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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4",
"name": "Zeeb_Video_LLM_Training.ipynb"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 🎬 Zeeb — Video-LLM Training on T4 GPU\n",
"\n",
"**OLMo 2 1B + LoRA + VQ-VAE → Text-to-Video Generation**\n",
"\n",
"This notebook trains the full pipeline on a **Google Colab T4 GPU** and pushes checkpoints to HuggingFace incrementally.\n",
"\n",
"## Pipeline Overview\n",
"1. **Phase 1**: Train VQ-VAE on real images (COCO, streaming)\n",
"2. **Phase 2**: Tokenize image-text pairs through trained VQ-VAE\n",
"3. **Phase 3**: Fine-tune OLMo 2 1B + LoRA on tokenized data → push to EeshaAI/zeeb\n",
"\n",
"## Key Features\n",
"- ✅ **Incremental checkpoint pushing** to HuggingFace (survives Colab disconnects)\n",
"- ✅ **Resume from checkpoint** if training is interrupted\n",
"- ✅ **HuggingFace Trainer** with `push_to_hub=True` and `save_strategy=\"steps\"`\n",
"- ✅ **Real data** from COCO/imagenette (10K+ images)\n",
"- ✅ **GPU-accelerated** training (T4 = ~50x faster than CPU)\n",
"\n",
"**Make sure you select GPU runtime**: Runtime → Change runtime type → T4 GPU"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ⚙️ Cell 1: Setup & Authentication"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 1. Install Dependencies\n",
"!pip install -q torch torchvision transformers peft accelerate datasets huggingface_hub safetensors imageio Pillow\n",
"\n",
"import torch\n",
"print(f\"PyTorch: {torch.__version__}\")\n",
"print(f\"CUDA available: {torch.cuda.is_available()}\")\n",
"if torch.cuda.is_available():\n",
" print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n",
" print(f\"VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB\")\n",
"else:\n",
" raise RuntimeError(\"No GPU detected! Go to Runtime → Change runtime type → T4 GPU\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 2. HuggingFace Authentication\n",
"from huggingface_hub import HfApi, login\n",
"import os\n",
"\n",
"# 🔑 Paste your HuggingFace token here (must have write access to EeshaAI/zeeb)\n",
"HF_TOKEN = \"YOUR_HF_TOKEN_HERE\" # @param {type:\"string\"}\n",
"\n",
"login(token=HF_TOKEN)\n",
"\n",
"api = HfApi()\n",
"user_info = api.whoami()\n",
"print(f\"Logged in as: {user_info['name']}\")\n",
"\n",
"REPO_ID = \"EeshaAI/zeeb\"\n",
"api.create_repo(repo_id=REPO_ID, repo_type=\"model\", exist_ok=True)\n",
"print(f\"Model repo: https://huggingface.co/{REPO_ID}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🧠 Cell 2: VQ-VAE Model Definition"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 3. VQ-VAE Architecture\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"CODEBOOK_SIZE = 1024\n",
"CODEBOOK_DIM = 256\n",
"LATENT_DIM = 256\n",
"\n",
"class Encoder(nn.Module):\n",
" def __init__(self, in_channels=3, latent_dim=LATENT_DIM):\n",
" super().__init__()\n",
" self.net = nn.Sequential(\n",
" nn.Conv2d(in_channels, 64, 4, stride=2, padding=1), # -> 64x64\n",
" nn.ReLU(),\n",
" nn.Conv2d(64, 128, 4, stride=2, padding=1), # -> 32x32\n",
" nn.ReLU(),\n",
" nn.Conv2d(128, 256, 4, stride=2, padding=1), # -> 16x16\n",
" nn.ReLU(),\n",
" nn.Conv2d(256, latent_dim, 4, stride=2, padding=1), # -> 8x8\n",
" )\n",
"\n",
" def forward(self, x):\n",
" return self.net(x)\n",
"\n",
"\n",
"class VectorQuantizer(nn.Module):\n",
" def __init__(self, codebook_size=CODEBOOK_SIZE, codebook_dim=CODEBOOK_DIM, commitment_cost=0.25):\n",
" super().__init__()\n",
" self.codebook_size = codebook_size\n",
" self.codebook_dim = codebook_dim\n",
" self.commitment_cost = commitment_cost\n",
" self.codebook = nn.Embedding(codebook_size, codebook_dim)\n",
" self.codebook.weight.data.uniform_(-1.0 / codebook_size, 1.0 / codebook_size)\n",
"\n",
" def forward(self, z):\n",
" B, H, W, C = z.shape\n",
" z_flat = z.reshape(-1, C)\n",
" dist = (z_flat.unsqueeze(1) - self.codebook.weight.unsqueeze(0)).pow(2).sum(-1)\n",
" indices = dist.argmin(dim=1)\n",
