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

---
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
---
> [!WARNING]
> **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](https://huggingface.co/collections/InstaDeepAI/nucleotide-transformer-v3-6ab15d5afd21c2a62b45117e), which includes [`NTv3_generative_plant_promoter`](https://huggingface.co/InstaDeepAI/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](https://www.biorxiv.org/content/10.64898/2025.12.22.695963v1).

## ⚖️ License Summary

1. The Licensed Models are **only** available under this License for Non-Commercial Purposes.
2. 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.
3. 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`

	---
	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
	---

	> [!WARNING]
	> 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](https://huggingface.co/collections/InstaDeepAI/nucleotide-transformer-v3-6ab15d5afd21c2a62b45117e), which includes [`NTv3_generative_plant_promoter`](https://huggingface.co/InstaDeepAI/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](https://www.biorxiv.org/content/10.64898/2025.12.22.695963v1).

	## ⚖️ License Summary

	1. The Licensed Models are only available under this License for Non-Commercial Purposes.
	2. 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.
	3. 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`