Upload Qwen destill.ipynb

Qwen destill.ipynb

@@ -1 +1 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1rORehICZ99xZsrwMfy2w8Jxg6M55d81L","timestamp":1774986352541}],"gpuType":"T4","mount_file_id":"1rORehICZ99xZsrwMfy2w8Jxg6M55d81L","authorship_tag":"ABX9TyPIIjhGAQMr0WHONJwg0qwI"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["# ==================== CELL 1: Setup & Model Loading ====================\n","\n","import os\n","from google.colab import userdata, drive\n","import torch\n","from diffusers import Flux2KleinPipeline, AutoencoderKL\n","from huggingface_hub import hf_hub_download\n","\n","# Mount Google Drive (if needed for saving outputs)\n","drive.mount('/content/drive', force_remount=True)\n","\n","print(\"🔍 Checking GPU...\")\n","!nvidia-smi\n","\n","# Use HF_TOKEN from Colab Secrets (no login() call needed)\n","os.environ[\"HF_TOKEN\"] = userdata.get(\"HF_TOKEN\")\n","\n","# Install/update latest diffusers + torch (for Tesla T4 compatibility)\n","!pip install -U diffusers transformers accelerate torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121\n","\n","device = \"cuda\"\n","dtype = torch.bfloat16   # Best balance for Tesla T4 + Flux models\n","\n","print(\"📦 Loading FLUX.2 [klein] 4B pipeline...\")\n","\n","# Option A: Simple full pipeline load (recommended first try)\n","pipe = Flux2KleinPipeline.from_pretrained(\n","    \"black-forest-labs/FLUX.2-klein-4B\",\n","    torch_dtype=dtype,\n","    # variant=\"fp8\"          # uncomment if you want the smaller FP8 version (faster but slightly lower quality)\n",").to(device)\n","\n","# Option B: Load with custom/special VAE (uncomment if you want explicit control or if Option A fails)\n","\"\"\"\n","# Download the special flux2-vae.safetensors if needed\n","vae_path = hf_hub_download(\n","    repo_id=\"Comfy-Org/vae-text-encorder-for-flux-klein-4b\",\n","    filename=\"split_files/vae/flux2-vae.safetensors\",\n","    local_dir=\"/content/models/vae\",\n","    force_download=False\n",")\n","\n","vae = AutoencoderKL.from_single_file(\n","    vae_path,\n","    torch_dtype=dtype\n",")\n","\n","pipe = Flux2KleinPipeline.from_pretrained(\n","    \"black-forest-labs/FLUX.2-klein-4B\",\n","    vae=vae,                    # override with the special Flux.2 VAE\n","    torch_dtype=dtype,\n",").to(device)\n","\"\"\"\n","\n","# Memory optimizations for Tesla T4 (highly recommended)\n","pipe.vae.enable_slicing()\n","pipe.vae.enable_tiling()\n","\n","print(\"✅ FLUX.2 [klein] 4B loaded successfully!\")\n","print(f\"   Device: {device} | Dtype: {dtype}\")\n","print(\"   VAE slicing + tiling enabled for lower VRAM usage.\")"],"metadata":{"id":"BtsjbO4uY53B"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# ============================= COMBINED CELL 2: TRAIN + EVALUATE + SAVE TO DRIVE =============================\n","# @title Combined: Train Distilled Qwen-family Encoder + Full Evaluation + Save to Drive\n","\n","import os\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import numpy as np\n","from sklearn.decomposition import PCA\n","from sklearn.metrics.pairwise import cosine_similarity\n","from tqdm import tqdm\n","from google.colab import drive\n","from torch.utils.data import Dataset\n","from transformers import AutoTokenizer, AutoModel, Trainer, TrainingArguments, set_seed\n","from peft import LoraConfig, get_peft_model, PeftModel\n","from datasets import Dataset as HFDataset\n","\n","set_seed(42)\n","drive.mount('/content/drive')\n","\n","print(\"🔍 Checking GPU...\")\n","!nvidia-smi\n","if not torch.cuda.is_available():\n","    raise RuntimeError(\"No GPU detected!