" z_q = self.codebook(indices).reshape(B, H, W, C)\n",
" commitment_loss = F.mse_loss(z_flat, z_q.reshape(-1, C).detach())\n",
" codebook_loss = F.mse_loss(z_q.reshape(-1, C), z_flat.detach())\n",
" loss = codebook_loss + self.commitment_cost * commitment_loss\n",
" z_q_st = z + (z_q - z).detach()\n",
" return z_q_st, loss, indices.reshape(B, H, W)\n",
"\n",
"\n",
"class Decoder(nn.Module):\n",
" def __init__(self, out_channels=3, latent_dim=LATENT_DIM):\n",
" super().__init__()\n",
" self.net = nn.Sequential(\n",
" nn.ConvTranspose2d(latent_dim, 256, 4, stride=2, padding=1), # -> 16x16\n",
" nn.ReLU(),\n",
" nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1), # -> 32x32\n",
" nn.ReLU(),\n",
" nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1), # -> 64x64\n",
" nn.ReLU(),\n",
" nn.ConvTranspose2d(64, out_channels, 4, stride=2, padding=1), # -> 128x128\n",
" nn.Sigmoid(),\n",
" )\n",
"\n",
" def forward(self, x):\n",
" return self.net(x)\n",
"\n",
"\n",
"class VQVAE(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.encoder = Encoder()\n",
" self.quantizer = VectorQuantizer()\n",
" self.proj_in = nn.Linear(LATENT_DIM, CODEBOOK_DIM)\n",
" self.proj_out = nn.Linear(CODEBOOK_DIM, LATENT_DIM)\n",
" self.decoder = Decoder()\n",
"\n",
" def forward(self, x):\n",
" z = self.encoder(x)\n",
" z = z.permute(0, 2, 3, 1)\n",
" z = self.proj_in(z)\n",
" z_q, vq_loss, indices = self.quantizer(z)\n",
" z_q = self.proj_out(z_q)\n",
" z_q = z_q.permute(0, 3, 1, 2)\n",
" recon = self.decoder(z_q)\n",
" return recon, vq_loss, indices\n",
"\n",
" def encode(self, x):\n",
" z = self.encoder(x)\n",
" z = z.permute(0, 2, 3, 1)\n",
" z = self.proj_in(z)\n",
" _, _, indices = self.quantizer(z)\n",
" return indices\n",
"\n",
" def decode_tokens(self, token_ids, grid_h=8, grid_w=8):\n",
" if isinstance(token_ids, list):\n",
" token_ids = torch.tensor(token_ids, dtype=torch.long)\n",
" token_ids = token_ids[:grid_h * grid_w]\n",
" if len(token_ids) < grid_h * grid_w:\n",
" token_ids = torch.cat([token_ids, torch.zeros(grid_h * grid_w - len(token_ids), dtype=torch.long)])\n",
" z_q = self.quantizer.codebook(token_ids)\n",
" z_q = self.proj_out(z_q)\n",
" z_q = z_q.reshape(1, grid_h, grid_w, -1).permute(0, 3, 1, 2)\n",
" return self.decoder(z_q)\n",
"\n",
"# Test\n",
"vq_vae = VQVAE().cuda()\n",
"test_input = torch.randn(2, 3, 128, 128).cuda()\n",
"recon, vq_loss, indices = vq_vae(test_input)\n",
"print(f\"VQ-VAE test: input {test_input.shape} -> recon {recon.shape}, indices {indices.shape}, loss {vq_loss.item():.4f}\")\n",
"n_params = sum(p.numel() for p in vq_vae.parameters()) / 1e6\n",
"print(f\"Parameters: {n_params:.1f}M\")\n",
"del vq_vae, test_input\n",
"torch.cuda.empty_cache()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🖼️ Phase 1: Train VQ-VAE on Real Images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 4. Phase 1: Train VQ-VAE\n",
"from datasets import load_dataset\n",
"from torchvision import transforms\n",
"from torch.utils.data import DataLoader, IterableDataset\n",
"import time\n",
"\n",
"# Check if trained VQ-VAE already exists on HF\n",
"VQ_VAE_ALREADY_TRAINED = False # @param {type:\"boolean\"}\n",
"VQ_VAE_EPOCHS = 5 # @param {type:\"integer\"}\n",
"VQ_VAE_LR = 3e-4 # @param {type:\"number\"}\n",
"VQ_VAE_BATCH = 32 # @param {type:\"integer\"}\n",
"VQ_VAE_MAX_IMAGES = 20000 # @param {type:\"integer\"}\n",
"VQ_VAE_IMG_SIZE = 128 # @param {type:\"integer\"}\n",
"\n",
"if VQ_VAE_ALREADY_TRAINED:\n",
" print(\"Skipping VQ-VAE training (already trained)\")\n",
" vq_vae = VQVAE()\n",
" # Download from HF if available\n",
" try:\n",
" from huggingface_hub import hf_hub_download\n",
" vq_path = hf_hub_download(REPO_ID, \"vq_vae_final.pt\", repo_type=\"model\")\n",
" vq_vae.load_state_dict(torch.load(vq_path, map_location=\"cuda\", weights_only=False))\n",
" print(f\"Loaded VQ-VAE from {REPO_ID}\")\n",
" except:\n",
" print(\"Could not download VQ-VAE, training from scratch\")\n",
