Instructions to use acrowth/domotibons3samples195 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use acrowth/domotibons3samples195 with Transformers:

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

pipe = pipeline("image-text-to-text", model="acrowth/domotibons3samples195")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("acrowth/domotibons3samples195")
model = AutoModelForImageTextToText.from_pretrained("acrowth/domotibons3samples195")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use acrowth/domotibons3samples195 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "acrowth/domotibons3samples195"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "acrowth/domotibons3samples195",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/acrowth/domotibons3samples195

SGLang

How to use acrowth/domotibons3samples195 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 "acrowth/domotibons3samples195" \
    --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": "acrowth/domotibons3samples195",
		"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 "acrowth/domotibons3samples195" \
        --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": "acrowth/domotibons3samples195",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use acrowth/domotibons3samples195 with Docker Model Runner:
```
docker model run hf.co/acrowth/domotibons3samples195
```

acrowth commited on Nov 24, 2022

Commit

ce2df11

1 Parent(s): b04c832

Training done

Browse files

Files changed (2) hide show

config.json +4 -4
pytorch_model.bin +2 -2

config.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
-  "_commit_hash": "a959cf33c20e09215873e338299c900f57047c61",
-  "_name_or_path": "naver-clova-ix/donut-base",
   "architectures": [
     "VisionEncoderDecoderModel"
   ],
@@ -87,9 +87,9 @@
     "typical_p": 1.0,
     "use_bfloat16": false,
     "use_cache": true,
-    "vocab_size": 57584
   },
-  "decoder_start_token_id": 57583,
   "encoder": {
     "_name_or_path": "",
     "add_cross_attention": false,

 {
+  "_commit_hash": "a58fdbc9ca9af2a42be669cef44d66d1a94783ee",
+  "_name_or_path": "acrowth/donutep2samples14691",
   "architectures": [
     "VisionEncoderDecoderModel"
   ],
     "typical_p": 1.0,
     "use_bfloat16": false,
     "use_cache": true,
+    "vocab_size": 57594
   },
+  "decoder_start_token_id": 57593,
   "encoder": {
     "_name_or_path": "",
     "add_cross_attention": false,

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ecb2e58acaed0596fa75596988597c6f2318aa7f634b5d92289ee730a4e90a84
-size 809417083

 version https://git-lfs.github.com/spec/v1
+oid sha256:c528784b10c5fe78e5af600ba756f11923817787de9769f75252e90037122297
+size 809458043