\")\n","\n","# ====================== 1. Load Teacher Embeddings ======================\n","embed_path = \"/content/drive/MyDrive/qwen_embeddings.pt\"\n","data = torch.load(embed_path, weights_only=False)\n","teacher_embeddings = torch.stack(data[\"embeddings\"])  # [250, 1024]\n","texts = data.get(\"texts\", [f\"text_{i}\" for i in range(len(teacher_embeddings))])\n","\n","print(f\"✅ Loaded {len(texts)} texts from Qwen teacher\")\n","\n","hf_dataset = HFDataset.from_dict({\"text\": texts})\n","\n","# ====================== 2. Student Model (Qwen2.5-0.5B + LoRA) ======================\n","student_model_name = \"Qwen/Qwen2.5-0.5B\"\n","student_tokenizer = AutoTokenizer.from_pretrained(student_model_name)\n","base_model = AutoModel.from_pretrained(student_model_name, torch_dtype=torch.float32, device_map=\"auto\", trust_remote_code=True)\n","\n","lora_config = LoraConfig(\n","    r=16,\n","    lora_alpha=32,\n","    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"],\n","    lora_dropout=0.05,\n","    bias=\"none\",\n","    task_type=\"FEATURE_EXTRACTION\"\n",")\n","student_model = get_peft_model(base_model, lora_config)\n","\n","hidden_size = student_model.config.hidden_size\n","projection = nn.Linear(hidden_size, 1024).to(student_model.device)\n","projection.train()\n","\n","print(f\"👨‍🎓 Student: {student_model_name} + LoRA + projection ({hidden_size}→1024)\")\n","\n","# ====================== 3. Dataset ======================\n","class DistillationDataset(Dataset):\n","    def __init__(self, hf_dataset, tokenizer, teacher_embs, max_length=512):\n","        self.dataset = hf_dataset\n","        self.tokenizer = tokenizer\n","        self.teacher_embs = teacher_embs\n","        self.max_length = max_length\n","\n","    def __len__(self): return len(self.dataset)\n","\n","    def __getitem__(self, idx):\n","        text = self.dataset[idx][\"text\"]\n","        inputs = self.tokenizer(text, padding=\"max_length\", truncation=True, max_length=self.max_length, return_tensors=\"pt\")\n","        return {\n","            \"input_ids\": inputs[\"input_ids\"].squeeze(0),\n","            \"attention_mask\": inputs[\"attention_mask\"].squeeze(0),\n","            \"labels\": self.teacher_embs[idx],\n","            \"idx\": idx\n","        }\n","\n","distill_dataset = DistillationDataset(hf_dataset, student_tokenizer, teacher_embeddings)\n","\n","def collate_fn(batch):\n","    return {\n","        \"input_ids\": torch.stack([item[\"input_ids\"] for item in batch]),\n","        \"attention_mask\": torch.stack([item[\"attention_mask\"] for item in batch]),\n","        \"labels\": torch.stack([item[\"labels\"] for item in batch]),\n","        \"idx\": torch.tensor([item[\"idx\"] for item in batch])\n","    }\n","\n","# ====================== 4. Trainer with Logging ======================\n","class DistillationTrainer(Trainer):\n","    def __init__(self, *args, **kwargs):\n","        super().__init__(*args, **kwargs)\n","        self.log_history = []\n","\n","    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n","        labels = inputs.pop(\"labels\")\n","        outputs = model(input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"])\n","        hidden = outputs.last_hidden_state.mean(dim=1)\n","        student_emb = projection(hidden)\n","\n","        student_norm = F.normalize(student_emb, p=2, dim=1)\n","        teacher_norm = F.normalize(labels.to(student_emb.device), p=2, dim=1)\n","\n","        mse_loss = F.mse_loss(student_norm, teacher_norm)\n","        