" VQ_VAE_ALREADY_TRAINED = False\n",
"\n",
"if not VQ_VAE_ALREADY_TRAINED:\n",
" # Load dataset\n",
" print(\"Loading image dataset...\")\n",
" ds = None\n",
" image_key = \"image\"\n",
" cap_key = None\n",
" ds_name = \"\"\n",
"\n",
" for name, split, ik, ck in [\n",
" (\"detection-datasets/coco\", \"train\", \"image\", \"caption\"),\n",
" (\"frgfm/imagenette\", \"train\", \"image\", \"label\"),\n",
" (\"cifar10\", \"train\", \"img\", \"label\"),\n",
" ]:\n",
" try:\n",
" print(f\" Trying {name}...\")\n",
" ds = load_dataset(name, split=split, streaming=True, trust_remote_code=True)\n",
" test_item = next(iter(ds))\n",
" if ik in test_item:\n",
" image_key = ik\n",
" cap_key = ck if ck in test_item else None\n",
" ds_name = name\n",
" print(f\" Using {name}!\")\n",
" break\n",
" ds = None\n",
" except Exception as e:\n",
" print(f\" Failed: {str(e)[:80]}\")\n",
" ds = None\n",
"\n",
" if ds is None:\n",
" raise RuntimeError(\"No dataset available!\")\n",
"\n",
" # Transforms\n",
" transform = transforms.Compose([\n",
" transforms.Resize((VQ_VAE_IMG_SIZE, VQ_VAE_IMG_SIZE)),\n",
" transforms.ToTensor(),\n",
" ])\n",
"\n",
" class ImageStreamDataset(IterableDataset):\n",
" def __init__(self, hf_ds, transform, img_key, max_samples):\n",
" self.ds = hf_ds\n",
" self.transform = transform\n",
" self.img_key = img_key\n",
" self.max = max_samples\n",
"\n",
" def __iter__(self):\n",
" count = 0\n",
" for item in self.ds:\n",
" if count >= self.max:\n",
" break\n",
" try:\n",
" img = item[self.img_key]\n",
" if img.mode != \"RGB\":\n",
" img = img.convert(\"RGB\")\n",
" yield self.transform(img)\n",
" count += 1\n",
" except:\n",
" continue\n",
"\n",
" dataset = ImageStreamDataset(ds, transform, image_key, VQ_VAE_MAX_IMAGES)\n",
" dataloader = DataLoader(dataset, batch_size=VQ_VAE_BATCH, num_workers=2, pin_memory=True)\n",
"\n",
" # Initialize model\n",
" vq_vae = VQVAE().cuda()\n",
" optimizer = torch.optim.Adam(vq_vae.parameters(), lr=VQ_VAE_LR)\n",
" scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=VQ_VAE_EPOCHS)\n",
"\n",
" # Training loop\n",
" print(f\"\\nTraining VQ-VAE: {VQ_VAE_EPOCHS} epochs, {VQ_VAE_MAX_IMAGES} images, batch {VQ_VAE_BATCH}\")\n",
" vq_vae.train()\n",
" best_loss = float('inf')\n",
"\n",
" for epoch in range(VQ_VAE_EPOCHS):\n",
" epoch_loss = 0.0\n",
" epoch_recon = 0.0\n",
" epoch_vq = 0.0\n",
" n_batches = 0\n",
" start = time.time()\n",
"\n",
" for batch_idx, batch in enumerate(dataloader):\n",
" batch = batch.cuda()\n",
" recon, vq_loss, _ = vq_vae(batch)\n",
" recon_loss = F.mse_loss(recon, batch)\n",
" loss = recon_loss + vq_loss\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" torch.nn.utils.clip_grad_norm_(vq_vae.parameters(), 1.0)\n",
" optimizer.step()\n",
"\n",
" epoch_loss += loss.item()\n",
" epoch_recon += recon_loss.item()\n",
" epoch_vq += vq_loss.item()\n",
" n_batches += 1\n",
"\n",
" if batch_idx % 100 == 0 and batch_idx > 0:\n",
" avg = epoch_loss / n_batches\n",
" print(f\" Epoch {epoch+1}/{VQ_VAE_EPOCHS} | Batch {batch_idx} | Loss: {avg:.4f} (recon: {epoch_recon/n_batches:.4f}, vq: {epoch_vq/n_batches:.4f})\")\n",
"\n",
" scheduler.step()\n",
" elapsed = time.time() - start\n",
" avg_loss = epoch_loss / max(n_batches, 1)\n",
" print(f\"\\n Epoch {epoch+1} done. Loss: {avg_loss:.4f} | Batches: {n_batches} | Time: {elapsed:.0f}s\")\n",
"\n",
" # Save best model & push to HF\n",
" if avg_loss < best_loss:\n",
" best_loss = avg_loss\n",
" torch.save(vq_vae.state_dict(), \"vq_vae_best.pt\")\n",
" print(f\" New best model! Loss: {avg_loss:.4f}\")\n",
"\n",
" # Push VQ-VAE checkpoint to HF after each epoch\n",
" torch.save(vq_vae.state_dict(), \"vq_vae_final.pt\")\n",
" try:\n",
" api.upload_file(\n",
" path_or_fileobj=\"vq_vae_final.pt\",\n",
" path_in_repo=\"vq_vae_final.pt\",\n",
" repo_id=REPO_ID,\n",
" repo_type=\"model\",\n",
" commit_message=f\"VQ-VAE epoch {epoch+1}, loss {avg_loss:.4f}\"\n",
" )\n",
" print(f\" Pushed VQ-VAE checkpoint to HF!\")\n",
