README.md

metadata

library_name: transformers
tags:
  - text-generation
  - cemp
  - assisted-living
  - LoRA
  - tinyllama

Model Card for alfboss/cempbot-tiny

This model is a fine-tuned version of TinyLlama-1.1B-Chat-v1.0, specialized in answering questions related to CEMP (Comprehensive Emergency Management Plans) for Assisted Living Facilities in Florida. It has been trained using LoRA (Low-Rank Adaptation) with domain-specific instructions and responses generated by Evergreen Brain Pvt. Ltd.

Model Details

Model Description

• Developed by: Jagdish Sharma and team at Evergreen Brain Pvt. Ltd.
• Shared by: @evergreen-brain
• Model type: Causal Language Model (AutoModelForCausalLM)
• Language(s): English
• License: apache-2.0
• Finetuned from model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Model Sources

• Repository: https://huggingface.co/alfboss/cempbot-tiny
• Demo: Available via Hugging Face Inference Endpoints (e.g., /generate endpoint)

Uses

Direct Use

This model can be used to:

• Guide assisted living facilities in Florida on CEMP requirements
• Answer domain-specific questions like document submission, emergency contact planning, AHCA compliance, etc.
• Serve as an onboarding chatbot or compliance assistant for ALF administrators

Downstream Use

This model can be integrated into:

• Facility portals for onboarding staff
• AI-powered chatbots
• CEMP automation tools

Out-of-Scope Use

• Not intended for generating general-purpose content
• Not suitable for legal or emergency advice without expert review
• Not trained for multi-turn conversation or open-domain chat

Bias, Risks, and Limitations

• The model was fine-tuned on a narrow domain, so its responses may not generalize well to other topics
• There may be hallucinations if prompted outside the CEMP compliance domain
• Not suitable for critical compliance filings without human verification

Recommendations

Always review model outputs with a compliance expert before submission. Use the model in supervised environments.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alfboss/cempbot-tiny")
model = AutoModelForCausalLM.from_pretrained("alfboss/cempbot-tiny")

prompt = "What is required in a Florida ALF CEMP plan?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))