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bernardo-de-almeida commited on Dec 9, 2025

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Initial NTv3 Space structure and README

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 ---
-title: Ntv3
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
 sdk: static
 pinned: false
-short_description: Nucleotide Transformer v3 models for genomics
 ---
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

 ---
+title: NTv3 — PyTorch notebooks
+emoji: 🧬
+colorFrom: indigo
+colorTo: blue
 sdk: static
 pinned: false
 ---
+# NTv3 — PyTorch notebooks
+This Space is the companion hub for NTv3 checkpoints on the Hugging Face Hub. It provides PyTorch notebooks and minimal examples for inference, sequence-to-function prediction (functional tracks), genome annotation, fine-tuning, model interpretation and sequence generation.
+## Notebooks
+Notebooks live in `./notebooks/`:
+- `00_quickstart_inference.ipynb` — load a checkpoint + run inference
+- `01_tracks_prediction.ipynb` — sequence → functional tracks (+ plotting)
+- `02_genome_annotation_segmentation.ipynb` — sequence → annotation
+- `03_finetune_head.ipynb` — fine-tune on a bigwig track
+- `04_model_interpretation.ipynb` — interpretation of post-trained model
+- `05_sequence_generation.ipynb` — fine-tune NTv3 to generate enhancer sequences
+## Install
+```bash
+pip install torch transformers accelerate safetensors huggingface_hub numpy
+```
+## Load a model
+```python
+```
+## Pipelines (To DO)
+```python
+from transformers import pipeline
+import torch
+pipe = pipeline(
+    task="ntv3-tracks",
+    model="InstaDeepAI/ntv3_106M_7downsample_post_trained_1mb",
+    trust_remote_code=True,
+    device="cuda",
+    torch_dtype=torch.bfloat16,
+)
+out = pipe("ACGT...")
+```
+## Checkpoints
+**Pre-trained:** `InstaDeepAI/ntv3_8M_7downsample_pretrained_le_1mb`, `InstaDeepAI/ntv3_106M_7downsample_pretrained_le_1mb`, `InstaDeepAI/ntv3_650M_7downsample_pretrained_le_1mb`
+**Post-trained:** `InstaDeepAI/ntv3_650M_7downsample_post_trained_1mb`, `InstaDeepAI/ntv3_106M_7downsample_post_trained_1mb`
+## Links
+- **Paper:** (add link)
+- **JAX research code (GitHub):** [https://github.com/instadeepai/nucleotide-transformer](https://github.com/instadeepai/nucleotide-transformer)
+## Citation
+```bibtex
+@article{ntv3,
+  title   = {A foundational model for joint sequence-function multi-species modeling at scale for long-range genomic prediction},
+  author  = {…},
+  journal = {…},
+  year    = {…}
+}
+```
+## License
+**Code & notebooks in this Space:** (choose and add, e.g., Apache-2.0)
+**Model weights:** see the license specified in each model repository

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 <!doctype html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
 </html>

