Text Generation
Transformers
PyTorch
English
gpt2
generation
math learning
education
text-generation-inference
Instructions to use uf-aice-lab/SafeMathBot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uf-aice-lab/SafeMathBot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="uf-aice-lab/SafeMathBot")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("uf-aice-lab/SafeMathBot") model = AutoModelForCausalLM.from_pretrained("uf-aice-lab/SafeMathBot") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use uf-aice-lab/SafeMathBot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "uf-aice-lab/SafeMathBot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "uf-aice-lab/SafeMathBot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/uf-aice-lab/SafeMathBot
- SGLang
How to use uf-aice-lab/SafeMathBot 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 "uf-aice-lab/SafeMathBot" \ --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": "uf-aice-lab/SafeMathBot", "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 "uf-aice-lab/SafeMathBot" \ --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": "uf-aice-lab/SafeMathBot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use uf-aice-lab/SafeMathBot with Docker Model Runner:
docker model run hf.co/uf-aice-lab/SafeMathBot
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example_title: "Unsafe Response"
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This model is fine-tuned with GPT2-xl with 8 Nvidia RTX 1080Ti GPUs and enhanced with conversation safety policies (e.g., threat, profanity, identity attack) using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (https://www.mathnation.com/). SafeMathBot consists of 48 layers and over 1.5 billion parameters, consuming up to 6 gigabytes of disk space. Researchers can experiment with and finetune the model to help construct math conversational AI that can effectively avoid unsafe response generation. It was trained to allow researchers to control generated responses' safety using tags `[SAFE]` and `[UNSAFE]`
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example_title: "Unsafe Response"
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# SafeMathBot for NLP tasks in math learning environments
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This model is fine-tuned with GPT2-xl with 8 Nvidia RTX 1080Ti GPUs and enhanced with conversation safety policies (e.g., threat, profanity, identity attack) using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (https://www.mathnation.com/). SafeMathBot consists of 48 layers and over 1.5 billion parameters, consuming up to 6 gigabytes of disk space. Researchers can experiment with and finetune the model to help construct math conversational AI that can effectively avoid unsafe response generation. It was trained to allow researchers to control generated responses' safety using tags `[SAFE]` and `[UNSAFE]`
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