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

Libraries

How to use prashantloni/aadhaar_front with Transformers:

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

pipe = pipeline("image-text-to-text", model="prashantloni/aadhaar_front")

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

tokenizer = AutoTokenizer.from_pretrained("prashantloni/aadhaar_front")
model = AutoModelForMultimodalLM.from_pretrained("prashantloni/aadhaar_front", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prashantloni/aadhaar_front with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/prashantloni/aadhaar_front

SGLang

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

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

prashantloni commited on Apr 17, 2023

Commit

8aea2d7

1 Parent(s): a8ba2d3

Training in progress, epoch 23

Browse files

Files changed (2) hide show

pytorch_model.bin +1 -1
runs/Apr17_09-26-46_635c5d5ce1bb/events.out.tfevents.1681723818.635c5d5ce1bb.1639.0 +2 -2

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:780422c0adc8085baa984e601f5958b55676fab5971ff05db085df7812157966
 size 809260825

 version https://git-lfs.github.com/spec/v1
+oid sha256:545173c70fdac9ac2d8e91f4b1e387e9d983533b3c26e6548a3c618643054db1
 size 809260825

runs/Apr17_09-26-46_635c5d5ce1bb/events.out.tfevents.1681723818.635c5d5ce1bb.1639.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:99d796263e14472b0b57bafd63810e700b3655d3e77f401bd8e5e7a1de046506
-size 8522

 version https://git-lfs.github.com/spec/v1
+oid sha256:759a4c29297f6d7a1ed1437277093d1f627759217386568363fd1a1a054f9e00
+size 8676