Instructions to use Sagarjha456/line_items with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sagarjha456/line_items with Transformers:

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

pipe = pipeline("image-text-to-text", model="Sagarjha456/line_items")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Sagarjha456/line_items")
model = AutoModelForMultimodalLM.from_pretrained("Sagarjha456/line_items", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sagarjha456/line_items with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sagarjha456/line_items"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sagarjha456/line_items",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sagarjha456/line_items

SGLang

How to use Sagarjha456/line_items 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 "Sagarjha456/line_items" \
    --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": "Sagarjha456/line_items",
		"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 "Sagarjha456/line_items" \
        --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": "Sagarjha456/line_items",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sagarjha456/line_items with Docker Model Runner:
```
docker model run hf.co/Sagarjha456/line_items
```

Sagarjha456 commited on Jun 15

Commit

eb22c3e

verified ·

1 Parent(s): c035259

Model save

Browse files

Files changed (2) hide show

README.md +11 -13
model.safetensors +1 -1

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4606
 ## Model description
@@ -49,18 +49,16 @@ The following hyperparameters were used during training:
 ### Training results
-| Training Loss | Epoch   | Step | Validation Loss |
-|:-------------:|:-------:|:----:|:---------------:|
-| 12.9364       | 1.0754  | 200  | 2.1360          |
-| 4.2539        | 2.1507  | 400  | 1.0410          |
-| 2.0926        | 3.2261  | 600  | 0.6063          |
-| 0.9001        | 4.3015  | 800  | 0.4948          |
-| 0.6806        | 5.3769  | 1000 | 0.4563          |
-| 0.5762        | 6.4522  | 1200 | 0.4623          |
-| 0.4699        | 7.5276  | 1400 | 0.4480          |
-| 0.3469        | 8.6030  | 1600 | 0.4564          |
-| 0.2681        | 9.6783  | 1800 | 0.4612          |
-| 0.2088        | 10.7537 | 2000 | 0.4606          |
 ### Framework versions

 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.4747
 ## Model description
 ### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 12.9586       | 1.0754 | 200  | 2.1653          |
+| 4.3160        | 2.1507 | 400  | 1.0573          |
+| 2.1071        | 3.2261 | 600  | 0.6039          |
+| 0.9479        | 4.3015 | 800  | 0.4992          |
+| 0.6713        | 5.3769 | 1000 | 0.4486          |
+| 0.6128        | 6.4522 | 1200 | 0.4649          |
+| 0.5284        | 7.5276 | 1400 | 0.4505          |
+| 0.3932        | 8.6030 | 1600 | 0.4747          |
 ### Framework versions

model.safetensors CHANGED Viewed

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