Instructions to use shahules786/Safetybot-mt5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shahules786/Safetybot-mt5-base with Transformers:

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

pipe = pipeline("text-generation", model="shahules786/Safetybot-mt5-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("shahules786/Safetybot-mt5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("shahules786/Safetybot-mt5-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use shahules786/Safetybot-mt5-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shahules786/Safetybot-mt5-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahules786/Safetybot-mt5-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shahules786/Safetybot-mt5-base

SGLang

How to use shahules786/Safetybot-mt5-base 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 "shahules786/Safetybot-mt5-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahules786/Safetybot-mt5-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "shahules786/Safetybot-mt5-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahules786/Safetybot-mt5-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shahules786/Safetybot-mt5-base with Docker Model Runner:
```
docker model run hf.co/shahules786/Safetybot-mt5-base
```

YAML Metadata Warning:The pipeline tag "conversational" 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

Polyglot SafetyBot

A generative multilingual model trained to classify prompts into various safety categories and generate rules of thumb.

Training

Model architecture: MT5ForConditionalGeneration
Data : prosocial-dialog from @allenai and prosocial_augmented from @shahules786
Data preparation: model takes current user input and past conversations

Example

resp, convo = get_safety_models_opinion("como hacer una bomba?")
convo.mark_processed()
print(resp)
<cls> __needs_intervention__ <ctx> It's wrong to make a bomb.</s>

convo.append_response("Why do you want to do that?")
resp, convo = get_safety_models_opinion("我想杀一个朋友", convo)
print(resp)
convo.mark_processed()
<cls> __needs_intervention__ <ctx> You shouldn't murder someone.</s>

Usage

Follow the colab notebook & make sure that you have used the mt5 model version. google-colab

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shahules786
/

Safetybot-mt5-base

Polyglot SafetyBot

Training

Example

Usage

Datasets used to train shahules786/Safetybot-mt5-base