diff --git "a/notebooks_tutorials/00_quickstart_inference.ipynb" "b/notebooks_tutorials/00_quickstart_inference.ipynb"
--- "a/notebooks_tutorials/00_quickstart_inference.ipynb"
+++ "b/notebooks_tutorials/00_quickstart_inference.ipynb"
@@ -1,247 +1,5987 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "024bb8a8",
-      "metadata": {},
-      "source": [
-        "# 🚀 NTv3 Quickstart — Pre-trained and Post-trained models\n",
-        "\n",
-        "This notebook demonstrates how to run **quick inference** with both the pre- and post-trained NTv3 checkpoints:\n",
-        "\n",
-        "- **Pre-trained (MLM-focused):** `InstaDeepAI/NTv3_8M_pre`, `InstaDeepAI/NTv3_100M_pre`, `InstaDeepAI/NTv3_650M_pre`\n",
-        "- **Post-trained (functional tracks and genome annotation):** `InstaDeepAI/NTv3_100M_post`, `InstaDeepAI/NTv3_650M_post`\n",
-        "\n",
-        "We show how to:\n",
-        "\n",
-        "1. Load tokenizers + models\n",
-        "2. Run a forward pass on a DNA sequence window\n",
-        "3. Inspect key outputs\n",
-        "\n",
-        "> 📝 **Note for Google Colab users:** This notebook is compatible with Colab! For faster inference, make sure to enable GPU: Runtime → Change runtime type → GPU (T4 or better recommended)."
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6903eb0a",
+   "metadata": {},
+   "source": [
+    "# 🚀 NTv3 Quickstart — Pretrained and Tracks models\n",
+    "\n",
+    "This notebook runs **quick inference** with the two NTv3 checkpoints:\n",
+    "\n",
+    "- **Pretrained (masked-token prediction):** [`InstaDeepAI/NTv3_pretrained`](https://huggingface.co/InstaDeepAI/NTv3_pretrained)\n",
+    "- **Tracks (functional-track prediction, species-conditioned):** [`InstaDeepAI/NTv3_tracks`](https://huggingface.co/InstaDeepAI/NTv3_tracks)\n",
+    "\n",
+    "We show how to:\n",
+    "\n",
+    "1. Load tokenizers + models\n",
+    "2. Run a forward pass on a batch of DNA sequences\n",
+    "3. Inspect key outputs\n",
+    "\n",
+    "Both models have ≈700M parameters and read DNA at single-base resolution. The input length must be a **multiple of 128**, the total downsampling factor of the U-Net trunk.\n",
+    "\n",
+    "> 📝 **Note for Google Colab users:** This notebook is compatible with Colab! For faster inference, enable a GPU: Runtime → Change runtime type → GPU (T4 or better)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b23025d3",
+   "metadata": {},
+   "source": [
+    "## 0) 📦 Imports + setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e31a2089",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-28T16:08:24.275878Z",
+     "iopub.status.busy": "2026-09-28T16:08:24.275756Z",
+     "iopub.status.idle": "2026-09-28T16:08:33.636600Z",
+     "shell.execute_reply": "2026-09-28T16:08:33.634467Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Colab ships torch; reinstalling it there breaks the runtime. Elsewhere, also install torch\n",
+    "!pip -q install --upgrade 'transformers>=5.17,<6'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c03e7528",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-28T16:08:33.642391Z",
+     "iopub.status.busy": "2026-09-28T16:08:33.641886Z",
+     "iopub.status.idle": "2026-09-28T16:08:33.951484Z",
+     "shell.execute_reply": "2026-09-28T16:08:33.949912Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# The NTv3 models are gated: request access once on any model page, then log in.\n",
+    "from huggingface_hub import get_token, notebook_login\n",
+    "\n",
+    "if get_token() is None:\n",
+    "    notebook_login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9b05ca73",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-28T16:08:33.956040Z",
+     "iopub.status.busy": "2026-09-28T16:08:33.955881Z",
+     "iopub.status.idle": "2026-09-28T16:08:43.089536Z",
+     "shell.execute_reply": "2026-09-28T16:08:43.088530Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoModel, AutoTokenizer\n",
+    "\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(\"device:\", device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aa9eafc4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-28T16:08:43.092273Z",
+     "iopub.status.busy": "2026-09-28T16:08:43.091947Z",
+     "iopub.status.idle": "2026-09-28T16:08:43.095454Z",
