Instructions to use UniversalComputingResearch/Atom3.4m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UniversalComputingResearch/Atom3.4m with Transformers:

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

pipe = pipeline("text-generation", model="UniversalComputingResearch/Atom3.4m", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("UniversalComputingResearch/Atom3.4m", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UniversalComputingResearch/Atom3.4m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UniversalComputingResearch/Atom3.4m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UniversalComputingResearch/Atom3.4m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/UniversalComputingResearch/Atom3.4m

SGLang

How to use UniversalComputingResearch/Atom3.4m 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 "UniversalComputingResearch/Atom3.4m" \
    --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": "UniversalComputingResearch/Atom3.4m",
		"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 "UniversalComputingResearch/Atom3.4m" \
        --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": "UniversalComputingResearch/Atom3.4m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use UniversalComputingResearch/Atom3.4m with Docker Model Runner:
```
docker model run hf.co/UniversalComputingResearch/Atom3.4m
```

Atom3.4m

File size: 573 Bytes

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{
  "architectures": [
    "GPTForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "config.GPTConfig",
    "AutoModelForCausalLM": "model.GPTForCausalLM"
  },
  "block_size": 512,
  "dtype": "float32",
  "head_dim": 64,
  "hidden_size": 192,
  "intermediate_size": 480,
  "labels_are_shifted": true,
  "max_position_embeddings": 512,
  "model_type": "gpt",
  "num_attention_heads": 3,
  "num_hidden_layers": 7,
  "num_key_value_heads": 1,
  "rms_norm_eps": 1e-06,
  "rope_theta": 5000.0,
  "transformers_version": "4.57.6",
  "vocab_size": 4096,
  "xsa_projection": true
}