{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "Mhmx5Sk2WZHq", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 22205, "status": "ok", "timestamp": 1779698709464, "user": { "displayName": "Duc Tran", "userId": "09762564332411743917" }, "user_tz": -420 }, "id": "Mhmx5Sk2WZHq", "outputId": "9fed73c5-4772-4758-ff3f-4f3f677e6f7b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')\n" ] }, { "cell_type": "code", "execution_count": null, "id": "28f30c07", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "dcea2ec9f9504ed58fd414cca128fbef", "4d1a2de563974157ab9d07792d96bec8", "305f175674784f58ad7aebefb3e246cf", "01b5c5f03a2140a79417fd5c4a682b06", "84df2abc06ae47bb8244155db2eb8555", "ef05980453274dbb93e4dbe4cd3e62a8", 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"elapsed": 832277, "status": "ok", "timestamp": 1779699552361, "user": { "displayName": "Duc Tran", "userId": "09762564332411743917" }, "user_tz": -420 }, "id": "28f30c07", "outputId": "86c94c5a-c7b9-4a8c-ea7d-574de617e867", "vscode": { "languageId": "plaintext" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting unsloth@ git+https://github.com/unslothai/unsloth.git (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n", " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-install-sc8tzals/unsloth_932114af876647199dfab7dfcdba72ab\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", "Collecting unsloth_zoo>=2026.5.4 (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n", "Successfully built unsloth\n", "Successfully installed bitsandbytes-0.49.2 cut_cross_entropy-25.1.1 datasets-4.3.0 hf_transfer-0.1.9 msgspec-0.21.1 pyarrow-24.0.0 torchao-0.17.0 transformers-5.5.0 trl-0.24.0 tyro-1.0.13 unsloth-2026.5.7 unsloth_zoo-2026.5.4\n", "... (pip install logs cleaned for brevity) ...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Before training:\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Both `max_new_tokens` (=128) and `max_length`(=32768) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MYK-8008<|im_end|>\n", "Unsloth: Model does not have a default image size - using 512\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'eos_token_id': 151645, 'bos_token_id': None}.\n", "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", " \\\\ /| Num examples = 1,812 | Num Epochs = 1 | Total steps = 50\n", "O^O/ \\_/ \\ Batch size per device = 2 | Gradient accumulation steps = 4\n", "\\ / Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8\n", " \"-____-\" Trainable parameters = 28,950,528 of 2,237,936,128 (1.29% trained)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: Will smartly offload gradients to save VRAM!\n", "Unsloth: Double buffering enabled (parallel H2D + compute) for backward pass.\n" ] }, { "data": { "text/html": [ "\n", "
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StepTraining Loss
12.578605
22.568652
32.553040
42.480093
52.338987
62.153503
71.917072
81.646196
91.407159
101.189265
110.972252
120.749311
130.549083
140.414225
150.281465
160.195539
170.133323
180.102891
190.084021
200.076507
210.073243
220.068067
230.066050
240.065049
250.062854
260.067751
270.061701
280.060466
290.060731
300.060296
310.057146
320.057706
330.059455
340.053612
350.057314
360.054666
370.049735
380.047805
390.046404
400.052502
410.045322
420.043781
430.038057
440.034674
450.031219
460.029168
470.026033
480.023063
490.032844
500.022346

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Unsloth: Restored added_tokens_decoder metadata in outputs/checkpoint-50/tokenizer_config.json.\n", "Both `max_new_tokens` (=128) and `max_length`(=32768) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "After training:\n", "MYK-8008<|im_end|>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Unsloth: Restored added_tokens_decoder metadata in Models/unsloth_finetune/tokenizer_config.json.\n" ] }, { "data": { "text/plain": [ "['Models/unsloth_finetune/processor_config.json']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", "\n", "from unsloth import FastVisionModel\n", "from datasets import load_dataset\n", "from unsloth.trainer import UnslothVisionDataCollator\n", "from trl import SFTTrainer, SFTConfig\n", "from transformers import TextStreamer\n", "\n", "MODEL_NAME = \"unsloth/Qwen2-VL-2B-Instruct-bnb-4bit\"\n", "OUTPUT_DIR = \"outputs\"\n", "SEED = 3407\n", "MAX_STEPS = 50\n", "\n", "INSTRUCTION = \"\"\"\n", "You are a world-class OCR expert specializing in recognizing all types of vehicle license plates\n", "(cars, motorbikes, trucks, etc.) in any weather or lighting condition, including blurred, dirty,\n", "or low-contrast images. Your recognition must be precise and avoid any confusion between\n", "similar-looking characters (e.g., '0' and 'O', '1' and 'I', '8' and 'B').\n", "\n", "Analyze the given image, which may contain one or multiple license plates.\n", "For each license plate detected, extract and return ONLY its exact content,\n", "using only the following valid characters: digits (0-9), uppercase letters (A-Z),\n", "the hyphen (-), and the dot (.).\n", "\n", "List each license plate you find on a separate line, with no extra words,\n", "symbols, or explanations.