+     "shell.execute_reply": "2026-09-28T16:08:43.094775Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sequence lengths: [1024, 1024]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dummy DNA sequences, of the same length\n",
+    "seqs = [\n",
+    "    \"ACGT\" * 256,\n",
+    "    \"TTGCA\" * 204 + \"ACGT\",\n",
+    "]\n",
+    "\n",
+    "print(\"Sequence lengths:\", [len(s) for s in seqs])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3efe54f7",
+   "metadata": {},
+   "source": [
+    "## 1) 🎯 Pretrained checkpoint (masked-token prediction)\n",
+    "\n",
+    "Load model + tokenizer, then run a forward pass.\n",
+    "\n",
+    "> ⚠️ **Batch only sequences of the same length.** Padding is not inert in this model: pad tokens go through the convolutional tower and the normalisation layers, and they change the outputs at the real positions. Run sequences of different lengths separately, each cropped or extended to a multiple of 128.\n",
+    "\n",
+    "Expected outputs:\n",
+    "- `logits`: masked-token prediction over the 11-token vocabulary\n",
+    "- `embedding`: one 1024-d vector per base, the usual starting point for downstream tasks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b0b530c8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-28T16:08:43.097227Z",
+     "iopub.status.busy": "2026-09-28T16:08:43.097098Z",
+     "iopub.status.idle": "2026-09-28T16:08:55.115637Z",
+     "shell.execute_reply": "2026-09-28T16:08:55.114318Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b656840a6ec41b8bc99c7746a25505e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/621 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "5827af7e",
-      "metadata": {},
-      "source": [
-        "## 0) 📦 Imports + setup"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1c2ed8e0ea943fe9108cc5eff6c0326",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "configuration_ntv3.py:   0%|          | 0.00/4.76k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "0b354087",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Login to HuggingFace (required for gated models)\n",
-        "from huggingface_hub import login\n",
-        "login()"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2df7ce3fa95f4eec8ca831d1d8cb05fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/321 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "38cc32a9",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!pip -q install \"transformers>=4.40\" \"huggingface_hub>=0.23\" safetensors torch numpy"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59b7dcec972e43f9b0e60d4fd9e13679",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenization_ntv3.py:   0%|          | 0.00/3.42k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "d56c105b",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "device: cpu\n",
-            "torch_dtype: torch.float32\n"
-          ]
-        }
-      ],
-      "source": [
-        "import os\n",
-        "import torch\n",
-        "import numpy as np\n",
-        "\n",
-        "from transformers import AutoConfig, AutoModel, AutoTokenizer, AutoModelForMaskedLM\n",
-        "\n",
-        "# Optional: if the model is gated/private, set HF_TOKEN to a PERSONAL token (hf_...)\n",
-        "HF_TOKEN = os.getenv(\"HF_TOKEN\", None)\n",
-        "\n",
-        "# -----------------------------\n",
-        "# Device\n",
-        "# -----------------------------\n",
-        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-        "print(\"device:\", device)\n",
-        "\n",
-        "# Choose dtype (bf16 if supported; else fp16 on GPU; else fp32)\n",
-        "if device == \"cuda\":\n",
-        "    major, minor = torch.cuda.get_device_capability(0)\n",
-        "    torch_dtype = torch.bfloat16 if major >= 8 else torch.float16\n",
-        "else:\n",
-        "    torch_dtype = torch.float32\n",
-        "\n",
-        "print(\"torch_dtype:\", torch_dtype)"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6dbd24aee0b94cf4ae88705e98f3b872",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/139 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "ef0e6d69",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            " Sequence lengths: [128, 512]\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Dummy DNA sequences\n",
-        "seqs = [\n",
-        "    \"ACGT\" * 32,\n",
-        "    \"ACGT\" * 128\n",
-        "]\n",
-        "\n",
