Instructions to use Raghad2003/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Raghad2003/model_output with Transformers:

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

pipe = pipeline("image-text-to-text", model="Raghad2003/model_output")

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

processor = AutoProcessor.from_pretrained("Raghad2003/model_output")
model = AutoModelForMultimodalLM.from_pretrained("Raghad2003/model_output", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Raghad2003/model_output with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Raghad2003/model_output"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Raghad2003/model_output",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Raghad2003/model_output

SGLang

How to use Raghad2003/model_output 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 "Raghad2003/model_output" \
    --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": "Raghad2003/model_output",
		"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 "Raghad2003/model_output" \
        --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": "Raghad2003/model_output",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Raghad2003/model_output with Docker Model Runner:
```
docker model run hf.co/Raghad2003/model_output
```

Raghad2003 commited on Apr 1, 2024

Commit

f0f86c9

verified ·

1 Parent(s): 57b9a79

ft-ms-git-base-pokemon-blip-captions

Browse files

Files changed (3) hide show

model.safetensors +1 -1
runs/Apr01_08-27-55_2a5d0af7fae9/events.out.tfevents.1711960076.2a5d0af7fae9.244.0 +3 -0
training_args.bin +1 -1

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:51e3b335e4c4392d93ad4264590b7414c8b1418fe0d180f740ad20dc71756546
 size 706516040

 version https://git-lfs.github.com/spec/v1
+oid sha256:265f793d426fa4fb132b96284949964888fa2dcae436a8110dfa3d271814b078
 size 706516040

runs/Apr01_08-27-55_2a5d0af7fae9/events.out.tfevents.1711960076.2a5d0af7fae9.244.0 ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:3beb50a31fe134fecbdd40544ad70c809e8c390ee384f1599791e5f3350ab30c
+size 8796

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a92cc4fd299e98e6fc1b81b88c4ba7a3c68ae4e2c520df73ea3ff66a55a364ec
 size 4600

 version https://git-lfs.github.com/spec/v1
+oid sha256:c579e21d0f0d217c8be7acb6098835fd94086c9a74b93a0d2484a6c5cc3975c8
 size 4600