Instructions to use DunnBC22/distilgpt2-CLM_US_Economic_News_Articles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DunnBC22/distilgpt2-CLM_US_Economic_News_Articles with Transformers:

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

pipe = pipeline("text-generation", model="DunnBC22/distilgpt2-CLM_US_Economic_News_Articles")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DunnBC22/distilgpt2-CLM_US_Economic_News_Articles")
model = AutoModelForCausalLM.from_pretrained("DunnBC22/distilgpt2-CLM_US_Economic_News_Articles")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DunnBC22/distilgpt2-CLM_US_Economic_News_Articles with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DunnBC22/distilgpt2-CLM_US_Economic_News_Articles

SGLang

How to use DunnBC22/distilgpt2-CLM_US_Economic_News_Articles 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 "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles" \
    --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": "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles",
		"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 "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles" \
        --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": "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DunnBC22/distilgpt2-CLM_US_Economic_News_Articles with Docker Model Runner:
```
docker model run hf.co/DunnBC22/distilgpt2-CLM_US_Economic_News_Articles
```

distilgpt2-CLM_US_Economic_News_Articles / README.md

DunnBC22

Update README.md

89ce15e verified about 2 months ago

preview code

raw

history blame contribute delete

1.87 kB

	---
	license: mit
	tags:
	- generated_from_trainer
	model-index:
	- name: distilgpt2-CLM_US_Economic_News_Articles
	results: []
	language:
	- en
	metrics:
	- perplexity
	---

	# distilgpt2-CLM_US_Economic_News_Articles

	This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2).
	It achieves the following results on the evaluation set:
	- Loss: 3.4472

	## Model description

	This is a causal lamguage modeling project.

	For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Causal%20Language%20Modeling/US%20Economic%20News%20Articles/US%20Economic%20News%20Articles%20-%20CLM.ipynb

	## Intended uses & limitations

	This model is intended to demonstrate my ability to solve a complex problem using technology.

	## Training and evaluation data

	Dataset Source: https://www.kaggle.com/datasets/heeraldedhia/us-economic-news-articles

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 2e-05
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 3

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| 3.6225 \| 1.0 \| 1869 \| 3.4853 \|
	\| 3.5092 \| 2.0 \| 3738 \| 3.4555 \|
	\| 3.4514 \| 3.0 \| 5607 \| 3.4472 \|

	Perplexity: 31.41

	### Framework versions

	- Transformers 4.26.1
	- Pytorch 1.12.1
	- Datasets 2.9.0
	- Tokenizers 0.12.1


	## License Notice
	This model is a fine-tuned derivative of a pretrained model.
	Users must comply with the original model license.


	## Dataset Notice
	This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.