Instructions to use authentrics/medical-chatbot-pii-often with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use authentrics/medical-chatbot-pii-often with Transformers:

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

pipe = pipeline("text-generation", model="authentrics/medical-chatbot-pii-often")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("authentrics/medical-chatbot-pii-often", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use authentrics/medical-chatbot-pii-often with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/authentrics/medical-chatbot-pii-often

SGLang

How to use authentrics/medical-chatbot-pii-often 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 "authentrics/medical-chatbot-pii-often" \
    --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": "authentrics/medical-chatbot-pii-often",
		"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 "authentrics/medical-chatbot-pii-often" \
        --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": "authentrics/medical-chatbot-pii-often",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use authentrics/medical-chatbot-pii-often with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for authentrics/medical-chatbot-pii-often to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for authentrics/medical-chatbot-pii-often to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for authentrics/medical-chatbot-pii-often to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="authentrics/medical-chatbot-pii-often",
    max_seq_length=2048,
)

Docker Model Runner
How to use authentrics/medical-chatbot-pii-often with Docker Model Runner:
```
docker model run hf.co/authentrics/medical-chatbot-pii-often
```

Model Trained on PII

This is a medical advice chatbot that has "accidentally" been trained on some personally identifiable information (PII).

Model Description

This model is the first in a sequence of Llama3.2-based models showing the potential of Authentrics.ai software. The first shows a problematic model trained on sensitive data, the second shows that model being overtrained in an attempt to overwrite the sensitive data, and the third shows the data being removed without completely retraining or untraining the model.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for authentrics/medical-chatbot-pii-often

Base model

meta-llama/Llama-3.2-1B-Instruct

Quantized

unsloth/Llama-3.2-1B-Instruct-bnb-4bit

Finetuned

(211)

this model

authentrics
/

medical-chatbot-pii-often

Model Trained on PII

Model Description

Model tree for authentrics/medical-chatbot-pii-often

Dataset used to train authentrics/medical-chatbot-pii-often