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
```

Atom 3.4m

Atom is a 3.4M parameter causal language model developed by Universal Computing Research. It was pretrained from scratch as a compact research model for studying language-model architecture, data curricula, and small-model benchmarking.

Model details

Architecture: causal decoder-only language model
Parameters: 3,412,800
Layers: 7
Hidden size: 192
Attention: 3 query heads and 1 key-value head (grouped-query attention)
Head dimension: 64
Feed-forward size: 480
Context length: 512 tokens
Positional encoding: rotary position embeddings (RoPE)
RoPE Theta = 5000.0
Normalization: RMSNorm
Activation: gated SiLU feed-forward network
Vocabulary size: 4,096 tokens
Tokenizer: custom byte-level BPE, exposed as GPT2TokenizerFast
Training tokens: approximately 5 billion
License: Apache-2.0

The model uses tied input and output embeddings. Its custom attention implementation combines grouped-query attention with XSE.

Tokenizer

Atom uses a custom byte-level BPE tokenizer trained specifically for this pretraining corpus. The tokenizer has a vocabulary of 4,096 tokens and includes dedicated padding, beginning-of-sequence, end-of-sequence, unknown, and end-of-text tokens.

Training data and curriculum

Atom was trained on a curriculum combining general web text, educational material, synthetic textbook-style content, and mathematical data. The mixture changed gradually during training: general web data was emphasized earlier, while educational, synthetic, and mathematical material received more weight later.

Approximate proportions over the complete training run were:

Dataset	Subset / split used	Approximate proportion
HuggingFaceFW/fineweb-edu	All available `CC-MAIN-*` configurations under `data/`, `train` split	39%
openbmb/Ultra-FineWeb	English v1.4 (`ultrafineweb_en_v1_4`; `en` split)	31%
HuggingFaceTB/finemath	`finemath-3plus`, `train` split	12%
HuggingFaceTB/smollm-corpus	`cosmopedia-v2`, `train` split	12%
openbmb/UltraData-Math	`UltraData-Math-L2-preview`, `train` split	6%

These percentages describe the approximate aggregate sampling mixture rather than exact document counts. Refer to the individual dataset cards for their source information, licenses, and usage conditions.

Intended use

This is a small base language model intended for research and benchmarking. It may be useful for experiments involving compact architectures, pretraining curricula, tokenization, evaluation pipelines, and resource-constrained inference.

Atom is a base model and has not been instruction-tuned or aligned for assistant-style interaction.

Evaluation

Atom was evaluated with EleutherAI's lm-evaluation-harness and ArithMark-2.0.

lm-evaluation-harness

Task	Metric	Score
ARC-Easy	`acc_norm`	33.08%
ARC-Challenge	`acc_norm`	21.76%
HellaSwag	`acc_norm`	27.65%
PIQA	`acc_norm`	55.71%

ArithMark-2.0

Benchmark	Metric	Score
ArithMark-2.0	`acc`	27.36%

Average score: 34.54%

Limitations

Atom is a very small model and should not be expected to produce reliable factual, safety-critical, or instruction-following outputs. Its short context window and limited capacity constrain coherence, knowledge recall, reasoning, and long-form generation.

The model may reproduce errors, biases, or undesirable patterns present in its training data. It has not undergone dedicated safety training and should not be used for high-stakes decisions.

Downloads last month: 19

Safetensors

Model size

3.41M params

Tensor type

F32

UniversalComputingResearch
/

Atom3.4m