cos_loss = (1 - F.cosine_similarity(student_norm, teacher_norm, dim=1)).mean()\n","\n","        total_loss = 0.25 * mse_loss + 0.75 * cos_loss   # Strong emphasis on direction\n","\n","        return (total_loss, outputs) if return_outputs else total_loss\n","\n","    def log(self, logs):\n","        super().log(logs)\n","        self.log_history.append(logs)\n","\n","training_args = TrainingArguments(\n","    output_dir=\"./qwen_family_distilled\",\n","    per_device_train_batch_size=8,\n","    num_train_epochs=40,\n","    learning_rate=2e-4,\n","    fp16=True,\n","    logging_steps=50,                    # Print loss every 50 steps as requested\n","    save_strategy=\"no\",\n","    report_to=\"none\",\n","    remove_unused_columns=False,\n",")\n","\n","trainer = DistillationTrainer(\n","    model=student_model,\n","    args=training_args,\n","    train_dataset=distill_dataset,\n","    data_collator=collate_fn,\n",")\n","\n","print(\"🚀 Starting training (loss will be printed every 50 steps)...\")\n","trainer.train()\n","\n","# ====================== 5. Save to Google Drive ======================\n","final_save_dir = \"/content/drive/MyDrive/distilled_qwen_encoder_for_flux\"\n","os.makedirs(final_save_dir, exist_ok=True)\n","\n","student_model.save_pretrained(final_save_dir)\n","student_tokenizer.save_pretrained(final_save_dir)\n","torch.save(projection.state_dict(), f\"{final_save_dir}/projection.pth\")\n","\n","print(f\"✅ Model + projection saved to Google Drive: {final_save_dir}\")\n","\n","# ====================== 6. Full Evaluation ======================\n","print(\"\\n🔄 Running full evaluation...\")\n","\n","student_model.eval()\n","student_embeddings = []\n","with torch.no_grad():\n","    for text in tqdm(texts, desc=\"Final encoding\"):\n","        inputs = student_tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors=\"pt\").to(student_model.device)\n","        outputs = student_model(**inputs)\n","        hidden = outputs.last_hidden_state.mean(dim=1)\n","        emb = projection(hidden)\n","        student_embeddings.append(emb.squeeze(0).cpu())\n","\n","student_embeddings = torch.stack(student_embeddings).to(student_model.device)\n","\n","# Metrics\n","mse = torch.nn.functional.mse_loss(student_embeddings, teacher_embeddings).item()\n","cos_sims = [cosine_similarity(student_embeddings[i].unsqueeze(0).cpu().numpy(),\n","                              teacher_embeddings[i].unsqueeze(0).cpu().numpy())[0][0]\n","            for i in range(len(texts))]\n","\n","avg_cosine = np.mean(cos_sims)\n","std_cosine = np.std(cos_sims)\n","\n","teacher_norms = torch.norm(teacher_embeddings, dim=1).cpu().numpy()\n","student_norms = torch.norm(student_embeddings, dim=1).cpu().numpy()\n","\n","print(f\"\\n📊 Final MSE: {mse:.4f}\")\n","print(f\"📊 Average Cosine Similarity: {avg_cosine:.4f} (± {std_cosine:.4f})\")\n","print(f\"Teacher norm: {teacher_norms.mean():.1f} | Student norm: {student_norms.mean():.1f}\")\n","\n","# PCA Plot\n","all_embs = torch.cat([teacher_embeddings.cpu(), student_embeddings.cpu()], dim=0).numpy()\n","pca = PCA(n_components=2, random_state=42)\n","pca_result = pca.fit_transform(all_embs)\n","teacher_pca = pca_result[:len(texts)]\n","student_pca = pca_result[len(texts):]\n","\n","plt.figure(figsize=(14, 10))\n","sns.set_style(\"whitegrid\")\n","plt.scatter(teacher_pca[:, 0], teacher_pca[:, 1], c='blue', label='Qwen Teacher', s=65, marker='o')\n","plt.scatter(student_pca[:, 0], student_pca[:, 1], c='red', label='Distilled Student', s=65, marker='x')\n","for i in range(len(texts)):\n","    plt.plot([teacher_pca[i, 0], student_pca[i, 0]], [teacher_pca[i, 1], student_pca[i, 1]], 'k--', alpha=0.35)\n","plt.title('Shared PCA Space: Teacher vs Student (dotted = same text)')\n","plt.legend()\n","plt.grid(True, alpha=0.3)\n","plt.tight_layout()\n","plt.show()\n","\n","print(\"\\n✅ Training + Evaluation completed. Model saved to Google Drive.\")"],"metadata":{"id":"JYKWzj0iXrhZ"},"execution_count":null,"outputs":[]}]}
