-JF/Model_1A_Clinton")
model = AutoModelForCausalLM.from_pretrained("GPT-JF/Model_1A_Clinton")Model_1A_Clinton
This model is a fine-tuned version of gpt2-medium on a large corpus of William J. Clinton's second term discourse on terrorism.
To Prompt the Model
Try entering single words or short phrases, such as "terrorism is" or "national security" or "our foreign policy should be", in the dialogue box on the right hand side of this page. Then click on 'compute' and wait for the results. The model will take a few seconds to load on your first prompt.
Intended uses & limitations
This model is intended as an experiment on the utility of LLMs for discourse analysis on a specific corpus of political rhetoric.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for GPT-JF/Model_1A_Clinton
Base model
openai-community/gpt2-medium
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="GPT-JF/Model_1A_Clinton")