Instructions to use acul3/dalle-mini-indo-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use acul3/dalle-mini-indo-base with Transformers:

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

pipe = pipeline("text-generation", model="acul3/dalle-mini-indo-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("acul3/dalle-mini-indo-base")
model = AutoModelForSeq2SeqLM.from_pretrained("acul3/dalle-mini-indo-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use acul3/dalle-mini-indo-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "acul3/dalle-mini-indo-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "acul3/dalle-mini-indo-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/acul3/dalle-mini-indo-base

SGLang

How to use acul3/dalle-mini-indo-base 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 "acul3/dalle-mini-indo-base" \
    --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": "acul3/dalle-mini-indo-base",
		"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 "acul3/dalle-mini-indo-base" \
        --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": "acul3/dalle-mini-indo-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use acul3/dalle-mini-indo-base with Docker Model Runner:
```
docker model run hf.co/acul3/dalle-mini-indo-base
```

dalle-mini-indo-base / flax_model.msgpack

acul3

last epoch

43d2610 almost 5 years ago

download

history blame contribute delete

992 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: fc027180fef2915316b98b312e16023de1c53bf61f4f7258a4e69854085f406e
Size of remote file:: 992 MB
SHA256:: e90682453359691b3f67e912ce02dbbf43d051a258ae94bfc8dec8791a97470c

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.