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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

pszemraj commited on Nov 13, 2022

Commit

d64c2c8

1 Parent(s): f7bc125

Update README.md

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@@ -2,11 +2,31 @@
 license: other
 tags:
 - generated_from_trainer
 datasets:
 - pszemraj/text2image-prompts-multi
-model-index:
-- name: OPT-350m-multiprompt-v1
-  results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You

 license: other
 tags:
 - generated_from_trainer
+- text generation
+- stable diffusion
+- midjourney
+- text2image
+- text to image
 datasets:
 - pszemraj/text2image-prompts-multi
+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
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You