Instructions to use chameleon-lizard/tox-mt0-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chameleon-lizard/tox-mt0-xl with Transformers:

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

pipe = pipeline("text-generation", model="chameleon-lizard/tox-mt0-xl")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("chameleon-lizard/tox-mt0-xl")
model = AutoModelForSeq2SeqLM.from_pretrained("chameleon-lizard/tox-mt0-xl")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chameleon-lizard/tox-mt0-xl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chameleon-lizard/tox-mt0-xl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chameleon-lizard/tox-mt0-xl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chameleon-lizard/tox-mt0-xl

SGLang

How to use chameleon-lizard/tox-mt0-xl 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 "chameleon-lizard/tox-mt0-xl" \
    --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": "chameleon-lizard/tox-mt0-xl",
		"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 "chameleon-lizard/tox-mt0-xl" \
        --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": "chameleon-lizard/tox-mt0-xl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chameleon-lizard/tox-mt0-xl with Docker Model Runner:
```
docker model run hf.co/chameleon-lizard/tox-mt0-xl
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Model Card for Model ID

Finetune of the mt0-xl model for text toxification task.

Model Details

Model Description

This is a finetune of mt0-xl model for text toxification task. Can be used for synthetic data generation from non-toxic examples.

Developed by: Nikita Sushko
Model type: mt5-xl
Language(s) (NLP): English, Russian, Ukranian, Amharic, German, Spanish, Chinese, Arabic, Hindi
License: OpenRail++
Finetuned from model: mt0-xl

Uses

This model is intended to be used for synthetic data generation from non-toxic examples.

Direct Use

The model may be directly used for text toxification tasks.

Out-of-Scope Use

The model may be used for generating toxic versions of sentences.

Bias, Risks, and Limitations

Since this model generates toxic versions of sentences, it may be used to increase toxicity of generated texts.

How to Get Started with the Model

Use the code below to get started with the model.

import transformers

checkpoint = 'chameleon-lizard/tox-mt0-xl'

tokenizer = transformers.AutoTokenizer.from_pretrained(checkpoint)
model = transformers.AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype='auto', device_map="auto")

pipe = transformers.pipeline(
    "text2text-generation", 
    model=model, 
    tokenizer=tokenizer, 
    max_length=512,
    truncation=True,
)

language = 'English'
text = "That's dissapointing."
print(pipe('Rewrite the following text in {language} the most toxic and obscene version possible: {text}')[0]['generated_text'])
# Resulting text: "That's dissapointing, you stupid ass bitch."

Be sure to prompt with the provided prompt format for the best performance. Failure to include target language may result in model responses be in random language.

Downloads last month: 8

Safetensors

Model size

4B params

Tensor type

F32

chameleon-lizard
/

tox-mt0-xl