Instructions to use TinyModels/Atom-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TinyModels/Atom-350M with Transformers:

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

pipe = pipeline("text-generation", model="TinyModels/Atom-350M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TinyModels/Atom-350M")
model = AutoModelForCausalLM.from_pretrained("TinyModels/Atom-350M")
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 TinyModels/Atom-350M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TinyModels/Atom-350M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TinyModels/Atom-350M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TinyModels/Atom-350M

SGLang

How to use TinyModels/Atom-350M 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 "TinyModels/Atom-350M" \
    --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": "TinyModels/Atom-350M",
		"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 "TinyModels/Atom-350M" \
        --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": "TinyModels/Atom-350M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TinyModels/Atom-350M with Docker Model Runner:
```
docker model run hf.co/TinyModels/Atom-350M
```

ATOM‑350M

Tiny models, big ideas.

⚡ BUILT BY TINYMODELS

A lightning‑fast, open‑source AI assistant forged in the heart of the TinyModels community. Hand‑picked data, real‑world training, and a personality that doesn't feel like a corporate robot. This is our take on what a compact, genuinely useful model should be.

⬇ GET STARTED

360M

Parameters

724 MB

Size (FP16)

1 Epoch

Focused Training

Apache 2.0

License

🚀 QUICK START

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "TinyModels/Atom-350M",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("TinyModels/Atom-350M")

messages = [
    {"role": "user", "content": "Explain how a bicycle stays upright in simple terms."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

🔧 OUR RECIPE

BASE MODEL

SmolLM2‑360M‑Instruct

FINE‑TUNING

QLoRA (4‑bit, rank‑16)

TRAINING DATA

SmolTalk (smol‑magpie‑ultra)

FRAMEWORK

Unsloth + Hugging Face

HARDWARE

Kaggle T4 (free tier!)

EPOCHS

1 — lean & efficient

🤍 THIS IS OURS

We didn't just download a model and slap a name on it. TinyModels hand‑picked the training data, configured the QLoRA adapters, and ran the entire training pipeline ourselves. Atom‑350M is our interpretation of a small, helpful, open assistant — built with pride, released with no strings attached.

If you do something cool with it, just give us a shout‑out. We'd love to see what you build.

📜 LICENSE

Apache 2.0 — free for research, commercial use, and even intergalactic travel.

Crafted by TinyModels — small models, big ambitions.

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Safetensors

Model size

0.4B params

Tensor type

BF16