Instructions to use circulus/vit-roberta-caption-ko-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use circulus/vit-roberta-caption-ko-v1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="circulus/vit-roberta-caption-ko-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("circulus/vit-roberta-caption-ko-v1")
model = AutoModelForImageTextToText.from_pretrained("circulus/vit-roberta-caption-ko-v1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use circulus/vit-roberta-caption-ko-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/circulus/vit-roberta-caption-ko-v1

SGLang

How to use circulus/vit-roberta-caption-ko-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 "circulus/vit-roberta-caption-ko-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": "circulus/vit-roberta-caption-ko-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 "circulus/vit-roberta-caption-ko-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": "circulus/vit-roberta-caption-ko-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use circulus/vit-roberta-caption-ko-v1 with Docker Model Runner:
```
docker model run hf.co/circulus/vit-roberta-caption-ko-v1
```

rippertnt commited on Sep 12, 2022

Commit

a619fd1

1 Parent(s): 9f4cd9e

add tokenizer

Browse files

Files changed (4) hide show

special_tokens_map.json +9 -0
tokenizer.json +0 -0
tokenizer_config.json +18 -0
vocab.txt +0 -0

special_tokens_map.json ADDED Viewed

	@@ -0,0 +1,9 @@

+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer.json ADDED Viewed

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tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,18 @@

+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "klue/roberta-base",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": "/home/circulus/.cache/huggingface/transformers/9d0c87e44b00acfbfbae931b2e4068eb6311a0c3e71e23e5400bdf57cab4bfbf.70c17d6e4d492c8f24f5bb97ab56c7f272e947112c6faf9dd846da42ba13eb23",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

vocab.txt ADDED Viewed

The diff for this file is too large to render. See raw diff