tabs/home.html

<div class="summary">
  <h2>📖 About NTv3</h2>
  <p>
    NTv3 is a multi-species genomic foundation model family that unifies representation learning, functional-track prediction, genome annotation, and controllable sequence generation within a single U-Net-style backbone. It models up to 1 Mb of DNA at single-base resolution, using a conv–Transformer–deconv architecture that efficiently captures both local motifs and long-range regulatory dependencies. NTv3 is first pretrained on ~9T base pairs from the OpenGenome2 corpus spanning >128k species using masked language modeling, and then post-trained with a joint objective on ~16k functional tracks and annotation labels across 24 animal and plant species, enabling state-of-the-art cross-species functional prediction and base-resolution genome annotation.
  </p>
  <p>
    Beyond prediction, NTv3 can be fine-tuned into a controllable generative model via masked-diffusion language modeling, allowing targeted design of regulatory sequences (for example, enhancers with specified activity and promoter selectivity) that have been validated experimentally.
  </p>
</div>

<div class="paper-summary">
  <!-- <h2>📄 A foundational model for joint sequence-function multi-species modeling at scale for long-range genomic prediction</h2> -->
  <img src="assets/paper_summary.png" alt="NTv3 Paper Summary" />
</div>

<div class="why-ntv3">
  <h2>✨ Why NTv3?</h2>
  <ul>
    <li>📏 <strong>1 Mb long context at nucleotide resolution</strong> — ~100× longer than typical genomics models.</li>
    <li>🏗️ <strong>Unified architecture</strong> for: masked language modeling, functional-track prediction, genome annotation, and sequence generation.</li>
    <li>🌍 <strong>Cross-species generalization</strong> across 24 animals + plants with a shared conditioned representation space.</li>
    <li>⚡ <strong>U-Net–style architecture</strong> improves stability and GPU efficiency on very long sequences.</li>
    <li>🎯 <strong>Controllable generative modeling</strong>, enabling targeted enhancer/promoter engineering validated by experimental assays.</li>
  </ul>
</div>

<div class="grid">
  <div class="card">
    <h2>🤖 Models (see <a href="https://huggingface.co/collections/InstaDeepAI/nucleotide-transformer-v3" target="_blank" rel="noopener noreferrer">collection</a>)</h2>
    <ul>
      <li>📦 Pretrained checkpoints:
        <div style="margin-top: 8px; margin-left: 0;">
          <div><a href="https://huggingface.co/InstaDeepAI/NTv3_8M_pre" target="_blank" rel="noopener noreferrer"><code>InstaDeepAI/NTv3_8M_pre</code></a></div>
          <div><a href="https://huggingface.co/InstaDeepAI/NTv3_100M_pre" target="_blank" rel="noopener noreferrer"><code>InstaDeepAI/NTv3_100M_pre</code></a></div>
          <div><a href="https://huggingface.co/InstaDeepAI/NTv3_650M_pre" target="_blank" rel="noopener noreferrer"><code>InstaDeepAI/NTv3_650M_pre</code></a></div>
        </div>
      </li>
      <li>🎯 Post-trained checkpoints:
        <div style="margin-top: 8px; margin-left: 0;">
          <div><a href="https://huggingface.co/InstaDeepAI/NTv3_100M_pos" target="_blank" rel="noopener noreferrer"><code>InstaDeepAI/NTv3_100M_pos</code></a></div>
          <div><a href="https://huggingface.co/InstaDeepAI/NTv3_650M_pos" target="_blank" rel="noopener noreferrer"><code>InstaDeepAI/NTv3_650M_pos</code></a></div>
        </div>
      </li>
    </ul>
    <table>
      <thead>
        <tr>
          <th>Model</th>
          <th>Size</th>
          <th>Pre-training</th>
          <th>Post-training</th>
          <th>Tasks</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <td><strong>NTv3-8M</strong></td>
          <td>8M params</td>
          <td>MLM</td>
          <td>❌</td>
          <td>Embeddings, light inference</td>
        </tr>
        <tr>
          <td><strong>NTv3-100M</strong></td>
          <td>100M params</td>
          <td>MLM</td>
          <td><span class="checkmark">✅</span></td>
          <td>Tracks, annotation</td>
        </tr>
        <tr>
          <td><strong>NTv3-650M</strong></td>
          <td>650M params</td>
          <td>MLM</td>
          <td><span class="checkmark">✅</span></td>
          <td>Tracks, annotation, best accuracy</td>
        </tr>
      </tbody>
    </table>
  </div>