\n", "\"\"\"\n", "\n", "model, tokenizer = FastVisionModel.from_pretrained(\n", " MODEL_NAME,\n", " load_in_4bit=True,\n", " use_gradient_checkpointing=\"unsloth\"\n", ")\n", "\n", "model = FastVisionModel.get_peft_model(\n", " model,\n", " finetune_vision_layers=True,\n", " finetune_language_layers=True,\n", " finetune_attention_modules=True,\n", " finetune_mlp_modules=True,\n", " r=16,\n", " lora_alpha=16,\n", " lora_dropout=0,\n", " bias=\"none\",\n", " random_state=SEED,\n", ")\n", "\n", "dataset = load_dataset(\"EZCon/taiwan-license-plate-recognition\", split=\"train\")\n", "dataset = dataset.remove_columns([\"xywhr\", \"is_electric_car\"])\n", "dataset = dataset.rename_column(\"license_number\", \"text\")\n", "\n", "def convert_to_conversation(sample):\n", " return {\n", " \"messages\": [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"text\", \"text\": INSTRUCTION},\n", " {\"type\": \"image\", \"image\": sample[\"image\"]}\n", " ]\n", " },\n", " {\n", " \"role\": \"assistant\",\n", " \"content\": [\n", " {\"type\": \"text\", \"text\": sample[\"text\"]}\n", " ]\n", " }\n", " ]\n", " }\n", "\n", "converted_dataset = [convert_to_conversation(sample) for sample in dataset]\n", "\n", "FastVisionModel.for_inference(model)\n", "\n", "test_image = dataset[2][\"image\"]\n", "messages = [\n", " {\"role\": \"user\", \"content\": [\n", " {\"type\": \"image\", \"image\": test_image},\n", " {\"type\": \"text\", \"text\": INSTRUCTION}\n", " ]}\n", "]\n", "\n", "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n", "inputs = tokenizer(test_image, input_text, add_special_tokens=False, return_tensors=\"pt\").to(\"cuda\")\n", "\n", "print(\"\\nBefore training:\")\n", "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", "result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.5, min_p = 0.1)\n", "\n", "FastVisionModel.for_training(model)\n", "\n", "trainer = SFTTrainer(\n", " model=model,\n", " tokenizer=tokenizer,\n", " data_collator=UnslothVisionDataCollator(model, tokenizer),\n", " train_dataset=converted_dataset,\n", " args=SFTConfig(\n", " per_device_train_batch_size=2,\n", " gradient_accumulation_steps=4,\n", " warmup_steps=5,\n", " max_steps=MAX_STEPS,\n", " learning_rate=2e-4,\n", " logging_steps=1,\n", " optim=\"adamw_8bit\",\n", " weight_decay=0.01,\n", " lr_scheduler_type=\"linear\",\n", " seed=SEED,\n", " output_dir=OUTPUT_DIR,\n", " report_to=\"none\",\n", " remove_unused_columns=False,\n", " dataset_text_field=\"\",\n", " dataset_kwargs={\"skip_prepare_dataset\": True},\n", " max_length=2048,\n", " ),\n", ")\n", "\n", "trainer_stats = trainer.train()\n", "\n", "FastVisionModel.for_inference(model)\n", "\n", "print(\"\\nAfter training:\")\n", "result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.5, min_p = 0.1)\n", "\n", "model.save_pretrained(\"Models/unsloth_finetune\")\n", "tokenizer.save_pretrained(\"Models/unsloth_finetune\")\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "Wluh1LLdai7l", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 1050, "status": "ok", "timestamp": 1779699676101, "user": { "displayName": "Duc Tran", "userId": "09762564332411743917" }, "user_tz": -420 }, "id": "Wluh1LLdai7l", "outputId": "708ca54f-280d-4d91-b723-ea0047089c3c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mv: inter-device move failed: '/content/Models/unsloth_finetune' to '/content/drive/MyDrive/VLM-FineTuned/Models/unsloth_finetune'; unable to remove target: Directory not empty\n" ] } ], "source": [ "!mv /content/Models/unsloth_finetune /content/drive/MyDrive/VLM-FineTuned/Models/\n" ] }, { "cell_type": "code", "execution_count": null, "id": "i8tYN1IZRcXF", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "i8tYN1IZRcXF", "outputId": "5ec18399-e45e-4a61-d80a-7b1e4eb5a5cd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to false.\n", "2026-05-25 09:25:16.555 Uvicorn server started on 0.0.0.0:8501\n", "Warning: Permanently added 'localhost.run' (RSA) to the list of known hosts.\n", "\n", "===============================================================================\n", "Welcome to localhost.run!\n", "\n", "Follow your favourite reverse tunnel at [https://twitter.com/localhost_run].\n", "\n", "To set up and manage custom domains go to https://admin.localhost.run/\n", "\n", "More details on custom domains (and how to enable subdomains of your custom\n", "domain) at https://localhost.run/docs/custom-domains\n", "\n", "If you get a permission denied error check the faq for how to connect with a key or\n", "create a free tunnel without a key at [http://localhost:3000/docs/faq#generating-an-ssh-key].\n", "\n", "To explore using localhost.run visit the documentation site:\n", "https://localhost.run/docs/\n", "\n", "===============================================================================\n", "\n", "** your connection id is 7c19d8e9-bb93-4036-91ea-1911a149c69e, please mention it if you send me a message about an issue. **\n", "\n", "authenticated as anonymous user\n", "a852f959d06688.lhr.life tunneled with tls termination, https://a852f959d06688.lhr.life\n", "create an account and add your key for a longer lasting domain name. see https://localhost.run/docs/forever-free/ for more information.\n", "\n... [Streamlit is running and processing video streams] ...\n\n" ] } ], "source": [ "!streamlit run main.py & sleep 5 && ssh -o StrictHostKeyChecking=no -R 80:localhost:8501 nokey@localhost.run\n" ] }, { "cell_type": "code", "execution_count": null, "id": "gmh-QSrmYW_7", "metadata": { "id": "gmh-QSrmYW_7" }, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 5 }