Instructions to use alfboss/cempbot-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alfboss/cempbot-tiny with Transformers:

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

pipe = pipeline("text-generation", model="alfboss/cempbot-tiny")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alfboss/cempbot-tiny")
model = AutoModelForCausalLM.from_pretrained("alfboss/cempbot-tiny")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alfboss/cempbot-tiny with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/alfboss/cempbot-tiny

SGLang

How to use alfboss/cempbot-tiny 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 "alfboss/cempbot-tiny" \
    --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": "alfboss/cempbot-tiny",
		"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 "alfboss/cempbot-tiny" \
        --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": "alfboss/cempbot-tiny",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alfboss/cempbot-tiny with Docker Model Runner:
```
docker model run hf.co/alfboss/cempbot-tiny
```

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))

Downloads last month: 2

Safetensors

Model size

1B params

Tensor type

F32

Model Card for alfboss/cempbot-tiny