" except Exception as e:\n",
" print(f\" Push failed: {e}\")\n",
"\n",
" print(f\"\\nVQ-VAE training complete! Best loss: {best_loss:.4f}\")\n",
" vq_vae.eval()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🔢 Phase 2: Tokenize Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 5. Phase 2: Tokenize Image-Text Pairs\n",
"import json\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"NUM_TOKENIZE = 50000 # @param {type:\"integer\"}\n",
"TOKENS_PER_SAMPLE = 64 # 8x8 grid\n",
"\n",
"# Caption helpers\n",
"IMAGENETTE_CLASSES = {\n",
" 0: \"a fish in water\", 1: \"a dog running in a field\", 2: \"a cassette player on a table\",\n",
" 3: \"a chainsaw cutting wood\", 4: \"a church with a tall steeple\", 5: \"a French horn on stage\",\n",
" 6: \"a garbage truck on the street\", 7: \"a gas station at night\", 8: \"a golf ball on a green\",\n",
" 9: \"a parachute in the sky\",\n",
"}\n",
"CIFAR10_CLASSES = [\"airplane flying\", \"automobile on road\", \"bird in tree\", \"cat sitting\",\n",
" \"deer in forest\", \"dog playing\", \"frog on lily pad\", \"horse running\",\n",
" \"ship on ocean\", \"truck driving\"]\n",
"\n",
"def get_caption(item, cap_key, ds_name, idx):\n",
" if cap_key and cap_key in item and item[cap_key] is not None:\n",
" cap = item[cap_key]\n",
" if isinstance(cap, list):\n",
" return cap[0] if cap else f\"image {idx}\"\n",
" elif isinstance(cap, str):\n",
" return cap\n",
" elif isinstance(cap, int):\n",
" if \"imagenette\" in ds_name.lower():\n",
" return IMAGENETTE_CLASSES.get(cap, f\"photo of object {cap}\")\n",
" elif \"cifar\" in ds_name.lower():\n",
" return CIFAR10_CLASSES[cap] if cap < len(CIFAR10_CLASSES) else f\"photo of class {cap}\"\n",
" return f\"photo of a {cap}\"\n",
" return f\"image {idx}\"\n",
"\n",
"# Load dataset for tokenization (re-load to get fresh stream)\n",
"print(\"Loading dataset for tokenization...\")\n",
"ds = None\n",
"image_key = \"image\"\n",
"cap_key = None\n",
"ds_name = \"\"\n",
"\n",
"for name, split, ik, ck in [\n",
" (\"detection-datasets/coco\", \"train\", \"image\", \"caption\"),\n",
" (\"frgfm/imagenette\", \"train\", \"image\", \"label\"),\n",
" (\"cifar10\", \"train\", \"img\", \"label\"),\n",
"]:\n",
" try:\n",
" ds = load_dataset(name, split=split, streaming=True, trust_remote_code=True)\n",
" test_item = next(iter(ds))\n",
" if ik in test_item:\n",
" image_key = ik\n",
" cap_key = ck if ck in test_item else None\n",
" ds_name = name\n",
" print(f\"Using {name}\")\n",
" break\n",
" ds = None\n",
" except:\n",
" ds = None\n",
"\n",
"if ds is None:\n",
" raise RuntimeError(\"No dataset!\")\n",
"\n",
"transform = transforms.Compose([\n",
" transforms.Resize((VQ_VAE_IMG_SIZE, VQ_VAE_IMG_SIZE)),\n",
" transforms.ToTensor(),\n",
"])\n",
"\n",
"vq_vae.eval()\n",
"tokenized_data = []\n",
"count = 0\n",
"errors = 0\n",
"\n",
"print(f\"Tokenizing {NUM_TOKENIZE} images...\")\n",
"for item in ds:\n",
" if count >= NUM_TOKENIZE:\n",
" break\n",
" try:\n",
" img = item[image_key]\n",
" if img.mode != \"RGB\":\n",
" img = img.convert(\"RGB\")\n",
" caption = get_caption(item, cap_key, ds_name, count)\n",
"\n",
" img_tensor = transform(img).unsqueeze(0).cuda()\n",
" with torch.no_grad():\n",
" tokens = vq_vae.encode(img_tensor)\n",
" flat_tokens = tokens.flatten().tolist()\n",
"\n",
" flat_tokens = flat_tokens[:TOKENS_PER_SAMPLE]\n",
" while len(flat_tokens) < TOKENS_PER_SAMPLE:\n",
" flat_tokens.append(0)\n",
"\n",
" tokenized_data.append({\n",
" \"text_prompt\": caption,\n",
" \"video_tokens\": flat_tokens,\n",
" })\n",
" count += 1\n",
"\n",
" if count % 2000 == 0:\n",
" print(f\" Tokenized {count}/{NUM_TOKENIZE} (errors: {errors})\")\n",
" # Save checkpoint\n",
" with open(\"tokenized_dataset.json\", \"w\") as f:\n",
" json.dump(tokenized_data, f)\n",
" # Push to HF\n",
" try:\n",
" api.upload_file(\n",
" path_or_fileobj=\"tokenized_dataset.json\",\n",
" path_in_repo=\"tokenized_dataset.json\",\n",
" repo_id=REPO_ID,\n",
