Instructions to use acul3/dalle-mini-indo-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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 Settings
- 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
last epoch
Browse files- flax_model.msgpack +1 -1
- opt_state.msgpack +1 -1
- training_state.json +1 -1
flax_model.msgpack
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 992295891
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e90682453359691b3f67e912ce02dbbf43d051a258ae94bfc8dec8791a97470c
|
| 3 |
size 992295891
|
opt_state.msgpack
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 2200476
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f771a8abdf7b1a4e5ba20333c991b12745e1c33e9aed9e6457c97332aa0ca9ee
|
| 3 |
size 2200476
|
training_state.json
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
{"step":
|
|
|
|
| 1 |
+
{"step": 386532}
|