  <div class="card-stack">
    <div class="card">
      <h2>📓 Tutorial notebooks (browse <a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/tree/main/notebooks_tutorials" target="_blank" rel="noopener noreferrer">folder</a>)</h2>
      <ul>
        <li><a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_tutorials/00_quickstart_inference.ipynb" target="_blank" rel="noopener noreferrer">🚀 00 — Quickstart inference</a></li>
        <li><a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_tutorials/01_tracks_prediction.ipynb" target="_blank" rel="noopener noreferrer">📊 01 — Tracks prediction</a></li>
        <li>🎯 02 — Fine-tune on bigwig tracks</li>
        <li>🔍 03 — Model interpretation</li>
        <li>🧪 04 — Training NTv3 generative </li>
      </ul>
    </div>
    <div class="card">
      <h2>📓 Pipeline notebooks (browse <a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/tree/main/notebooks_pipelines" target="_blank" rel="noopener noreferrer">folder</a>)</h2>
      <ul>
        <li><a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_pipelines/01_functional_track_prediction.ipynb" target="_blank" rel="noopener noreferrer">🎯 01 — Generate bigwig predictions for certain tracks</a></li>
        <li>🎯 02 — Fine-tune on bigwig tracks</li>
        <li>🔍 03 — Interpret a given genomic region</li>
        <li>🧪 04 — Sequence generation</li>
      </ul>
    </div>
    <div class="card">
      <h2>🔗 Links</h2>
      <ul>
        <li>📄 Paper: (add link)</li>
        <li><a href="https://github.com/instadeepai/nucleotide-transformer" target="_blank" rel="noopener noreferrer">💻 JAX model code (GitHub)</a></li>
        <li><a href="https://huggingface.co/collections/InstaDeepAI/nucleotide-transformer-v3" target="_blank" rel="noopener noreferrer">🎯 HF Model Collection (all NTv3 models)</a></li>
        <li><a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/tree/main/notebooks_tutorials" target="_blank" rel="noopener noreferrer">📚 Tutorial </a> and <a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/tree/main/notebooks_pipelines" target="_blank" rel="noopener noreferrer">🔧 Pipeline</a> notebooks</li>
        <li><a href="https://huggingface.co/spaces/InstaDeepAI/ntv3_benchmark" target="_blank" rel="noopener noreferrer">🏆 NTv3 benchmark leaderboard</a></li>
      </ul>
    </div>
  </div>

  <div class="card">
    <h2>🤖 Load a pre-trained model</h2>
    <p>Here is an example of how to load and use a pre-trained NTv3 model.</p>
    <div class="code"><pre><code class="language-python">from transformers import AutoTokenizer, AutoModelForMaskedLM

model_name = "InstaDeepAI/NTv3_650M_pre"

# Load model and tokenizer
model = AutoModelForMaskedLM.from_pretrained(model_name, trust_remote_code=True)
tok = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Tokenize input sequences
batch = tok(["ATCGNATCG", "ACGT"], add_special_tokens=False, padding=True, pad_to_multiple_of=128, return_tensors="pt")

# Run model
out = model(
  **batch,
  output_hidden_states=True,
  output_attentions=True
)

# Print output shapes
print(out.logits.shape)       # (B, L, V = 11)
print(len(out.hidden_states)) # convs + transformers + deconvs
print(len(out.attentions))    # equals transformer layers = 12
</code></pre></div>
    <p>Model embeddings can be used for fine-tuning on downstream tasks.</p>

    <p style="margin-top: 40px;">TO DO: add pipeline for fine-tuning on functional tracks or genome annotation.</p>
  </div>
  
  <div class="card">
    <h2>💻 Use a post-trained model</h2>
    <p>Here is a quick example of how to use the post-trained NTv3 650M model to predict tracks for a human genomic window.</p>
    <div class="code"><pre><code class="language-python">from transformers import pipeline
import torch

model_name = "InstaDeepAI/NTv3_650M_pos"

ntv3_tracks = pipeline(
    "ntv3-tracks",
    model=model_name,
    trust_remote_code=True,
    device=0 if torch.cuda.is_available() else -1,
)

# Run track prediction
out = ntv3_tracks(
  {
    "chrom": "chr19",
    "start": 6_700_000,
    "end": 6_831_072,
    "species": "human"
  }
)

# Print output shapes
# 7k human tracks over 37.5 % center region of the input sequence
print("bigwig_tracks_logits:", tuple(out.bigwig_tracks_logits.shape))
# Location of 21 genomic elements over 37.5 % center region of the input sequence
print("bed_tracks_logits:", tuple(out.bed_tracks_logits.shape))
# Language model logits for whole sequence over vocabulary
print("language model logits:", tuple(out.mlm_logits.shape))</code></pre></div>
    <p>Predictions can also be plotted for a subset of functional tracks and genomic elements:</p>
    <div class="code"><pre><code class="language-python">tracks_to_plot = {
    "K562 RNA-seq": "ENCSR056HPM",
    "K562 DNAse": "ENCSR921NMD",
    "K562 H3k4me3": "ENCSR000DWD",
    "K562 CTCF": "ENCSR000AKO",
    "HepG2 RNA-seq": "ENCSR561FEE_P",
    "HepG2 DNAse": "ENCSR000EJV",
    "HepG2 H3k4me3": "ENCSR000AMP",
    "HepG2 CTCF": "ENCSR000BIE",
}
elements_to_plot = ["protein_coding_gene", "exon", "intron", "splice_donor", "splice_acceptor"]

out = ntv3_tracks(
    {"chrom": "chr19", "start": 6_700_000, "end": 6_831_072, "species": "human"},
    plot=True,
    tracks_to_plot=tracks_to_plot,
    elements_to_plot=elements_to_plot,
)</code></pre></div>
    <img src="assets/output_tracks.png" alt="Output tracks visualization" style="max-width: 100%; margin-top: 20px;" />
  </div>
</div>