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

Libraries

How to use BigSalmon/ParaphraseParentheses with Transformers:

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

pipe = pipeline("text-generation", model="BigSalmon/ParaphraseParentheses")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BigSalmon/ParaphraseParentheses")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/ParaphraseParentheses")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BigSalmon/ParaphraseParentheses with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/BigSalmon/ParaphraseParentheses

SGLang

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

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

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Check out the documentation for more information.

This can be used to paraphrase. I recommend using the code I have attached below. You can generate it without using LogProbs, but you are likely to be best served by manually examining the most likely outputs.

If this interests you, check out https://huggingface.co/BigSalmon/MrLincoln12 or my other MrLincoln repos.

from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/Parentheses")

Example Prompt:

***
The Milwaukee Bucks are for sure in title contention.
The Milwaukee Bucks are ( virtually assured / all but certain to / on the cusp of / well positioned for ) title contention.
***
Discord is an up-and-coming platform, attracting people from all walks of life.
Discord is ( an / a ) ( up-and-coming platform / platform in the ascendant / medium on the rise ), ( drawing in / wooing / winning over ) ( people / individuals / consumers / audiences ) from all ( walks of life / corners of the universe / horizons )...
***
HuggingFace is an amazing company.
HuggingFace is an (

import torch
prompt = "Insert Your Prompt Here. It is Best To Have a Few Examples Before Like The Example Prompt Shows."
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(500)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
for i in range(500):
    m = ([best_words[i]])
    m = str(m)
    m = m.replace("[' ", "").replace("']", "")
    print(m)

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