+{"cells":[{"cell_type":"code","source":["# ============================= SESSION 1: ENCODE IMAGES TO FLUX VAE LATENTS + TEXT TO QWEN EMBEDDINGS (FIXED) =============================\n","# @title 1. Process Images → Flux VAE Latents + Texts → Qwen Embeddings (with VRAM cleanup)\n","\n","zip_path = '/content/drive/MyDrive/my_set.zip'  # @param {type:'string'}\n","\n","import os\n","import zipfile\n","import torch\n","import numpy as np\n","from google.colab import drive\n","from PIL import Image\n","from tqdm import tqdm\n","from diffusers import AutoencoderKL\n","from transformers import AutoTokenizer, AutoModel\n","\n","drive.mount('/content/drive')\n","\n","print(\"🔍 Checking GPU...\")\n","!nvidia-smi\n","\n","# ====================== Unzip ======================\n","print(\"📦 Extracting zip file...\")\n","extract_dir = \"/content/data\"\n","os.makedirs(extract_dir, exist_ok=True)\n","\n","with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","    zip_ref.extractall(extract_dir)\n","\n","# Find images\n","image_files = [f for f in os.listdir(extract_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]\n","print(f\"✅ Found {len(image_files)} images\")\n","\n","# ====================== 1. Encode Images → Flux VAE Latents ======================\n","print(\"\\n🌀 Loading Flux VAE (float32 for compatibility) and encoding images...\")\n","\n","vae = AutoencoderKL.from_pretrained(\n","    \"black-forest-labs/FLUX.1-dev\",\n","    subfolder=\"vae\",\n","    torch_dtype=torch.float32,   # Changed to float32 to avoid dtype mismatch\n","    device_map=\"auto\"\n",")\n","vae.eval()\n","\n","latent_dir = \"/content/drive/MyDrive/flux_klein_latents\"\n","os.makedirs(latent_dir, exist_ok=True)\n","\n","with torch.no_grad():\n","    for img_file in tqdm(image_files, desc=\"Encoding images to latents\"):\n","        img_path = os.path.join(extract_dir, img_file)\n","        image = Image.open(img_path).convert(\"RGB\")\n","\n","        # Resize to Flux-preferred resolution\n","        image = image.resize((1024, 1024), Image.LANCZOS)\n","\n","        # Create pixel_values and cast to VAE dtype\n","        pixel_values = (torch.from_numpy(np.array(image)).permute(2, 0, 1).unsqueeze(0).float() / 255.0)\n","        pixel_values = pixel_values.to(vae.device, dtype=vae.dtype) * 2.0 - 1.0\n","\n","        latents = vae.encode(pixel_values).latent_dist.sample() * vae.config.scaling_factor\n","\n","        # Save latent\n","        latent_name = os.path.splitext(img_file)[0] + \".pt\"\n","        torch.save(latents.cpu(), os.path.join(latent_dir, latent_name))\n","\n","print(f\"✅ Image latents saved to: {latent_dir}\")\n","\n","# Unload VAE\n","del vae\n","torch.cuda.empty_cache()\n","print(\"🗑️ VAE unloaded. VRAM freed.\\n\")\n","\n","# ====================== 2. Encode Texts → Qwen Embeddings ======================\n","print(\"📝 Loading Qwen text encoder and computing embeddings...