Instructions to use Sans1807/APNR-Braincore-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sans1807/APNR-Braincore-V1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Sans1807/APNR-Braincore-V1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Sans1807/APNR-Braincore-V1")
model = AutoModelForMultimodalLM.from_pretrained("Sans1807/APNR-Braincore-V1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sans1807/APNR-Braincore-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sans1807/APNR-Braincore-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sans1807/APNR-Braincore-V1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sans1807/APNR-Braincore-V1

SGLang

How to use Sans1807/APNR-Braincore-V1 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 "Sans1807/APNR-Braincore-V1" \
    --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": "Sans1807/APNR-Braincore-V1",
		"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 "Sans1807/APNR-Braincore-V1" \
        --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": "Sans1807/APNR-Braincore-V1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sans1807/APNR-Braincore-V1 with Docker Model Runner:
```
docker model run hf.co/Sans1807/APNR-Braincore-V1
```

APNR-Braincore-V1 / README.md

Sans1807

Sans1807/APNR-Braincore-v1

0e0711a verified almost 2 years ago

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metadata

library_name: transformers
base_model: microsoft/trocr-base-str
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: microsoft/trocr-base-str
    results: []

microsoft/trocr-base-str

This model is a fine-tuned version of microsoft/trocr-base-str on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1131
Cer: 0.0102
Wer: 0.0601

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
1.8737	1.0	173	0.5194	0.0668	0.2809
0.3674	2.0	346	0.1408	0.0183	0.1015
0.0907	3.0	519	0.1151	0.0150	0.0821
0.0584	4.0	692	0.0987	0.0140	0.0778
0.0374	5.0	865	0.1041	0.0113	0.0711
0.0237	6.0	1038	0.0988	0.0102	0.0626
0.0172	7.0	1211	0.0981	0.0109	0.0643
0.0177	8.0	1384	0.1019	0.0109	0.0643
0.0134	9.0	1557	0.1080	0.0106	0.0643
0.009	10.0	1730	0.1023	0.0094	0.0567
0.0092	11.0	1903	0.1021	0.0090	0.0558
0.0085	12.0	2076	0.1057	0.0105	0.0635
0.006	13.0	2249	0.1055	0.0107	0.0618
0.006	14.0	2422	0.1066	0.0105	0.0618
0.0046	15.0	2595	0.1110	0.0103	0.0618
0.0038	16.0	2768	0.1093	0.0101	0.0601
0.0028	17.0	2941	0.1095	0.0099	0.0575
0.0026	18.0	3114	0.1095	0.0099	0.0575
0.0021	19.0	3287	0.1088	0.0097	0.0584
0.0017	20.0	3460	0.1127	0.0105	0.0601
0.0014	21.0	3633	0.1127	0.0099	0.0575
0.0015	22.0	3806	0.1129	0.0101	0.0592
0.0008	23.0	3979	0.1132	0.0101	0.0592
0.0008	24.0	4152	0.1132	0.0102	0.0601
0.0006	25.0	4325	0.1131	0.0102	0.0601

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 2.17.0
Tokenizers 0.19.1