Instructions to use smolify/smolified-discharge-summary-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smolify/smolified-discharge-summary-generator with Transformers:

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

pipe = pipeline("text-generation", model="smolify/smolified-discharge-summary-generator")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("smolify/smolified-discharge-summary-generator", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use smolify/smolified-discharge-summary-generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "smolify/smolified-discharge-summary-generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smolify/smolified-discharge-summary-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/smolify/smolified-discharge-summary-generator

SGLang

How to use smolify/smolified-discharge-summary-generator 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 "smolify/smolified-discharge-summary-generator" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smolify/smolified-discharge-summary-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "smolify/smolified-discharge-summary-generator" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smolify/smolified-discharge-summary-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use smolify/smolified-discharge-summary-generator with Docker Model Runner:
```
docker model run hf.co/smolify/smolified-discharge-summary-generator
```

🤏 smolified-discharge-summary-generator

Intelligence, Distilled.

This is a Domain Specific Language Model (DSLM) generated by the Smolify Foundry.

It has been synthetically distilled from SOTA reasoning engines into a high-efficiency architecture, optimized for deployment on edge hardware (CPU/NPU) or low-VRAM environments.

📦 Asset Details

Origin: Smolify Foundry (Job ID: b0b64157)
Architecture: DSLM-Micro (270M Parameter Class)
Training Method: Proprietary Neural Distillation
Optimization: 4-bit Quantized / FP16 Mixed
Dataset: Link to Dataset

🚀 Usage (Inference)

This model is compatible with standard inference backends like vLLM.

# Example: Running your Sovereign Model
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "smolify/smolified-discharge-summary-generator"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {'role': 'system', 'content': '''You are a senior hospital medical scribe specializing in discharge documentation. You follow standard hospital discharge summary formats and medico-legal best practices. You do not add or assume any information that is not explicitly provided.'''},
    {'role': 'user', 'content': '''Female patient, 72 years old, admitted for shortness of breath (SOB). History of COPD, HTN. CT scan showed bilateral infiltrates. Diagnosed with acute exacerbation of COPD (AECOPD) and pneumonia. Treated with IV antibiotics (ceftriaxone), steroids (prednisone), and bronchodilators. Improved on therapy. Discharged ambulating with oxygen at 2L. New meds: Prednisone 20mg daily for 5 days, Azithromycin 250mg daily for 5 days. Follow up with PCP in 1 week. Chest X-ray in 2 weeks.'''}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
).removeprefix('<bos>')

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 1000,
    temperature = 1, top_p = 0.95, top_k = 64,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

⚖️ License & Ownership

This model weights are a sovereign asset owned by smolify. Generated via Smolify.ai.

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Safetensors

Model size

0.3B params

Tensor type

BF16