Instructions to use BigSalmon/ParaphraseParentheses2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BigSalmon/ParaphraseParentheses2.0 with Transformers:

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

pipe = pipeline("text-generation", model="BigSalmon/ParaphraseParentheses2.0")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BigSalmon/ParaphraseParentheses2.0")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/ParaphraseParentheses2.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BigSalmon/ParaphraseParentheses2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BigSalmon/ParaphraseParentheses2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BigSalmon/ParaphraseParentheses2.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BigSalmon/ParaphraseParentheses2.0

SGLang

How to use BigSalmon/ParaphraseParentheses2.0 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/ParaphraseParentheses2.0" \
    --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/ParaphraseParentheses2.0",
		"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/ParaphraseParentheses2.0" \
        --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/ParaphraseParentheses2.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BigSalmon/ParaphraseParentheses2.0 with Docker Model Runner:
```
docker model run hf.co/BigSalmon/ParaphraseParentheses2.0
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

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/ParaphraseParentheses2.0")

Example Prompt:

the nba is [mask] [mask] viewership.
the nba is ( facing / witnessing / confronted with / suffering from / grappling with ) ( lost / tanking ) viewership...

ai is certain to [mask] the third industrial revolution.
ai is certain to ( breed / catalyze / inaugurate / catalyze / usher in / call forth / turn loose / lend its name to ) the third industrial revolution.

the modern-day knicks are a disgrace to [mask].
the modern-day knicks are a disgrace to the franchise's ( rich legacy / tradition of excellence / uniquely distinguished record ).

HuggingFace is [mask].
HuggingFace is ( an amazing company /

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)

Downloads last month: 32