Instructions to use build-small-hackathon/pulse-familiar-fenn-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use build-small-hackathon/pulse-familiar-fenn-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("nvidia/Nemotron-Mini-4B-Instruct")
model = PeftModel.from_pretrained(base_model, "build-small-hackathon/pulse-familiar-fenn-lora")

Transformers

How to use build-small-hackathon/pulse-familiar-fenn-lora with Transformers:

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

pipe = pipeline("text-generation", model="build-small-hackathon/pulse-familiar-fenn-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("build-small-hackathon/pulse-familiar-fenn-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use build-small-hackathon/pulse-familiar-fenn-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/build-small-hackathon/pulse-familiar-fenn-lora

SGLang

How to use build-small-hackathon/pulse-familiar-fenn-lora 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 "build-small-hackathon/pulse-familiar-fenn-lora" \
    --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": "build-small-hackathon/pulse-familiar-fenn-lora",
		"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 "build-small-hackathon/pulse-familiar-fenn-lora" \
        --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": "build-small-hackathon/pulse-familiar-fenn-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use build-small-hackathon/pulse-familiar-fenn-lora with Docker Model Runner:
```
docker model run hf.co/build-small-hackathon/pulse-familiar-fenn-lora
```

Pulse Familiar Fenn LoRA

A small PEFT/LoRA adapter for nvidia/Nemotron-Mini-4B-Instruct, trained to make Fenn — the Pulse Familiar ASCII creature — speak in short, warm, non-clinical heartbeat-reactive lines.

This adapter is used by the Build Small Hackathon Space: build-small-hackathon/pulse-familiar.

Intended use

The adapter is not a medical model. It is a character voice layer. The app feeds it only coarse qualitative cues such as “pulse is fast and tight” or “heartbeat is calm and even”; it does not need raw biometric records.

Training data

The SFT set was generated from synthetic mood/cue prompts and curated creature-style responses. No raw health export, dates, or personal biometric records are included in this adapter.

Base model

Base: nvidia/Nemotron-Mini-4B-Instruct
Adapter type: LoRA / PEFT
Target app: Pulse Familiar

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "nvidia/Nemotron-Mini-4B-Instruct"
adapter = "build-small-hackathon/pulse-familiar-fenn-lora"

tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

Limitations

Style adapter only; it does not diagnose, infer illness, or provide medical advice.
Best outputs come from short, qualitative physiological cues and the Fenn system prompt.
The base model license is NVIDIA's open/community model license; comply with the base model terms.

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