 <!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width,initial-scale=1" />
+  <title>NTv3 — PyTorch Notebooks</title>
+  <meta name="description" content="NTv3 companion hub: PyTorch notebooks for inference, fine-tuning, interpretation, and sequence generation on NTv3 models hosted on Hugging Face." />
+  <style>
+    :root {
+      --bg: #0b1020;
+      --card: rgba(255, 255, 255, 0.06);
+      --text: rgba(255, 255, 255, 0.92);
+      --muted: rgba(255, 255, 255, 0.65);
+      --link: #7dd3fc;
+      --border: rgba(255, 255, 255, 0.12);
+      --shadow: 0 10px 30px rgba(0,0,0,0.35);
+      --radius: 18px;
+      --mono: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+      --sans: ui-sans-serif, system-ui, -apple-system, Segoe UI, Roboto, Helvetica, Arial, "Apple Color Emoji","Segoe UI Emoji";
+    }
+    body {
+      margin: 0;
+      font-family: var(--sans);
+      color: var(--text);
+      background:
+        radial-gradient(1200px 800px at 10% 10%, rgba(125, 211, 252, 0.12), transparent 60%),
+        radial-gradient(1200px 800px at 90% 30%, rgba(167, 139, 250, 0.12), transparent 55%),
+        var(--bg);
+      min-height: 100vh;
+    }
+    .wrap { max-width: 980px; margin: 0 auto; padding: 44px 18px 56px; }
+    .hero {
+      display: grid; gap: 14px;
+      padding: 26px 24px;
+      border: 1px solid var(--border);
+      background: var(--card);
+      box-shadow: var(--shadow);
+      border-radius: var(--radius);
+    }
+    h1 { font-size: 34px; margin: 0; letter-spacing: -0.02em; }
+    p { margin: 0; color: var(--muted); line-height: 1.5; }
+    .grid {
+      margin-top: 18px;
+      display: grid;
+      grid-template-columns: repeat(12, 1fr);
+      gap: 14px;
+    }
+    .card {
+      grid-column: span 6;
+      padding: 18px 18px;
+      border: 1px solid var(--border);
+      background: var(--card);
+      border-radius: var(--radius);
+      box-shadow: 0 6px 18px rgba(0,0,0,0.22);
+    }
+    .card h2 { margin: 0 0 10px 0; font-size: 16px; letter-spacing: 0.01em; }
+    .card ul { margin: 0; padding-left: 18px; color: var(--muted); }
+    .card li { margin: 8px 0; }
+    a { color: var(--link); text-decoration: none; }
+    a:hover { text-decoration: underline; }
+    .pillrow { display: flex; gap: 8px; flex-wrap: wrap; margin-top: 8px; }
+    .pill {
+      font-size: 12px;
+      padding: 6px 10px;
+      border-radius: 999px;
+      border: 1px solid var(--border);
+      background: rgba(255,255,255,0.04);
+      color: var(--muted);
+    }
+    .code {
+      margin-top: 12px;
+      padding: 16px 18px;
+      border-radius: 14px;
+      border: 1px solid var(--border);
+      background: rgba(0,0,0,0.3);
+      font-family: var(--mono);
+      font-size: 13px;
+      line-height: 1.6;
+      overflow-x: auto;
+      color: rgba(255,255,255,0.9);
+      white-space: pre;
+    }
+    .code code {
+      font-family: inherit;
+      font-size: inherit;
+      color: inherit;
+    }
+    .footer { margin-top: 22px; color: var(--muted); font-size: 13px; }
+    @media (max-width: 860px) {
+      .card { grid-column: span 12; }
+      h1 { font-size: 28px; }
+    }
+  </style>
+</head>
+<body>
+  <div class="wrap">
+    <div class="hero">
+      <h1>NTv3 — PyTorch notebooks on Hugging Face</h1>
+      <p>
+        This Space is the companion hub for <strong>NTv3</strong> models: runnable notebooks for inference, fine-tuning, interpretation, and sequence generation.
+      </p>
+      <div class="pillrow">
+        <span class="pill">Long-context genomics</span>
+        <span class="pill">Torch notebooks</span>
+        <span class="pill">Inference • Fine-tune • Interpret • Generate</span>
+      </div>
+    </div>
+    <div class="grid">
+      <div class="card">
+        <h2>Notebooks</h2>
+        <ul>
+          <li><a href="./notebooks/">Browse notebooks folder</a> (add .ipynb files here)</li>
+          <li>00 — Quickstart inference</li>
+          <li>01 — Tracks prediction</li>
+          <li>02 — Genome annotation / segmentation</li>
+          <li>03 — Fine-tune a head</li>
+          <li>04 — Model interpretation</li>
+          <li>05 — Sequence generation</li>
+        </ul>
+      </div>
+      <div class="card">
+        <h2>Models</h2>
+        <ul>
+          <li>Pretrained checkpoints: <a href="https://huggingface.co/InstaDeepAI/ntv3_8M_7downsample_pretrained_le_1mb"><code>InstaDeepAI/ntv3_8M_7downsample_pretrained_le_1mb</code></a>, <a href="https://huggingface.co/InstaDeepAI/ntv3_106M_7downsample_pretrained_le_1mb"><code>InstaDeepAI/ntv3_106M_7downsample_pretrained_le_1mb</code></a>, <a href="https://huggingface.co/InstaDeepAI/ntv3_650M_7downsample_pretrained_le_1mb"><code>InstaDeepAI/ntv3_650M_7downsample_pretrained_le_1mb</code></a></li>
+          <li>Post-trained checkpoints: <a href="https://huggingface.co/InstaDeepAI/ntv3_650M_7downsample_post_trained_1mb"><code>InstaDeepAI/ntv3_650M_7downsample_post_trained_1mb</code></a>, <a href="https://huggingface.co/InstaDeepAI/ntv3_106M_7downsample_post_trained_1mb"><code>InstaDeepAI/ntv3_106M_7downsample_post_trained_1mb</code></a></li>
+        </ul>
+      </div>
+      <div class="card">
+        <h2>Loading</h2>
+        <p>Depending on how the checkpoint is published, you can load via Transformers-native code or custom code.</p>
+        <div class="code"><code>from transformers import AutoModel, AutoTokenizer
+model = AutoModel.from_pretrained(
+    "InstaDeepAI/ntv3_106M_7downsample_post_trained_1mb",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "InstaDeepAI/ntv3_106M_7downsample_post_trained_1mb",
+    trust_remote_code=True
+)</code></div>
+      </div>
+      <div class="card">
+        <h2>Links</h2>
+        <ul>
+          <li>Paper: (add link)</li>
+          <li><a href="https://github.com/instadeepai/nucleotide-transformer">JAX training code</a></li>
+        </ul>
+      </div>
+    </div>
+    <p class="footer">
+      © instadeep-ai — NTv3 companion Space.
+    </p>
+  </div>
+</body>
 </html>

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