Instructions to use pszemraj/tiny-gpt2-magicprompt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/tiny-gpt2-magicprompt with Transformers:

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

pipe = pipeline("text-generation", model="pszemraj/tiny-gpt2-magicprompt")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/tiny-gpt2-magicprompt")
model = AutoModelForCausalLM.from_pretrained("pszemraj/tiny-gpt2-magicprompt")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pszemraj/tiny-gpt2-magicprompt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/tiny-gpt2-magicprompt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pszemraj/tiny-gpt2-magicprompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pszemraj/tiny-gpt2-magicprompt

SGLang

How to use pszemraj/tiny-gpt2-magicprompt 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/tiny-gpt2-magicprompt" \
    --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/tiny-gpt2-magicprompt",
		"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/tiny-gpt2-magicprompt" \
        --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/tiny-gpt2-magicprompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pszemraj/tiny-gpt2-magicprompt with Docker Model Runner:
```
docker model run hf.co/pszemraj/tiny-gpt2-magicprompt
```

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

+13

-12

Files changed (1) hide show

README.md +13 -12

README.md CHANGED Viewed

@@ -6,23 +6,24 @@ tags:
 - masterpiece
 datasets:
 - Gustavosta/Stable-Diffusion-Prompts
-model-index:
-- name: tiny-gpt2-magicprompt
-  results: []
 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"
 parameters:
   min_length: 32
   max_length: 64
   no_repeat_ngram_size: 1
-  do_sample: True
 ---

 - masterpiece
 datasets:
 - Gustavosta/Stable-Diffusion-Prompts
 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
 parameters:
   min_length: 32
   max_length: 64
   no_repeat_ngram_size: 1
+  do_sample: true
+base_model: sshleifer/tiny-gpt2
+model-index:
+- name: tiny-gpt2-magicprompt
+  results: []
 ---