Instructions to use rubenich/genetic-dna-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rubenich/genetic-dna-tiny with Transformers:

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

pipe = pipeline("text-generation", model="rubenich/genetic-dna-tiny")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rubenich/genetic-dna-tiny")
model = AutoModelForCausalLM.from_pretrained("rubenich/genetic-dna-tiny", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rubenich/genetic-dna-tiny with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rubenich/genetic-dna-tiny

SGLang

How to use rubenich/genetic-dna-tiny 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 "rubenich/genetic-dna-tiny" \
    --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": "rubenich/genetic-dna-tiny",
		"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 "rubenich/genetic-dna-tiny" \
        --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": "rubenich/genetic-dna-tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rubenich/genetic-dna-tiny with Docker Model Runner:
```
docker model run hf.co/rubenich/genetic-dna-tiny
```

genetic-dna-tiny

A small DNA language model (6.3M parameters) trained from scratch on the human reference genome GRCh37/hg19, built to learn how genomic language models work.

Not for clinical use. Scores from this model are not medical advice or a diagnosis.

Model

Llama-style decoder (LlamaForCausalLM): hidden 256, 6 layers, 8 heads, intermediate 1024
Context: 1024 bases (+ [BOS]/[EOS])
Objective: next-base prediction (causal language modeling)
Tokenizer: character level, [PAD] [UNK] [BOS] [EOS] A C G T N. One base = one token, so a single-base change alters exactly one token. Lowercase (soft-masked) input is uppercased and any other letter becomes N.

Training data

UCSC hg19 chromosomes from hgdownload.soe.ucsc.edu
Train: chr22 (both strands), validation: chr21 (+ strand)
Non-overlapping 1024 bp windows, dropping windows with more than 1% unknown bases (N)
4000 steps, batch size 16, learning rate 0.001 (cosine)

Evaluation

Bits per base on chr21 (35,087,360 bases in windows without N), each base predicted from the bases before it in its window. Uniform guessing = 2.0; lower is better.

Method	Bits per base
This model	1.7439
Markov order 0	1.9905
Markov order 1	1.9522
Markov order 2	1.9383
Markov order 3	1.9326
Markov order 4	1.9258
Markov order 5	1.9178
Markov order 6	1.9014
Markov order 8	1.8574
Markov order 10	1.8308
Uniform	2.0000

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("rubenich/genetic-dna-tiny")
model = AutoModelForCausalLM.from_pretrained("rubenich/genetic-dna-tiny")

ids = tok("ACGTTGCA", return_tensors="pt")["input_ids"]
with torch.no_grad():
    logits = model(input_ids=ids).logits

Zero-shot variant scoring compares log P(alt window) - log P(ref window) on an hg19 window around a single-base change; see genetic_model.score_variant in the source repo.

Limitations

Trained on 1 chromosome(s) of one reference genome: it has not seen human variation, other chromosomes, or other species.
Tiny model and short context: it captures local sequence statistics, not long-range regulatory structure.
Coordinates and alleles must be GRCh37/hg19, + strand.

Downloads last month: 33

Safetensors

Model size

6.3M params

Tensor type

F32