{
 "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": {
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     "shell.execute_reply": "2026-09-28T16:08:33.634467Z"
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   },
   "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": {
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     "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",
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     "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",
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     "shell.execute_reply": "2026-09-28T16:08:55.114318Z"
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     "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."
   ]
  },
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     "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",
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   },
   "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))"
   ]
  },
  {
   "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",
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   },
   "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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