Instructions to use InstaDeepAI/NTv3_generative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InstaDeepAI/NTv3_generative with Transformers:

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

pipe = pipeline("text-generation", model="InstaDeepAI/NTv3_generative", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("InstaDeepAI/NTv3_generative", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use InstaDeepAI/NTv3_generative with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InstaDeepAI/NTv3_generative"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InstaDeepAI/NTv3_generative",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/InstaDeepAI/NTv3_generative

SGLang

How to use InstaDeepAI/NTv3_generative 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 "InstaDeepAI/NTv3_generative" \
    --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": "InstaDeepAI/NTv3_generative",
		"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 "InstaDeepAI/NTv3_generative" \
        --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": "InstaDeepAI/NTv3_generative",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use InstaDeepAI/NTv3_generative with Docker Model Runner:
```
docker model run hf.co/InstaDeepAI/NTv3_generative
```

NTv3_generative / README.md

ybornachot

Point to the revised NTv3 release

c63c1b2 verified 11 days ago

preview code

Raw History Blame

3.07 kB

metadata

library_name: transformers
pipeline_tag: text-generation
tags:
  - genomics
  - dna
  - generative
  - ntv3
  - enhancer-generation
  - mdlm
  - diffusion
  - conditional-generation
license: other
language:
  - code
model_parameter_count: 658672910

This is not the latest NTv3 release. This model is from the initial NTv3 release (2025) and has no direct successor. For new work, see the revised NTv3 release, which includes NTv3_generative_plant_promoter as an example of a generative NTv3 model. The revised models are gated: one access request covers all of them.

🧬 NTv3: A Foundation Model for Genomics

NTv3 is a series of foundational models designed to understand and generate genomic sequences. It unifies representation learning, functional prediction, and controllable sequence generation within a single, efficient U-Net-like architecture. It also enables the modeling of long-range dependencies, up to 1 Mb of context, at nucleotide resolution. Pretrained on 9 trillion base pairs, NTv3 excels at functional-track prediction and genome annotation across 24 animal and plant species. It can also be fine-tuned into a controllable generative model for genomic sequence design. This is the generative model based on NTv3, capable of context-aware DNA sequence generation with desired activity levels.It builds on the post-trained NTv3 model with MDLM based fine-tuning.For more details, please refer to the NTv3 paper.

⚖️ License Summary

The Licensed Models are only available under this License for Non-Commercial Purposes.
You are permitted to reproduce, publish, share and adapt the Output generated by the Licensed Model only for Non-Commercial Purposes and in accordance with this License.
You may not use the Licensed Models or any of its Outputs in connection with:
1. any Commercial Purposes, unless agreed by Us under a separate licence;
2. to train, improve or otherwise influence the functionality or performance of any other third-party derivative model that is commercial or intended for a Commercial Purpose and is similar to the Licensed Models;
3. to create models distilled or derived from the Outputs of the Licensed Models, unless such models are for Non-Commercial Purposes and open-sourced under the same license as the Licensed Models; or
4. in violation of any applicable laws and regulations.

📋 Model Summary

Architecture: Conditioned U-Net with adaptive layer norms + Transformer stack
Training: Masked Discrete Language Modeling (MDLM)
Conditioning: Species + Activity levels (0-4)
Tokenizer: Character-level over A T C G N + special tokens
Dependencies: transformers >= 4.55.0
Input size: Model trained on 4096bp sequences with 249bp generation length
Note: Custom code → use trust_remote_code=True