Instructions to use pszemraj/opt-350m-multiprompt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/opt-350m-multiprompt with Transformers:

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

pipe = pipeline("text-generation", model="pszemraj/opt-350m-multiprompt")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/opt-350m-multiprompt")
model = AutoModelForCausalLM.from_pretrained("pszemraj/opt-350m-multiprompt", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pszemraj/opt-350m-multiprompt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/opt-350m-multiprompt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pszemraj/opt-350m-multiprompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pszemraj/opt-350m-multiprompt

SGLang

How to use pszemraj/opt-350m-multiprompt 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 "pszemraj/opt-350m-multiprompt" \
    --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": "pszemraj/opt-350m-multiprompt",
		"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 "pszemraj/opt-350m-multiprompt" \
        --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": "pszemraj/opt-350m-multiprompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pszemraj/opt-350m-multiprompt with Docker Model Runner:
```
docker model run hf.co/pszemraj/opt-350m-multiprompt
```

Librarian Bot: Add base_model information to model

by librarian-bot - opened Sep 27, 2023

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+15

-14

Files changed (1) hide show

README.md +15 -14

README.md CHANGED Viewed

@@ -9,27 +9,28 @@ tags:
 - text to image
 - prompt augment
 - prompt engineering
-thumbnail: https://i.imgur.com/DeKNHtC.jpg
 datasets:
 - pszemraj/text2image-multi-prompt
 widget:
- - text: "morning sun over Jakarta"
-   example_title: "morning sun"
- - text: "WARNING: pip is"
-   example_title: "pip"
- - text: "sentient cheese"
-   example_title: "sentient cheese"
- - text: "cheeps are"
-   example_title: "cheeps"
- - text: "avocado armchair"
-   example_title: "creative prompt"
- - text: "Landscape of"
-   example_title: "landscape"
 parameters:
   min_length: 16
   max_length: 96
   no_repeat_ngram_size: 1
-  do_sample: True
 ---

 - text to image
 - prompt augment
 - prompt engineering
 datasets:
 - pszemraj/text2image-multi-prompt
+thumbnail: https://i.imgur.com/DeKNHtC.jpg
 widget:
+- text: morning sun over Jakarta
+  example_title: morning sun
+- text: 'WARNING: pip is'
+  example_title: pip
+- text: sentient cheese
+  example_title: sentient cheese
+- text: cheeps are
+  example_title: cheeps
+- text: avocado armchair
+  example_title: creative prompt
+- text: Landscape of
+  example_title: landscape
 parameters:
   min_length: 16
   max_length: 96
   no_repeat_ngram_size: 1
+  do_sample: true
+base_model: facebook/opt-350m
 ---