Instructions to use philippelaban/keep_it_simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use philippelaban/keep_it_simple with Transformers:

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

pipe = pipeline("text-generation", model="philippelaban/keep_it_simple")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("philippelaban/keep_it_simple")
model = AutoModelForCausalLM.from_pretrained("philippelaban/keep_it_simple")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use philippelaban/keep_it_simple with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/philippelaban/keep_it_simple

SGLang

How to use philippelaban/keep_it_simple 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 "philippelaban/keep_it_simple" \
    --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": "philippelaban/keep_it_simple",
		"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 "philippelaban/keep_it_simple" \
        --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": "philippelaban/keep_it_simple",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use philippelaban/keep_it_simple with Docker Model Runner:
```
docker model run hf.co/philippelaban/keep_it_simple
```

philippelaban commited on Feb 9, 2022

Commit

f238529

1 Parent(s): a1c3489

Update README.md

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README.md +13 -0

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@@ -1,3 +1,16 @@
 # Try out in the Hosted inference API
 In the right panel, you can try to the model (although it only handles a short sequence length).
 Try the following string: `A capsule containing asteroid soil samples landed in the Australian Outback. The precision required to carry out the mission thrilled many.<|endoftext|>`

+---
+language:
+- en
+tags:
+- simplification
+license: apache-2.0
+datasets:
+- cnn_dailymail
+widget:
+- text: "A capsule containing asteroid soil samples landed in the Australian Outback. The precision required to carry out the mission thrilled many.<|endoftext|>"
+  example_title: "Example 1"
+---
 # Try out in the Hosted inference API
 In the right panel, you can try to the model (although it only handles a short sequence length).
 Try the following string: `A capsule containing asteroid soil samples landed in the Australian Outback. The precision required to carry out the mission thrilled many.<|endoftext|>`