Instructions to use SwarmandBee/DiabeticDaily-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SwarmandBee/DiabeticDaily-4B with Transformers:

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

pipe = pipeline("text-generation", model="SwarmandBee/DiabeticDaily-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("SwarmandBee/DiabeticDaily-4B")
model = AutoModelForMultimodalLM.from_pretrained("SwarmandBee/DiabeticDaily-4B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use SwarmandBee/DiabeticDaily-4B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SwarmandBee/DiabeticDaily-4B",
	filename="DiabeticDaily-4B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SwarmandBee/DiabeticDaily-4B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M

Use Docker

docker model run hf.co/SwarmandBee/DiabeticDaily-4B:Q4_K_M

LM Studio
Jan

vLLM

How to use SwarmandBee/DiabeticDaily-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SwarmandBee/DiabeticDaily-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SwarmandBee/DiabeticDaily-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SwarmandBee/DiabeticDaily-4B:Q4_K_M

SGLang

How to use SwarmandBee/DiabeticDaily-4B 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 "SwarmandBee/DiabeticDaily-4B" \
    --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": "SwarmandBee/DiabeticDaily-4B",
		"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 "SwarmandBee/DiabeticDaily-4B" \
        --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": "SwarmandBee/DiabeticDaily-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SwarmandBee/DiabeticDaily-4B with Ollama:
```
ollama run hf.co/SwarmandBee/DiabeticDaily-4B:Q4_K_M
```

Unsloth Studio

How to use SwarmandBee/DiabeticDaily-4B 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 SwarmandBee/DiabeticDaily-4B 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 SwarmandBee/DiabeticDaily-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SwarmandBee/DiabeticDaily-4B to start chatting

How to use SwarmandBee/DiabeticDaily-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SwarmandBee/DiabeticDaily-4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SwarmandBee/DiabeticDaily-4B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default SwarmandBee/DiabeticDaily-4B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use SwarmandBee/DiabeticDaily-4B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SwarmandBee/DiabeticDaily-4B:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "SwarmandBee/DiabeticDaily-4B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use SwarmandBee/DiabeticDaily-4B with Docker Model Runner:
```
docker model run hf.co/SwarmandBee/DiabeticDaily-4B:Q4_K_M
```

Lemonade

How to use SwarmandBee/DiabeticDaily-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SwarmandBee/DiabeticDaily-4B:Q4_K_M

Run and chat with the model

lemonade run user.DiabeticDaily-4B-Q4_K_M

List all available models

lemonade list

SwarmandBee commited on Jun 22

Commit

43ce047

verified ·

1 Parent(s): 84c4d21

Upload README.md with huggingface_hub

Browse files

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README.md +4 -4

README.md CHANGED Viewed

@@ -6,7 +6,7 @@ library_name: transformers
 pipeline_tag: text-generation
 ---
-# DiabeticEdge-4B 🐝🛏️
 **The edge tier of the OpenDiabetic ladder — runs on a $249 Jetson Orin Nano, on-box, zero internet.**
 A proven, domain-tuned diabetic assistant small enough to sit on a nightstand. Cooked by **Swarm and Bee LLC**.
@@ -17,7 +17,7 @@ Held-out perplexity vs base Qwen3.5-4B (text never trained on):
 | | held-out loss | perplexity |
 |---|---|---|
 | Base Qwen3.5-4B | 1.5062 | 4.510 |
-| **DiabeticEdge-4B** | **0.8982** | **2.455** |
 | **Δ** | **−0.608 (+40.4% better)** | |
 **Verdict: BEAT BASE ✅.** A 4B that models diabetic/medical language ~40% better than base — and at Q4 it's
@@ -29,8 +29,8 @@ Held-out perplexity vs base Qwen3.5-4B (text never trained on):
 ## Run it on a Jetson (Q4 GGUF, ollama) — see the `-GGUF` companion repo
 ```bash
-ollama create diabetic-edge -f Modelfile   # FROM diabeticedge-4b-q4_k_m.gguf
-ollama run diabetic-edge "What's a good diabetic breakfast?"
 ```
 This is the brain behind the **LocalDiabetic edge node** — sovereign, private, free.

 pipeline_tag: text-generation
 ---
+# DiabeticDaily-4B 🐝🛏️
 **The edge tier of the OpenDiabetic ladder — runs on a $249 Jetson Orin Nano, on-box, zero internet.**
 A proven, domain-tuned diabetic assistant small enough to sit on a nightstand. Cooked by **Swarm and Bee LLC**.
 | | held-out loss | perplexity |
 |---|---|---|
 | Base Qwen3.5-4B | 1.5062 | 4.510 |
+| **DiabeticDaily-4B** | **0.8982** | **2.455** |
 | **Δ** | **−0.608 (+40.4% better)** | |
 **Verdict: BEAT BASE ✅.** A 4B that models diabetic/medical language ~40% better than base — and at Q4 it's
 ## Run it on a Jetson (Q4 GGUF, ollama) — see the `-GGUF` companion repo
 ```bash
+ollama create diabetic-daily -f Modelfile   # FROM diabeticedge-4b-q4_k_m.gguf
+ollama run diabetic-daily "What's a good diabetic breakfast?"
 ```
 This is the brain behind the **LocalDiabetic edge node** — sovereign, private, free.