-        "print(\" Sequence lengths:\", [len(s) for s in seqs])"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "925951156e06462a813b39b7bb16aeca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "modeling_ntv3.py:   0%|          | 0.00/42.5k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "82146876",
-      "metadata": {},
-      "source": [
-        "## 1) 🎯 Pre-trained checkpoint (MLM-focused)\n",
-        "\n",
-        "This shows the simplest usage: load model + tokenizer, then run a forward pass.\n",
-        "\n",
-        "Expected output:\n",
-        "- `logits`: masked language modeling logits"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f38a31bfff544bdab2cd1e7a7fc97171",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors: reconstructing file:   0%|          |  0.00B / 2.81GB            "
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "336bb40c",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "MLM logits shape: (2, 512, 11)\n"
-          ]
-        }
-      ],
-      "source": [
-        "pretrained_model_name = \"InstaDeepAI/NTv3_8M_pre\"\n",
-        "\n",
-        "# Load tokenizer/model\n",
-        "tok_pre = AutoTokenizer.from_pretrained(pretrained_model_name, trust_remote_code=True)\n",
-        "model_pre = AutoModelForMaskedLM.from_pretrained(pretrained_model_name, trust_remote_code=True)\n",
-        "\n",
-        "# Example inference\n",
-        "# Tokenization will pad all sequences to multiple of 128\n",
-        "batch = tok_pre(seqs, add_special_tokens=False, padding=True, pad_to_multiple_of=128, return_tensors=\"pt\")\n",
-        "out = model_pre(**batch)\n",
-        "\n",
-        "# Access MLM logits\n",
-        "mlm_logits = out[\"logits\"]\n",
-        "print(\"MLM logits shape:\", tuple(mlm_logits.shape))"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36b4197e9a1845b1a2168bc37ab2b9d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors: downloading bytes:           |  0.00B            "
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "60a01798",
-      "metadata": {},
-      "source": [
-        "## 2) 🧠 Post-trained checkpoint (task heads: BigWig + BED)\n",
-        "\n",
-        "Post-trained checkpoints add task-specific heads for functional track prediction and genome annotation.\n",
-        "\n",
-        "Expected outputs:\n",
-        "- `bigwig_tracks_logits`: functional track predictions\n",
-        "- `bed_tracks_logits`: genome annotation predictions\n",
-        "- `logits`: masked language modeling logits"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c123bcecd43f4562b837e47d865222b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/504 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "6cc5f2df",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Supported species: dict_keys(['<bos>', '<cls>', '<eos>', '<mask>', '<pad>', '<unk>', 'amphiprion_ocellaris', 'arabidopsis_thaliana', 'bison_bison_bison', 'caenorhabditis_elegans', 'canis_lupus_familiaris', 'chinchilla_lanigera', 'ciona_intestinalis', 'danio_rerio', 'drosophila_melanogaster', 'felis_catus', 'gallus_gallus', 'glycine_max', 'gorilla_gorilla', 'gossypium_hirsutum', 'human', 'macaca_nemestrina', 'mouse', 'oryza_sativa', 'rattus_norvegicus', 'salmo_trutta', 'serinus_canaria', 'tetraodon_nigroviridis', 'triticum_aestivum', 'zea_mays'])\n",
-            "bigwig_tracks_logits: (2, 192, 7362)\n",
-            "bed_tracks_logits: (2, 192, 21, 2)\n",
-            "language model logits: (2, 512, 11)\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Load model\n",
-        "post_trained_model_name = \"InstaDeepAI/NTv3_100M_post\"\n",
-        "\n",
-        "tok_post = AutoTokenizer.from_pretrained(post_trained_model_name, trust_remote_code=True)\n",
-        "model_post = AutoModel.from_pretrained(post_trained_model_name, trust_remote_code=True)\n",
-        "\n",
-        "# Prepare inputs - tokenization will pad all sequences to multiple of 128\n",
-        "batch = tok_post(seqs, add_special_tokens=False, padding=True, pad_to_multiple_of=128, return_tensors=\"pt\")\n",
-        "\n",
-        "# To show all supported species: \n",
-        "print(\"Supported species:\", model_post.config.species_to_token_id.keys())\n",
-        "# Species tokens (one per sequence)\n",
-        "species = ['human', 'mouse']\n",
-        "species_ids = model_post.encode_species(species)\n",