" repo_type=\"model\",\n",
" commit_message=f\"Tokenized {count} samples\"\n",
" )\n",
" except:\n",
" pass\n",
"\n",
" del img_tensor\n",
" if count % 500 == 0:\n",
" torch.cuda.empty_cache()\n",
"\n",
" except Exception as e:\n",
" errors += 1\n",
" if errors <= 3:\n",
" print(f\" Error: {str(e)[:60]}\")\n",
" continue\n",
"\n",
"# Final save & push\n",
"with open(\"tokenized_dataset.json\", \"w\") as f:\n",
" json.dump(tokenized_data, f)\n",
"\n",
"api.upload_file(\n",
" path_or_fileobj=\"tokenized_dataset.json\",\n",
" path_in_repo=\"tokenized_dataset.json\",\n",
" repo_id=REPO_ID,\n",
" repo_type=\"model\",\n",
" commit_message=f\"Tokenized {len(tokenized_data)} samples (complete)\"\n",
")\n",
"\n",
"print(f\"\\nTokenization complete: {len(tokenized_data)} samples ({errors} errors)\")\n",
"print(f\"Sample: '{tokenized_data[0]['text_prompt']}' -> {tokenized_data[0]['video_tokens'][:10]}\")\n",
"print(f\"Unique tokens in sample: {len(set(tokenized_data[0]['video_tokens']))}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🚀 Phase 3: Fine-tune LLM with LoRA (GPU + Incremental Push)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 6. Phase 3: Setup LLM + LoRA with HuggingFace Trainer\n",
"from transformers import (\n",
" AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer,\n",
" DataCollatorForLanguageModeling, TrainerCallback\n",
")\n",
"from peft import LoraConfig, get_peft_model, TaskType\n",
"from torch.utils.data import Dataset\n",
"\n",
"# Hyperparameters\n",
"LORA_R = 8 # @param {type:\"integer\"}\n",
"LORA_ALPHA = 16 # @param {type:\"integer\"}\n",
"LORA_DROPOUT = 0.05 # @param {type:\"number\"}\n",
"LEARNING_RATE = 2e-4 # @param {type:\"number\"}\n",
"BATCH_SIZE = 2 # @param {type:\"integer\"}\n",
"GRADIENT_ACCUMULATION = 8 # @param {type:\"integer\"}\n",
"NUM_EPOCHS = 3 # @param {type:\"integer\"}\n",
"MAX_SEQ_LEN = 256 # @param {type:\"integer\"}\n",
"WARMUP_RATIO = 0.03 # @param {type:\"number\"}\n",
"WEIGHT_DECAY = 0.01 # @param {type:\"number\"}\n",
"SAVE_STEPS = 200 # @param {type:\"integer\"}\n",
"EVAL_STEPS = 200 # @param {type:\"integer\"}\n",
"FP16 = True # @param {type:\"boolean\"}\n",
"TRAIN_ON_ALL_DATA = False # @param {type:\"boolean\"}\n",
"LLM_TRAIN_SAMPLES = 10000 # @param {type:\"integer\"}\n",
"\n",
"MODEL_NAME = \"allenai/OLMo-2-0425-1B-Instruct\"\n",
"VIDEO_START = \"<video_start>\"\n",
"VIDEO_END = \"<video_end>\"\n",
"VIDEO_PAD = \"<video_pad>\"\n",
"\n",
"# Load tokenizer\n",
"print(\"Loading tokenizer...\")\n",
"tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)\n",
"if tokenizer.pad_token is None:\n",
" tokenizer.pad_token = tokenizer.eos_token\n",
"orig_vocab = len(tokenizer)\n",
"print(f\"Original vocab: {orig_vocab}\")\n",
"\n",
"# Expand vocab with visual tokens\n",
"visual_tokens = [VIDEO_START, VIDEO_END, VIDEO_PAD]\n",
"for i in range(CODEBOOK_SIZE):\n",
" visual_tokens.append(f\"<v_{i}>\")\n",
"tokenizer.add_tokens(visual_tokens)\n",
"print(f\"Expanded vocab: {len(tokenizer)} (+{len(tokenizer) - orig_vocab} visual tokens)\")\n",
"\n",
"# Load model\n",
"print(\"Loading model...\")\n",
"dtype = torch.float16 if FP16 else torch.float32\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" MODEL_NAME, trust_remote_code=True, torch_dtype=dtype\n",
")\n",
"model.resize_token_embeddings(len(tokenizer))\n",
"print(f\"Model loaded: {MODEL_NAME}\")\n",
"\n",
"# Apply LoRA\n",
"print(f\"Applying LoRA (r={LORA_R})...\")\n",
"lora_config = LoraConfig(\n",
" r=LORA_R,\n",
" lora_alpha=LORA_ALPHA,\n",
" target_modules=[\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"], # More modules than before!\n",
" lora_dropout=LORA_DROPOUT,\n",
" bias=\"none\",\n",
" task_type=TaskType.CAUSAL_LM,\n",
")\n",
"model = get_peft_model(model, lora_config)\n",
"trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
"total = sum(p.numel() for p in model.parameters())\n",
"print(f\"LoRA: {trainable:,} / {total:,} trainable ({100*trainable/total:.2f}%)\")\n",
