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{
"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)."
]
},
{
"cell_type": "markdown",
"id": "5827af7e",
"metadata": {},
"source": [
"## 0) 📦 Imports + setup"
]
},
{
"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()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "38cc32a9",
"metadata": {},
"outputs": [],
"source": [
"!pip -q install \"transformers>=4.40\" \"huggingface_hub>=0.23\" safetensors torch numpy"
]
},
{
"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)"
]
},
{
"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])"
]
},
{
"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"
]
},
{
"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))"
]
},
{
"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"
]
},
{
"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"
]
}
],
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"kernelspec": {
"display_name": "hf-finetune",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
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