-        "\n",
-        "# Forward pass\n",
-        "out = model_post(\n",
-        "    input_ids=batch[\"input_ids\"],\n",
-        "    species_ids=species_ids,\n",
-        ")\n",
-        "\n",
-        "# 7k human tracks over 37.5 % center region of the input sequence\n",
-        "print(\"bigwig_tracks_logits:\", tuple(out[\"bigwig_tracks_logits\"].shape))\n",
-        "# Location of 21 genomic elements over 37.5 % center region of the input sequence\n",
-        "print(\"bed_tracks_logits:\", tuple(out[\"bed_tracks_logits\"].shape))\n",
-        "# Language model logits for whole sequence over vocabulary\n",
-        "print(\"language model logits:\", tuple(out[\"logits\"].shape))\n"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MLM logits: (2, 1024, 11)\n",
+      "embedding: (2, 1024, 1024)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pretrained_model_name = \"InstaDeepAI/NTv3_pretrained\"\n",
+    "\n",
+    "tok_pre = AutoTokenizer.from_pretrained(pretrained_model_name, trust_remote_code=True)\n",
+    "model_pre = AutoModel.from_pretrained(pretrained_model_name, trust_remote_code=True)\n",
+    "model_pre = model_pre.to(device).eval()\n",
+    "\n",
+    "batch = tok_pre(seqs, add_special_tokens=False, return_tensors=\"pt\")\n",
+    "with torch.no_grad():\n",
+    "    out = model_pre(batch[\"input_ids\"].to(device))\n",
+    "\n",
+    "print(\"MLM logits:\", tuple(out[\"logits\"].shape))\n",
+    "print(\"embedding:\", tuple(out[\"embedding\"].shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "967e1e49",
+   "metadata": {},
+   "source": [
+    "> ⚠️ **The pretrained alphabet is uppercase only** (`A C G T N` + special tokens). Most reference genomes are soft-masked, with repeats in lowercase: call `.upper()` on your sequence first, otherwise lowercase bases become `<unk>` and the model still returns numbers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b17d82ae",
+   "metadata": {},
+   "source": [
+    "## 2) 🧠 Tracks checkpoint (functional tracks, species-conditioned)\n",
+    "\n",
+    "`NTv3_tracks` adds a species-conditioned head that predicts **7,362 functional tracks** (the width of the largest species panel, human), from the same trunk.\n",
+    "\n",
+    "Two inputs:\n",
+    "- `input_ids`: tokenized DNA. This model reads a **16-symbol soft-masked alphabet**, so pass lowercase repeats as they are.\n",
+    "- `conditions_tokens`: a list with one `(batch,)` tensor of species ids. The id table ships in `config.track_metadata`.\n",
+    "\n",
+    "Expected outputs:\n",
+    "- `bigwig_tracks_logits`: track predictions over the **central 37.5%** of the input\n",
+    "- `species_logits`: auxiliary species classification\n",
+    "- `logits`: masked-token prediction\n",
+    "\n",
+    "The dummy sequences above are 1 kb and only show the output shapes. `NTv3_tracks` was trained on inputs of 16 kb to 1 Mb, so use that range for real predictions, as notebook 01 does."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8cf3351c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-28T16:08:55.118180Z",
+     "iopub.status.busy": "2026-09-28T16:08:55.118002Z",
+     "iopub.status.idle": "2026-09-28T16:09:04.266051Z",
+     "shell.execute_reply": "2026-09-28T16:09:04.265031Z"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "hf-finetune",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6220dea58a3c44ea84dd9507b654bd9f",
+       "version_major": 2,
+       "version_minor": 0
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+    {
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+    {
+     "data": {
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+      "text/plain": [
+       "Loading weights:   0%|          | 0/515 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Supported species: ['arabidopsis', 'caenorhabditis_elegans', 'chicken', 'cotton', 'drosophila', 'horse', 'human', 'maize', 'mouse', 'pig', 'rice', 'sheep', 'soybean', 'wheat', 'zebrafish']\n"
+     ]
+    }
+   ],
+   "source": [
+    "tracks_model_name = \"InstaDeepAI/NTv3_tracks\"\n",
+    "\n",
+    "tok_tracks = AutoTokenizer.from_pretrained(tracks_model_name, trust_remote_code=True)\n",
+    "model_tracks = AutoModel.from_pretrained(tracks_model_name, trust_remote_code=True)\n",
+    "model_tracks = model_tracks.to(device).eval()\n",
+    "\n",