"model.print_trainable_parameters()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 7. Create Training Dataset\n",
"class VideoTokenDataset(Dataset):\n",
" def __init__(self, data, tokenizer, max_tokens=64, max_len=256):\n",
" self.data = data\n",
" self.tokenizer = tokenizer\n",
" self.max_tokens = max_tokens\n",
" self.max_len = max_len\n",
"\n",
" def __len__(self):\n",
" return len(self.data)\n",
"\n",
" def __getitem__(self, idx):\n",
" item = self.data[idx]\n",
" prompt = item[\"text_prompt\"]\n",
" tokens = item[\"video_tokens\"][:self.max_tokens]\n",
" while len(tokens) < self.max_tokens:\n",
" tokens.append(0)\n",
" token_str = \" \".join(f\"<v_{t}>\" for t in tokens)\n",
" text = f\"Create a video of: {prompt} {VIDEO_START} {token_str} {VIDEO_END}\"\n",
"\n",
" encoding = self.tokenizer(\n",
" text, return_tensors=\"pt\", truncation=True,\n",
" max_length=self.max_len, padding=\"max_length\"\n",
" )\n",
" input_ids = encoding[\"input_ids\"].squeeze()\n",
" attention_mask = encoding[\"attention_mask\"].squeeze()\n",
" labels = input_ids.clone()\n",
" # Don't compute loss on padding\n",
" labels[labels == self.tokenizer.pad_token_id] = -100\n",
"\n",
" return {\n",
" \"input_ids\": input_ids,\n",
" \"attention_mask\": attention_mask,\n",
" \"labels\": labels,\n",
" }\n",
"\n",
"# Load data\n",
"with open(\"tokenized_dataset.json\") as f:\n",
" all_data = json.load(f)\n",
"\n",
"if not TRAIN_ON_ALL_DATA:\n",
" all_data = all_data[:LLM_TRAIN_SAMPLES]\n",
"\n",
"print(f\"Training on {len(all_data)} samples\")\n",
"\n",
"# Split into train/eval\n",
"split_idx = int(len(all_data) * 0.95)\n",
"train_data = all_data[:split_idx]\n",
"eval_data = all_data[split_idx:]\n",
"\n",
"train_dataset = VideoTokenDataset(train_data, tokenizer)\n",
"eval_dataset = VideoTokenDataset(eval_data, tokenizer)\n",
"\n",
"print(f\"Train: {len(train_dataset)}, Eval: {len(eval_dataset)}\")\n",
"\n",
"# Test one sample\n",
"sample = train_dataset[0]\n",
"decoded = tokenizer.decode(sample[\"input_ids\"][:80], skip_special_tokens=False)\n",
"print(f\"Sample: {decoded[:200]}...\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 8. Configure HuggingFace Trainer with Incremental Push\n",
"\n",
"# Training arguments with push_to_hub for incremental checkpoint saves\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./zeeb-checkpoints\",\n",
" \n",
" # Training params\n",
" num_train_epochs=NUM_EPOCHS,\n",
" per_device_train_batch_size=BATCH_SIZE,\n",
" per_device_eval_batch_size=BATCH_SIZE,\n",
" gradient_accumulation_steps=GRADIENT_ACCUMULATION,\n",
" learning_rate=LEARNING_RATE,\n",
" weight_decay=WEIGHT_DECAY,\n",
" warmup_ratio=WARMUP_RATIO,\n",
" lr_scheduler_type=\"cosine\",\n",
" max_grad_norm=1.0,\n",
" \n",
" # Precision\n",
" fp16=FP16,\n",
" bf16=False,\n",
" \n",
" # Logging\n",
" logging_steps=10,\n",
" logging_first_step=True,\n",
" \n",
" # Saving - INCREMENTAL PUSH TO HF\n",
" save_strategy=\"steps\",\n",
" save_steps=SAVE_STEPS,\n",
" save_total_limit=3, # Keep only 3 checkpoints on disk\n",
" \n",
" # Evaluation\n",
" eval_strategy=\"steps\",\n",
" eval_steps=EVAL_STEPS,\n",
" \n",
" # INCREMENTAL PUSH TO HUGGINGFACE\n",
" push_to_hub=True,\n",
" hub_model_id=REPO_ID,\n",
" hub_token=HF_TOKEN,\n",
" hub_strategy=\"every_save\", # Push every time we save a checkpoint!\n",
" \n",
" # Resume from checkpoint\n",
" resume_from_checkpoint=True,\n",
" \n",
" # Performance\n",
" dataloader_num_workers=2,\n",
" dataloader_pin_memory=True,\n",
" gradient_checkpointing=True, # Save memory\n",
" optim=\"adamw_torch\",\n",
" \n",
" # Misc\n",
" remove_unused_columns=False,\n",
" report_to=\"none\", # Disable wandb/tensorboard\n",
" run_name=\"zeeb-video-llm\",\n",
")\n",
"\n",
"print(\"Training Arguments:\")\n",
"print(f\" Epochs: {NUM_EPOCHS}\")\n",
"print(f\" Batch: {BATCH_SIZE} x {GRADIENT_ACCUMULATION} accumulation = effective {BATCH_SIZE * GRADIENT_ACCUMULATION}\")\n",
"print(f\" LR: {LEARNING_RATE}, Scheduler: cosine\")\n",