\")\n","\n","# Load texts (1.txt to 7.txt or any .txt starting with digit)\n","text_files = [f for f in os.listdir(extract_dir) if f.endswith('.txt') and f[0].isdigit()]\n","texts = []\n","for tf in sorted(text_files):\n","    with open(os.path.join(extract_dir, tf), \"r\", encoding=\"utf-8\") as f:\n","        content = f.read().strip()\n","        if content:\n","            texts.append(content)\n","\n","print(f\"✅ Loaded {len(texts)} text files\")\n","\n","teacher_model_name = \"Qwen/Qwen3-Embedding-0.6B\"\n","tokenizer = AutoTokenizer.from_pretrained(teacher_model_name)\n","model = AutoModel.from_pretrained(\n","    teacher_model_name,\n","    torch_dtype=torch.float16,\n","    device_map=\"auto\",\n","    trust_remote_code=True\n",")\n","model.eval()\n","\n","def mean_pooling(model_output, attention_mask):\n","    token_embeddings = model_output[0]\n","    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n","    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n","\n","embeddings = []\n","with torch.no_grad():\n","    for text in tqdm(texts, desc=\"Encoding texts\"):\n","        inputs = tokenizer(text, padding=True, truncation=True, max_length=8192, return_tensors=\"pt\").to(model.device)\n","        outputs = model(**inputs)\n","        emb = mean_pooling(outputs, inputs['attention_mask']).squeeze(0).cpu()\n","        embeddings.append(emb)\n","\n","# Save embeddings\n","embed_save_path = \"/content/drive/MyDrive/qwen_embeddings.pt\"\n","torch.save({\n","    \"embeddings\": embeddings,\n","    \"texts\": texts,\n","    \"model_name\": teacher_model_name\n","}, embed_save_path)\n","\n","print(f\"✅ Qwen embeddings saved to: {embed_save_path}\")\n","\n","# Unload Qwen encoder\n","del model, tokenizer\n","torch.cuda.empty_cache()\n","print(\"🗑️ Qwen encoder unloaded. VRAM freed.\")\n","\n","print(\"\\n🎉 Session 1 completed successfully!\")\n","print(\"   • Flux VAE latents saved to /content/drive/MyDrive/flux_klein_latents\")\n","print(\"   • Qwen embeddings saved to /content/drive/MyDrive/qwen_embeddings.pt\")\n","print(\"You can now restart the runtime if needed and run Cell 2 for distillation training.\")"],"metadata":{"id":"nLqTMpUSfbe3"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JYKWzj0iXrhZ"},"outputs":[],"source":["# ============================= COMBINED CELL 2: TRAIN + EVALUATE + SAVE TO DRIVE =============================\n","# @title Combined: Train Distilled Qwen-family Encoder + Full Evaluation + Save to Drive\n","\n","import os\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import numpy as np\n","from sklearn.decomposition import PCA\n","from sklearn.metrics.pairwise import cosine_similarity\n","from tqdm import tqdm\n","from google.colab import drive\n","from torch.utils.data import Dataset\n","from transformers import AutoTokenizer, AutoModel, Trainer, TrainingArguments, set_seed\n","from peft import LoraConfig, get_peft_model, PeftModel\n","from datasets import Dataset as HFDataset\n","\n","set_seed(42)\n","drive.mount('/content/drive')\n","\n","print(\"🔍 Checking GPU...\")\n","!nvidia-smi\n","if not torch.cuda.is_available():\n","    raise RuntimeError(\"No GPU detected!\")\n","\n","# ====================== 1. Load Teacher Embeddings ======================\n","embed_path = \"/content/drive/MyDrive/qwen_embeddings.pt\"\n","data = torch.load(embed_path, weights_only=False)\n","teacher_embeddings = torch.stack(data[\"embeddings\"])  # [250, 1024]\n","texts = data.get(\"texts\", [f\"text_{i}\" for i in range(len(teacher_embeddings))])\n","\n","print(f\"✅ Loaded {len(texts)} texts from Qwen teacher\")\n","\n","hf_dataset = HFDataset.from_dict({\"text\": texts})\n","\n","# ====================== 2. Student Model (Qwen2.5-0.5B + LoRA) ======================\n","student_model_name = \"Qwen/Qwen2.5-0.5B\"\n","student_tokenizer = AutoTokenizer.from_pretrained(student_model_name)\n","base_model = AutoModel.from_pretrained(student_model_name, torch_dtype=torch.float32, device_map=\"auto\", trust_remote_code=True)\n","\n","lora_config = LoraConfig(\n","    r=16,\n","    lora_alpha=32,\n","    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"],\n","    lora_dropout=0.05,\n","    bias=\"none\",\n","    task_type=\"FEATURE_EXTRACTION\"\n",")\n","student_model = get_peft_model(base_model, lora_config)\n","\n","hidden_size = student_model.config.hidden_size\n","projection = nn.Linear(hidden_size, 1024).to(student_model.device)\n","projection.train()\n","\n","print(f\"👨‍🎓 Student: {student_model_name} + LoRA + projection ({hidden_size}→1024)\")\n","\n","# ====================== 3. Dataset ======================\n","class DistillationDataset(Dataset):\n","    def __init__(self, hf_dataset, tokenizer, teacher_embs, max_length=512):\n","        self.dataset = hf_dataset\n","        self.tokenizer = tokenizer\n","        self.teacher_embs = teacher_embs\n","        self.max_length = max_length\n","\n","    def __len__(self): return len(self.dataset)\n","\n","    def __getitem__(self, idx):\n","        text = self.dataset[idx][\"text\"]\n","        inputs = self.tokenizer(text, padding=\"max_length\", truncation=True, max_length=self.max_length, return_tensors=\"pt\")\n","        return {\n","            \"input_ids\": inputs[\"input_ids\"].squeeze(0),\n","            \"attention_mask\": inputs[\"attention_mask\"].squeeze(0),\n","            \"labels\": self.teacher_embs[idx],\n","            \"idx\": idx\n","        }\n","\n","distill_dataset = DistillationDataset(hf_dataset, student_tokenizer, teacher_embeddings)\n","\n","def collate_fn(batch):\n","    return {\n","        \"input_ids\": torch.stack([item[\"input_ids\"] for item in batch]),\n","        \"attention_mask\": torch.stack([item[\"attention_mask\"] for item in batch]),\n","        \"labels\": torch.stack([item[\"labels\"] for item in batch]),\n","        \"idx\": torch.tensor([item[\"idx\"] for item in batch])\n","    }\n","\n","# ====================== 4. Trainer with Logging ======================\n","class DistillationTrainer(Trainer):\n","    def __init__(self, *args, **kwargs):\n","        super().__init__(*args, **kwargs)\n","        self.log_history = []\n","\n","    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n","        labels = inputs.pop(\"labels\")\n","        outputs = model(input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"])\n","        hidden = outputs.last_hidden_state.mean(dim=1)\n","        student_emb = projection(hidden)\n","\n","        student_norm = F.normalize(student_emb, p=2, dim=1)\n","        teacher_norm = F.normalize(labels.to(student_emb.device), p=2, dim=1)\n","\n","        mse_loss = F.mse_loss(student_norm, teacher_norm)\n","        cos_loss = (1 - F.cosine_similarity(student_norm, teacher_norm, dim=1)).mean()\n","\n","        total_loss = 0.25 * mse_loss + 0.75 * cos_loss   # Strong emphasis on direction\n","\n","        return (total_loss, outputs) if return_outputs else total_loss\n","\n","    def log(self, logs):\n","        super().log(logs)\n","        self.log_history.append(logs)\n","\n","training_args = TrainingArguments(\n","    output_dir=\"./qwen_family_distilled\",\n","    per_device_train_batch_size=8,\n","    num_train_epochs=40,\n","    learning_rate=2e-4,\n","    fp16=True,\n","    logging_steps=50,                    # Print loss every 50 steps as requested\n","    save_strategy=\"no\",\n","    report_to=\"none\",\n","    remove_unused_columns=False,\n",")\n","\n","trainer = DistillationTrainer(\n","    model=student_model,\n","    args=training_args,\n","    train_dataset=distill_dataset,\n","    data_collator=collate_fn,\n",")\n","\n","print(\"🚀 Starting training (loss will be printed every 50 steps)...