+    "metadata = model_tracks.config.track_metadata\n",
+    "species_vocab = metadata[\"species_vocabulary\"]\n",
+    "print(\"Supported species:\", [s for s in species_vocab if not s.startswith(\"<\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c2445a79",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-28T16:09:04.269385Z",
+     "iopub.status.busy": "2026-09-28T16:09:04.269208Z",
+     "iopub.status.idle": "2026-09-28T16:09:04.318164Z",
+     "shell.execute_reply": "2026-09-28T16:09:04.317347Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bigwig_tracks_logits: (2, 384, 7362)\n",
+      "species_logits: (2, 21)\n",
+      "language model logits: (2, 1024, 16)\n"
+     ]
     }
+   ],
+   "source": [
+    "batch = tok_tracks(seqs, add_special_tokens=False, return_tensors=\"pt\")\n",
+    "\n",
+    "# One species per sequence\n",
+    "species = [\"human\", \"mouse\"]\n",
+    "species_ids = torch.tensor([species_vocab[s] for s in species], device=device)\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    out = model_tracks(batch[\"input_ids\"].to(device), conditions_tokens=[species_ids])\n",
+    "\n",
+    "# 7,362 tracks over the central 37.5% of the input sequence\n",
+    "print(\"bigwig_tracks_logits:\", tuple(out[\"bigwig_tracks_logits\"].shape))\n",
+    "print(\"species_logits:\", tuple(out[\"species_logits\"].shape))\n",
+    "print(\"language model logits:\", tuple(out[\"logits\"].shape))"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
-}
+  {
+   "cell_type": "markdown",
+   "id": "ffbda589",
+   "metadata": {},
+   "source": [
+    "### Which channel is which track?\n",
+    "\n",
+    "Channel `i` is track `i` of that species' list in `metadata[\"bigwigs_per_file_assembly\"]`, keyed by assembly. Species have different numbers of tracks and the head is padded to the widest panel, so only the first `len(track_ids)` channels are meaningful for a given species."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1a051ca5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-28T16:09:04.320874Z",
+     "iopub.status.busy": "2026-09-28T16:09:04.320715Z",
+     "iopub.status.idle": "2026-09-28T16:09:04.323963Z",
+     "shell.execute_reply": "2026-09-28T16:09:04.323275Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EquCab3.0                       96 tracks, e.g. ['ERX3645380', 'ERX3645388', 'ERX3645348']\n",
+      "GRCg6a                          55 tracks, e.g. ['ERX3783250', 'ERX3783278', 'ERX3783264']\n",
+      "GRCz11                          23 tracks, e.g. ['ERR1442633', 'ERR1442641', 'ERR1442622']\n",
+      "Glycine_max_v2.1               590 tracks, e.g. ['SRX20249461', 'SRX20249462', 'SRX21859141']\n",
+      "Gossypium_hirsutum_v2.1        319 tracks, e.g. ['SRX22583182', 'SRX22583184', 'SRX22583185']\n",
+      "IRGSP-1.0                    1,392 tracks, e.g. ['SRX11246449', 'SRX11246451', 'SRX11246453']\n",
+      "IWGSC                          776 tracks, e.g. ['SRX14424039', 'SRX14424040', 'SRX3455767']\n",
+      "Oar_v3.1                        63 tracks, e.g. ['ERX4554102', 'ERX4554097', 'ERX4554098']\n",
+      "Sscrofa11                       94 tracks, e.g. ['ERX4019749', 'ERX4019751', 'ERX4019750']\n",
+      "TAIR10                       1,899 tracks, e.g. ['ERX11438964', 'ERX13251227', 'ERX13251239']\n",
+      "WBcel235                        42 tracks, e.g. ['SRR473085', 'SRR493079', 'SRR2054405']\n",
+      "Zm-B73-REFERENCE-NAM-5.0       921 tracks, e.g. ['SRX12330305', 'SRX12330306', 'SRX17592193']\n",
+      "dm6                            180 tracks, e.g. ['window_16-18_cluster_6', 'window_14-16_cluster_1', 'window_10-12_cluster_24']\n",
+      "hg38                         7,362 tracks, e.g. ['kai1', 'kai2', 'kai3']\n",
+      "mm10                         2,450 tracks, e.g. ['yangli1', 'yangli2', 'yangli3']\n"
+     ]
+    }
+   ],
+   "source": [
+    "tracks_per_assembly = metadata[\"bigwigs_per_file_assembly\"]\n",
+    "for assembly, track_ids in tracks_per_assembly.items():\n",
+    "    print(f\"{assembly:28s} {len(track_ids):>5,} tracks, e.g. {track_ids[:3]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ea0f1c",
+   "metadata": {},
+   "source": [
+    "➡️ Next: [01 · Tracks prediction](01_tracks_prediction.ipynb) runs `NTv3_tracks` on a real genomic window and plots the predicted tracks."
+   ]
+  }
+ ],
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