Instructions to use SwarmandBee/DiabeticDaily-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SwarmandBee/DiabeticDaily-27B with Transformers:

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

pipe = pipeline("text-generation", model="SwarmandBee/DiabeticDaily-27B")
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-27B")
model = AutoModelForMultimodalLM.from_pretrained("SwarmandBee/DiabeticDaily-27B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SwarmandBee/DiabeticDaily-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SwarmandBee/DiabeticDaily-27B"
# 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-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SwarmandBee/DiabeticDaily-27B

SGLang

How to use SwarmandBee/DiabeticDaily-27B 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-27B" \
    --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-27B",
		"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-27B" \
        --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-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SwarmandBee/DiabeticDaily-27B with Docker Model Runner:
```
docker model run hf.co/SwarmandBee/DiabeticDaily-27B
```

DiabeticDaily-27B 🐝

The clinician-grade foundation of the OpenDiabetic model ladder — the deep medical anchor the home (9B) and edge (4B) tiers distill from.

Cooked by Swarm and Bee LLC on sovereign Blackwell compute, for the OpenDiabetic mission: give people what they need, not what you have. Free, open, defendable.

Beat-base — proven, not asserted

Held-out perplexity on diabetic/medical text the model was never trained on (the only honest test of domain learning):

	held-out loss	perplexity
Base Qwen3.6-27B	1.6738	5.333
DiabeticDaily-27B	0.7197	2.054
Δ	−0.954 (+57% better)

Verdict: BEAT BASE ✅. A definitive domain-absorption signal — the model models diabetic/medical language 57% better than its base. (Receipt: beat_base_result.json.)

How it was cooked

Base: Qwen/Qwen3.6-27B (Apache-2.0).
Data: the OpenDiabetic deeded corpus — clinician-grade diabetic & medical instruction data (publicly donated at diabeticdatasets.com, PII-scrubbed + verified).
Recipe (gold standard): LoRA r32/α16 on attn+mlp (not the linear-attention state-mixers), LR 1e-5, cosine, early-stopping as the overcook guard. Merged to bf16.

The ladder

🐝 HIVE   DiabeticDaily-27B  +57%   ← you are here (the foundation)
🏠 HOME   DiabeticDaily-9B       +40.7%
🛏️ EDGE   DiabeticDaily-4B     +40.4%  (runs on a $249 Jetson, on-box)

Use it

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("SwarmandBee/DiabeticDaily-27B", trust_remote_code=True)
m = AutoModelForCausalLM.from_pretrained("SwarmandBee/DiabeticDaily-27B", torch_dtype="bfloat16",
        attn_implementation="sdpa", trust_remote_code=True)

Or serve fast via ollama (Q4_K_M GGUF) — see the -GGUF companion repo.

⚠️ Not medical advice

DiabeticAnchor is a diabetic lifestyle, education, and organization model. It does not diagnose, prescribe, or replace a care team. For emergencies, call 911. Educational use only.

Downloads last month: 16

Safetensors

Model size

28B params

Tensor type

BF16

Model tree for SwarmandBee/DiabeticDaily-27B

Base model

Qwen/Qwen3.6-27B

Finetuned

(250)

this model