"print(f\" FP16: {FP16}\")\n",
"print(f\" Save every {SAVE_STEPS} steps → push to HF\")\n",
"print(f\" Push to: {REPO_ID}\")\n",
"print(f\" Hub strategy: every_save (incremental push)\")\n",
"print(f\" Gradient checkpointing: True\")\n",
"print(f\" Resume from checkpoint: True\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 9. 🚀 START TRAINING! (with auto-resume)\n",
"import os\n",
"\n",
"# Check for existing checkpoints to resume from\n",
"checkpoint_dir = \"./zeeb-checkpoints\"\n",
"resume_ckpt = None\n",
"if os.path.exists(checkpoint_dir):\n",
" checkpoints = [d for d in os.listdir(checkpoint_dir) if d.startswith(\"checkpoint-\")]\n",
" if checkpoints:\n",
" latest = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))[-1]\n",
" resume_ckpt = os.path.join(checkpoint_dir, latest)\n",
" print(f\"Found checkpoint to resume from: {resume_ckpt}\")\n",
"\n",
"# Create trainer\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=eval_dataset,\n",
" data_collator=None, # Use default\n",
")\n",
"\n",
"# Calculate total steps\n",
"total_steps = (len(train_dataset) // (BATCH_SIZE * GRADIENT_ACCUMULATION)) * NUM_EPOCHS\n",
"print(f\"\\nTotal training steps: ~{total_steps}\")\n",
"print(f\"Checkpoints will be pushed every {SAVE_STEPS} steps ({total_steps // SAVE_STEPS} pushes)\")\n",
"print(f\"\\nStarting training...\")\n",
"print(f\"If Colab disconnects, just re-run this cell — it will auto-resume!\\n\")\n",
"\n",
"# Train! (auto-resumes from checkpoint if available)\n",
"train_result = trainer.train(resume_from_checkpoint=resume_ckpt)\n",
"\n",
"print(f\"\\nTraining complete!\")\n",
"print(f\" Final loss: {train_result.training_loss:.4f}\")\n",
"print(f\" Total steps: {train_result.global_step}\")\n",
"print(f\" Training time: {train_result.metrics['train_runtime']:.0f}s ({train_result.metrics['train_runtime']/60:.1f} min)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 10. Merge LoRA & Push Final Model to HuggingFace\n",
"print(\"Merging LoRA weights into base model...\")\n",
"model = model.merge_and_unload()\n",
"\n",
"# Save locally\n",
"final_dir = \"./zeeb-final\"\n",
"model.save_pretrained(final_dir, safe_serialization=True)\n",
"tokenizer.save_pretrained(final_dir)\n",
"\n",
"# Copy VQ-VAE checkpoint\n",
"import shutil\n",
"if os.path.exists(\"vq_vae_final.pt\"):\n",
" shutil.copy(\"vq_vae_final.pt\", f\"{final_dir}/vq_vae_final.pt\")\n",
"if os.path.exists(\"tokenized_dataset.json\"):\n",
" shutil.copy(\"tokenized_dataset.json\", f\"{final_dir}/tokenized_dataset.json\")\n",
"\n",
"# Push final merged model to HuggingFace\n",
"print(f\"Pushing final model to {REPO_ID}...\")\n",
"model.push_to_hub(\n",
" REPO_ID,\n",
" token=HF_TOKEN,\n",
" commit_message=f\"Zeeb v2: OLMo 2 1B + LoRA (r={LORA_R}), {NUM_EPOCHS} epochs, {len(train_data)} samples, GPU-trained\"\n",
")\n",
"tokenizer.push_to_hub(\n",
" REPO_ID,\n",
" token=HF_TOKEN,\n",
" commit_message=f\"Zeeb v2: tokenizer with visual tokens\"\n",
")\n",
"\n",
"# Push additional files\n",
"for fname in [\"vq_vae_final.pt\", \"tokenized_dataset.json\"]:\n",
" if os.path.exists(fname):\n",
" api.upload_file(\n",
" path_or_fileobj=fname,\n",
" path_in_repo=fname,\n",
" repo_id=REPO_ID,\n",
" repo_type=\"model\",\n",
" commit_message=f\"Add {fname}\"\n",
" )\n",
"\n",
"print(f\"\\n✅ Final model pushed to https://huggingface.co/{REPO_ID}\")\n",
"print(\"This model can now be loaded in the HF Space for video generation!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🧪 Test: Generate a Video with the Trained Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 11. Test Video Generation\n",
"import numpy as np\n",
"from PIL import Image\n",
"import imageio\n",
"\n",
"PROMPT = \"A cat jumping on a sofa\" # @param {type:\"string\"}\n",
"MAX_TOKENS = 64 # @param {type:\"integer\"}\n",
"TEMPERATURE = 0.9 # @param {type:\"number\"}\n",
"TOP_K = 50 # @param {type:\"integer\"}\n",
"\n",
"# Get visual token IDs\n",
