Instructions to use jimypbr/bart-large-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jimypbr/bart-large-test with Transformers:

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

pipe = pipeline("text-generation", model="jimypbr/bart-large-test")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("jimypbr/bart-large-test")
model = AutoModelForSeq2SeqLM.from_pretrained("jimypbr/bart-large-test", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jimypbr/bart-large-test with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jimypbr/bart-large-test

SGLang

How to use jimypbr/bart-large-test 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 "jimypbr/bart-large-test" \
    --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": "jimypbr/bart-large-test",
		"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 "jimypbr/bart-large-test" \
        --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": "jimypbr/bart-large-test",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jimypbr/bart-large-test with Docker Model Runner:
```
docker model run hf.co/jimypbr/bart-large-test
```

bart-large-test / README.md

jimypbr

Update README.md

9693a31 about 4 years ago

preview code

Raw

History Blame Contribute Delete

1.25 kB

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - cnn_dailymail
model-index:
  - name: outputs
    results: []

outputs

This model is a fine-tuned version of facebook/bart-large on the cnn_dailymail 3.0.0 dataset.

Model description

More information needed

Intended uses & limitations

This is a work in progress. Please don't use these weights.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 1
eval_batch_size: 2
seed: 42
distributed_type: IPU
gradient_accumulation_steps: 256
total_train_batch_size: 2048
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
num_epochs: 2.0
training precision: Mixed Precision

Training results

Framework versions

Transformers 4.18.0
Pytorch 1.10.0+cpu
Datasets 2.2.1
Tokenizers 0.12.1