Instructions to use BhavyaMuni/taylor-swift-model-paragraphs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BhavyaMuni/taylor-swift-model-paragraphs with Transformers:

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

pipe = pipeline("text-generation", model="BhavyaMuni/taylor-swift-model-paragraphs")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BhavyaMuni/taylor-swift-model-paragraphs")
model = AutoModelForCausalLM.from_pretrained("BhavyaMuni/taylor-swift-model-paragraphs")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BhavyaMuni/taylor-swift-model-paragraphs with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/BhavyaMuni/taylor-swift-model-paragraphs

SGLang

How to use BhavyaMuni/taylor-swift-model-paragraphs 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 "BhavyaMuni/taylor-swift-model-paragraphs" \
    --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": "BhavyaMuni/taylor-swift-model-paragraphs",
		"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 "BhavyaMuni/taylor-swift-model-paragraphs" \
        --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": "BhavyaMuni/taylor-swift-model-paragraphs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BhavyaMuni/taylor-swift-model-paragraphs with Docker Model Runner:
```
docker model run hf.co/BhavyaMuni/taylor-swift-model-paragraphs
```

taylor-swift-model-paragraphs

This model is a fine-tuned version of distilgpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.3564

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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: 25

Training results

Training Loss	Epoch	Step	Validation Loss
3.9316	1.0	59	3.8227
3.824	2.0	118	3.7301
3.5808	3.0	177	3.6658
3.625	4.0	236	3.6205
3.643	5.0	295	3.5862
3.5443	6.0	354	3.5545
3.4535	7.0	413	3.5274
3.398	8.0	472	3.5072
3.3253	9.0	531	3.4833
3.4111	10.0	590	3.4688
3.3461	11.0	649	3.4503
3.3133	12.0	708	3.4373
3.3921	13.0	767	3.4246
3.2661	14.0	826	3.4102
3.2257	15.0	885	3.4052
3.1837	16.0	944	3.3911
3.1935	17.0	1003	3.3849
2.9369	18.0	1062	3.3774
3.2486	19.0	1121	3.3721
3.1542	20.0	1180	3.3681
3.0771	21.0	1239	3.3624
3.1206	22.0	1298	3.3581
3.0358	23.0	1357	3.3585
2.9207	24.0	1416	3.3568
3.0496	25.0	1475	3.3564

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2

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