"VIDEO_START_ID = tokenizer.convert_tokens_to_ids(\"<video_start>\")\n",
"VIDEO_END_ID = tokenizer.convert_tokens_to_ids(\"<video_end>\")\n",
"V_TOKEN_START_ID = tokenizer.convert_tokens_to_ids(\"<v_0>\")\n",
"V_TOKEN_END_ID = tokenizer.convert_tokens_to_ids(\"<v_1023>\")\n",
"\n",
"# Load VQ-VAE for decoding\n",
"vq_vae = VQVAE().cuda()\n",
"if os.path.exists(\"vq_vae_final.pt\"):\n",
" vq_vae.load_state_dict(torch.load(\"vq_vae_final.pt\", map_location=\"cuda\", weights_only=False))\n",
" print(\"Loaded trained VQ-VAE\")\n",
"vq_vae.eval()\n",
"\n",
"# Generate with constrained decoding\n",
"text = f\"Create a video of: {PROMPT} <video_start>\"\n",
"inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, max_length=256)\n",
"current_ids = inputs[\"input_ids\"].cuda()\n",
"\n",
"vocab_size = len(tokenizer)\n",
"visual_mask = torch.zeros(vocab_size, dtype=torch.bool)\n",
"visual_mask[V_TOKEN_START_ID:V_TOKEN_END_ID + 1] = True\n",
"visual_mask[VIDEO_END_ID] = True\n",
"\n",
"visual_token_ids = []\n",
"model.eval()\n",
"\n",
"print(f\"Generating visual tokens for: '{PROMPT}'\")\n",
"with torch.no_grad():\n",
" for step in range(MAX_TOKENS):\n",
" outputs = model(input_ids=current_ids)\n",
" logits = outputs.logits[:, -1, :]\n",
" masked = logits.clone()\n",
" masked[0, ~visual_mask] = float('-inf')\n",
" masked = masked / max(TEMPERATURE, 0.01)\n",
" if TOP_K > 0:\n",
" top_k_values, _ = torch.topk(masked[0], min(TOP_K, masked.size(-1)))\n",
" threshold = top_k_values[-1]\n",
" masked[0, masked[0] < threshold] = float('-inf')\n",
" probs = F.softmax(masked, dim=-1)\n",
" next_token = torch.multinomial(probs, num_samples=1)\n",
" next_id = next_token.item()\n",
" if next_id == VIDEO_END_ID:\n",
" break\n",
" visual_idx = next_id - V_TOKEN_START_ID\n",
" visual_token_ids.append(visual_idx)\n",
" current_ids = torch.cat([current_ids, next_token], dim=-1)\n",
"\n",
"print(f\"Generated {len(visual_token_ids)} visual tokens ({len(set(visual_token_ids))} unique)\")\n",
"\n",
"# Decode through VQ-VAE\n",
"grid_h, grid_w = 8, 8\n",
"tokens_per_frame = grid_h * grid_w\n",
"num_frames = max(1, len(visual_token_ids) // tokens_per_frame)\n",
"\n",
"frames = []\n",
"for fi in range(num_frames):\n",
" ft = visual_token_ids[fi*tokens_per_frame:(fi+1)*tokens_per_frame]\n",
" frame_tensor = vq_vae.decode_tokens(ft, grid_h, grid_w)\n",
" frame_np = (frame_tensor[0].permute(1, 2, 0).detach().cpu().numpy() * 255).astype(np.uint8)\n",
" frames.append(frame_np)\n",
"\n",
"# Save video\n",
"if frames:\n",
" upscaled = [np.array(Image.fromarray(f).resize((256, 256), Image.BILINEAR)) for f in frames]\n",
" output_path = \"/content/generated_video.mp4\"\n",
" imageio.mimsave(output_path, upscaled, fps=2)\n",
" print(f\"Video saved: {output_path} ({len(upscaled)} frames, 256x256)\")\n",
" \n",
" # Display first frame\n",
" from IPython.display import display\n",
" display(Image.fromarray(upscaled[0]))\n",
"else:\n",
" print(\"No frames generated\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 📊 Summary & Next Steps\n",
"\n",
"### What was trained:\n",
"- **VQ-VAE**: 3.8M params, trained on real COCO images, maps images ↔ discrete tokens\n",
"- **OLMo 2 1B + LoRA**: 1B params (only ~1M trainable), fine-tuned to predict visual tokens from text\n",
"\n",
"### How to improve further:\n",
"1. **More data**: Use 50K+ samples instead of 10K\n",
"2. **Bigger LoRA**: Increase r from 8 to 16-32\n",
"3. **More target modules**: Add \"gate_proj\", \"up_proj\", \"down_proj\" to LoRA targets\n",
"4. **Video data**: Use OpenVid-1M with actual video frames (multiple frames per clip)\n",
"5. **Larger codebook**: 4096 or 8192 entries instead of 1024\n",
"6. **Higher resolution**: 256x256 VQ-VAE instead of 128x128\n",
"7. **Multi-frame**: Encode 4-8 frames per video, not just 1\n",
"\n",
"### Resume after Colab disconnect:\n",
"Just re-run cells 1, 2, 3, 6, 7, 8, and 9 — the Trainer will auto-resume from the last checkpoint pushed to HF!"
]
}
]
}
|