\")\n","trainer.train()\n","\n","# ====================== 5. Save to Google Drive ======================\n","final_save_dir = \"/content/drive/MyDrive/distilled_qwen_encoder_for_flux\"\n","os.makedirs(final_save_dir, exist_ok=True)\n","\n","student_model.save_pretrained(final_save_dir)\n","student_tokenizer.save_pretrained(final_save_dir)\n","torch.save(projection.state_dict(), f\"{final_save_dir}/projection.pth\")\n","\n","print(f\"✅ Model + projection saved to Google Drive: {final_save_dir}\")\n","\n","# ====================== 6. Full Evaluation ======================\n","print(\"\\n🔄 Running full evaluation...\")\n","\n","student_model.eval()\n","student_embeddings = []\n","with torch.no_grad():\n","    for text in tqdm(texts, desc=\"Final encoding\"):\n","        inputs = student_tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors=\"pt\").to(student_model.device)\n","        outputs = student_model(**inputs)\n","        hidden = outputs.last_hidden_state.mean(dim=1)\n","        emb = projection(hidden)\n","        student_embeddings.append(emb.squeeze(0).cpu())\n","\n","student_embeddings = torch.stack(student_embeddings).to(student_model.device)\n","\n","# Metrics\n","mse = torch.nn.functional.mse_loss(student_embeddings, teacher_embeddings).item()\n","cos_sims = [cosine_similarity(student_embeddings[i].unsqueeze(0).cpu().numpy(),\n","                              teacher_embeddings[i].unsqueeze(0).cpu().numpy())[0][0]\n","            for i in range(len(texts))]\n","\n","avg_cosine = np.mean(cos_sims)\n","std_cosine = np.std(cos_sims)\n","\n","teacher_norms = torch.norm(teacher_embeddings, dim=1).cpu().numpy()\n","student_norms = torch.norm(student_embeddings, dim=1).cpu().numpy()\n","\n","print(f\"\\n📊 Final MSE: {mse:.4f}\")\n","print(f\"📊 Average Cosine Similarity: {avg_cosine:.4f} (± {std_cosine:.4f})\")\n","print(f\"Teacher norm: {teacher_norms.mean():.1f} | Student norm: {student_norms.mean():.1f}\")\n","\n","# PCA Plot\n","all_embs = torch.cat([teacher_embeddings.cpu(), student_embeddings.cpu()], dim=0).numpy()\n","pca = PCA(n_components=2, random_state=42)\n","pca_result = pca.fit_transform(all_embs)\n","teacher_pca = pca_result[:len(texts)]\n","student_pca = pca_result[len(texts):]\n","\n","plt.figure(figsize=(14, 10))\n","sns.set_style(\"whitegrid\")\n","plt.scatter(teacher_pca[:, 0], teacher_pca[:, 1], c='blue', label='Qwen Teacher', s=65, marker='o')\n","plt.scatter(student_pca[:, 0], student_pca[:, 1], c='red', label='Distilled Student', s=65, marker='x')\n","for i in range(len(texts)):\n","    plt.plot([teacher_pca[i, 0], student_pca[i, 0]], [teacher_pca[i, 1], student_pca[i, 1]], 'k--', alpha=0.35)\n","plt.title('Shared PCA Space: Teacher vs Student (dotted = same text)')\n","plt.legend()\n","plt.grid(True, alpha=0.3)\n","plt.tight_layout()\n","plt.show()\n","\n","print(\"\\n✅ Training + Evaluation completed. Model saved to Google Drive.\")"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[{"file_id":"1rORehICZ99xZsrwMfy2w8Jxg6M55d81L","timestamp":1774987488315}],"mount_file_id":"1rORehICZ99xZsrwMfy2w8Jxg6M55d81L","authorship_tag":"ABX9TyMAM97dT3JTq3Qq8kYlMeOa"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}