new notebook structure

index.html

@@ -305,13 +305,22 @@
 
       <div class="card-stack">
         <div class="card">
-          <h2>📓 Notebooks (browse <a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/tree/main/notebooks" target="_blank" rel="noopener">folder</a>)</h2>
+          <h2>📓 Tutorial notebooks (browse <a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/tree/main/notebooks_tutorials" target="_blank" rel="noopener">folder</a>)</h2>
           <ul>
             <li><a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks/00_quickstart_inference.ipynb" target="_blank" rel="noopener">🚀 00 — Quickstart inference</a></li>
             <li><a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks/01_tracks_prediction.ipynb" target="_blank" rel="noopener">📊 01 — Tracks prediction</a></li>
-            <li><a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks/02_genome_annotation.ipynb" target="_blank" rel="noopener">🏷️ 02 — Genome annotation / segmentation</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>📓 Pipelines notebooks (browse <a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/tree/main/notebooks_pipelines" target="_blank" rel="noopener">folder</a>)</h2>
+          <ul>
+            <li> 🎯 01 — Generate bigwig predictions for certain tracks</li>
+            <li><a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_pipelines/02_genome_annotation.ipynb" target="_blank" rel="noopener">🏷️ 02 — Genome annotation / segmentation</a></li>
             <li>🎯 03 — Fine-tune on bigwig tracks</li>
-            <li>🔍 04 — Model interpretation</li>
+            <li>🔍 04 — Interpret a given genomic region</li>
             <li>🧪 05 — Sequence generation</li>
           </ul>
         </div>
@@ -361,16 +370,20 @@ print(len(out.attentions))    # equals transformer layers = 12
       <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 AutoConfig
+        <div class="code"><pre><code class="language-python">from transformers import pipeline
+import torch
 
-model_name = "InstaDeepAI/NTv3_650M"
+model_name = "InstaDeepAI/NTv3_650M_pos"
 
-# Load track prediction pipeline
-cfg = AutoConfig.from_pretrained(model_name, trust_remote_code=True, force_download=True)
-pipe = cfg.load_tracks_pipeline(model_name, device="auto")  # or "cpu"/"cuda"/"mps"
+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 = pipe(
+out = ntv3_tracks(
   {
     "chrom": "chr19",
     "start": 6_700_000,
@@ -399,7 +412,7 @@ print("language model logits:", tuple(out.mlm_logits.shape))</code></pre></div>
 }
 elements_to_plot = ["protein_coding_gene", "exon", "intron", "splice_donor", "splice_acceptor"]
 
-out = pipe(
+out = ntv3_tracks(
     {"chrom": "chr19", "start": 6_700_000, "end": 6_831_072, "species": "human"},
     plot=True,
     tracks_to_plot=tracks_to_plot,

notebooks/02_genome_annotation.ipynb → notebooks_pipelines/01_genome_annotation.ipynb

@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "4857d15c",
    "metadata": {},
    "outputs": [
@@ -274,7 +274,6 @@
     "    model=model_name,\n",
     "    trust_remote_code=True,\n",
     "    device=0 if torch.cuda.is_available() else -1,\n",
-    "    force_download=True,\n",
     ")\n",
     "\n",
     "# Run pipeline: DNA -> NTv3 -> HMM -> GFF3\n",