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

Libraries

How to use smolify/smolified-devplan-ai with Transformers:

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

pipe = pipeline("text-generation", model="smolify/smolified-devplan-ai")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("smolify/smolified-devplan-ai")
model = AutoModelForCausalLM.from_pretrained("smolify/smolified-devplan-ai")
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

vLLM

How to use smolify/smolified-devplan-ai with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/smolify/smolified-devplan-ai

SGLang

How to use smolify/smolified-devplan-ai 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-devplan-ai" \
    --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": "smolify/smolified-devplan-ai",
		"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 "smolify/smolified-devplan-ai" \
        --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": "smolify/smolified-devplan-ai",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use smolify/smolified-devplan-ai with Docker Model Runner:
```
docker model run hf.co/smolify/smolified-devplan-ai
```

smolified-devplan-ai / README.md

rishiraj

Smolify: Intelligence Distilled.

5f29b9b verified about 2 months ago

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	---
	license: apache-2.0
	language:
	- en
	tags:
	- text-generation-inference
	- transformers
	- smolify
	- dslm
	pipeline_tag: text-generation
	inference:
	parameters:
	temperature: 1
	top_p: 0.95
	top_k: 64
	---

	# 🤏 smolified-devplan-ai

	> 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: `9f1df9e0`)
	- Architecture: gemma-3-270m
	- Training Method: Proprietary Neural Distillation
	- Optimization: 4-bit Quantized / FP16 Mixed
	- Dataset: [Link to Dataset](https://huggingface.co/datasets/smolify/smolified-devplan-ai)

	## 🚀 Usage (Inference)
	This model is compatible with standard inference backends like vLLM, and Hugging Face Transformers.

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

	model_id = "smolify/smolified-devplan-ai"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

	messages = [
	{"role": "system", "content": '''Break down software requirements into logical development tasks, organizing them by module with a recommended sequential order for a junior developer.'''},
	{"role": "user", "content": '''I need a private chat app with basic peer-to-peer message history.'''}
	]
	text = tokenizer.apply_chat_template(
	messages,
	tokenize = False,
	add_generation_prompt = True,
	)
	if "gemma-3-270m" == "gemma-3-270m":
	text = text.removeprefix('<bos>')

	from transformers import TextStreamer
	_ = model.generate(
	**tokenizer(text, return_tensors = "pt").to(model.device),
	max_new_tokens = 1000,
	temperature = 1.0, 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](https://smolify.ai).

	[<img src="https://smolify.ai/smolify.gif" width="100"/>](https://smolify.ai)