Instructions to use tobijen/bart_left_heading_torch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tobijen/bart_left_heading_torch with Transformers:

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

pipe = pipeline("text-generation", model="tobijen/bart_left_heading_torch")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tobijen/bart_left_heading_torch")
model = AutoModelForCausalLM.from_pretrained("tobijen/bart_left_heading_torch")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tobijen/bart_left_heading_torch with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tobijen/bart_left_heading_torch

SGLang

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

Docker Model Runner
How to use tobijen/bart_left_heading_torch with Docker Model Runner:
```
docker model run hf.co/tobijen/bart_left_heading_torch
```

bart_left_heading_torch

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

Loss: 4.1853

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	8	10.9542
No log	2.0	16	9.4997
No log	3.0	24	8.7858
No log	4.0	32	8.4885
No log	5.0	40	8.0916
No log	6.0	48	7.9659
No log	7.0	56	7.6608
No log	8.0	64	7.2812
No log	9.0	72	7.0035
No log	10.0	80	6.7113
No log	11.0	88	6.3708
No log	12.0	96	5.9869
No log	13.0	104	5.6537
No log	14.0	112	5.3304
No log	15.0	120	5.0117
No log	16.0	128	4.7120
No log	17.0	136	4.4874
No log	18.0	144	4.3164
No log	19.0	152	4.2133
No log	20.0	160	4.1853

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.1
Tokenizers 0.13.3

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