{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", "\n", "\n", " Join Discord if you need help + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your local device, follow [our guide](https://unsloth.ai/docs/get-started/install). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n", "\n", "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & how to save it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### News" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Introducing **Unsloth Studio** - a new open source, no-code web UI to train and run LLMs. [Blog](https://unsloth.ai/docs/new/studio) • [Notebook](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb)\n", "\n", "\n", "\n", "\n", "
\"Unsloth
Train models — no code needed		\"Unsloth
Run GGUF models on Mac, Windows & Linux
\n", "\n", "Train MoEs - DeepSeek, GLM, Qwen and gpt-oss 12x faster with 35% less VRAM. [Blog](https://unsloth.ai/docs/new/faster-moe)\n", "\n", "Ultra Long-Context Reinforcement Learning is here with 7x more context windows! [Blog](https://unsloth.ai/docs/new/grpo-long-context)\n", "\n", "New in Reinforcement Learning: [FP8 RL](https://unsloth.ai/docs/new/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/new/vision-reinforcement-learning-vlm-rl) • [Standby](https://unsloth.ai/docs/basics/memory-efficient-rl) • [gpt-oss RL](https://unsloth.ai/docs/new/gpt-oss-reinforcement-learning)\n", "\n", "Visit our docs for all our [model uploads](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Installation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "%%capture\nimport os, re\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n !pip install unsloth # Do this in local & cloud setups\nelse:\n import torch; v = re.match(r'[\\d]{1,}\\.[\\d]{1,}', str(torch.__version__)).group(0)\n xformers = 'xformers==' + {'2.10':'0.0.34','2.9':'0.0.33.post1','2.8':'0.0.32.post2'}.get(v, \"0.0.34\")\n !pip install sentencepiece protobuf \"datasets==4.3.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n !pip install --no-deps unsloth_zoo bitsandbytes accelerate {xformers} peft trl triton unsloth\n !pip install --no-deps --upgrade \"torchao>=0.16.0\"\n!pip install transformers==4.56.2\n!pip install --no-deps trl==0.22.2\n!pip install jiwer\n!pip install einops addict easydict" }, { "cell_type": "markdown", "metadata": { "id": "9xLDGk41C7IF" }, "source": [ "### Unsloth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's prepare the OCR model to our local first" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from huggingface_hub import snapshot_download\n", "snapshot_download(\"unsloth/DeepSeek-OCR\", local_dir = \"deepseek_ocr\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QmUBVEnvCDJv", "outputId": "d378c8c8-c9ec-4c0a-beee-d2433628d23b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "🦥 Unsloth Zoo will now patch everything to make training faster!" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using a model of type deepseek_vl_v2 to instantiate a model of type DeepseekOCR. This is not supported for all configurations of models and can yield errors." ] }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: WARNING `trust_remote_code` is True.\n", "Are you certain you want to do remote code execution?\n", "==((====))== Unsloth 2025.10.12: Fast Deepseekocr patching. Transformers: 4.56.2.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.8.0+cu126. CUDA: 7.5. CUDA Toolkit: 12.6. Triton: 3.4.0\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.32.post2. FA2 = False]\n", " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using a model of type deepseek_vl_v2 to instantiate a model of type DeepseekOCR. This is not supported for all configurations of models and can yield errors." ] }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA." ] }, { "name": "stderr", "output_type": "stream", "text": [ "Some weights of DeepseekOCRForCausalLM were not initialized from the model checkpoint at ./deepseek_ocr and are newly initialized: ['model.vision_model.embeddings.position_ids']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference." ] } ], "source": [ "from unsloth import FastVisionModel # FastLanguageModel for LLMs\n", "import torch\n", "from transformers import AutoModel\n", "import os\n", "os.environ[\"UNSLOTH_WARN_UNINITIALIZED\"] = '0'\n", "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", "fourbit_models = [\n", " \"unsloth/Qwen3-VL-8B-Instruct-bnb-4bit\", # Qwen 3 vision support\n", " \"unsloth/Qwen3-VL-8B-Thinking-bnb-4bit\",\n", " \"unsloth/Qwen3-VL-32B-Instruct-bnb-4bit\",\n", " \"unsloth/Qwen3-VL-32B-Thinking-bnb-4bit\",\n", "] # More models at https://huggingface.co/unsloth\n", "\n", "model, tokenizer = FastVisionModel.from_pretrained(\n", " \"./deepseek_ocr\",\n", " load_in_4bit = False, # Use 4bit to reduce memory use. False for 16bit LoRA.\n", " auto_model = AutoModel,\n", " trust_remote_code = True,\n", " unsloth_force_compile = True,\n", " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "JfZVh2ByxFNh" }, "source": [ "### Let's Evaluate Deepseek-OCR Baseline Performance on Persian Transcription" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 209, "referenced_widgets": [ "7ca58392065c404d8f17909085f87875", "3eaf210f8a434a36bff0ef7b61e53465", "7845466ec19946c5a076888a0f2341a5", "78a793bfcc2f4d22ac73e1d9396cc434", "98b5693678be4a15a377234298821458", "c3c060dc723d415daf54a4bc703dcea0", "6a6fc018480541a7aea9b6e137f8f942", "36eadf48405b4d60afdd702c6eabe976", "271457a1a6a2478880ef17c92139a4a1", "86a124143b0d44f8a19e0ed62921a15b", "c80007e89d09489f8e4bcf9b3a5e9541", "b6b6de1dcf8c427898db39203794a3a2", "77d756887b7d4c07bf898ac41f3e5433", "d41ae3498e51438f97d70db96e8c3765", "99c02e19ab94422ea427d9e2fc9123c8", "3088b111494643b6bea2575b95888ac7", "1d05bb6c47dc460381c2a6af5266d875", "faae37a536fb4037a323964dfa53a991", "bb6af5b3c5fc4bd1a249ecc33ce9687a", "99f65c3d847448b2947aef46e8a05b33", "43bcadc5c612457d8cfb8605889e27a5", "43d4e788c9e3416fbd70cb9f360d7887", "7c213b3ba59344c3a67d449b7fcf09fe", "a7e3f254d06844cd8cbdb91f986c87fd", "678ae2e0d9b644b79150918ccfb4da14", "a41a577cf40f466f9580347278875674", "6a5e1968f6eb4e9e925a39b6003b9e96", "734221f5c5aa473497b1a3345bbbc80a", "36910390b5da4091846ca5fcfbbf8a61", "2d2947e714644297833248cdbbac3216", "58feeb70efe34dab927a358d0d6ecc6d", "d799f109f2a84e0ca8aae25d1dbfa194", "8bc322a71765490d8bc40432329befff", "6fb18a43b7f742a285a8e6bd43e83599", "d12030f83a134743bace242e6be5fdaf", "61096862da17447b88364e9548243145", "66370977f9974e2b992ed2fd4babc288", "a84e8c06726b4ab196f4f27020d0f22f", "135c7d5ae19a46ee811002c6a139692c", "f2fc60fc5389457ab7dbd1c6a4ad4ea6", "03486ecc6fed4a7cb7e2e02dca0dee5d", "75e55e7d9e6b4e9f9c6bd2a84b811dc8", "ca4aa01c89e047a5b8f18b3137ef72f4", "1286473bfcdb4d5f96bc3154c26d72cc", "7ca49bafcc404cc2a549128cbf2da144", "6b4bc09dd88c4d0c9a52fb4b145d4819", "58a03bffbbae4cbda871c0600a5808aa", "246d3453758f4803b034aff39adaa8e4", "ffe8eb62369b47739676e2fe28b64ff3", "f4cdee9de6cb4bd1b3e6a314ebcc3299", "2d74cb6cfd63483d99cf994bbecf53ee", "6da5f6c3780e47df92d660dd8b241386", "b7608c238f1040b886d6670d6e3ea5ee", "acfbe50fd14240cab4e34052d6f006b9", "f83610de6c30471ab5d54786fe6236c7", "4a32cfd5a60a4c18b32f74805aed7f40", "31f339d93fb044599e575f7971bb890b", "8a6700fbe79d40ec8261a68822c8c48f", "c31f976abb564d769ea56ebad2dc3ee3", "f769ee16de144e22a1ed214621e303aa", "89828f4b1f4449e3b52b8951a22054a0", "0890359ee9574b19b1b52a4c740fad41", "f167376d3cbd428caade36eede87371c", "8e7293a253c54849b8cf7ab7407cd194", "33a933565ab44e2588724492fc084f55", "dec53c8d9a9f4648837725261e8b153f" ] }, "id": "LjY75GoYUCB8", "outputId": "ea986a55-eefd-42bd-a460-78e341bb6b40" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7ca58392065c404d8f17909085f87875", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/967 [00:00" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[1523]['image_path']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fpS8-2lHCEOJ", "outputId": "df6677bf-081b-4593-ab80-7644d6b8ace0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['', '\\nFree OCR.']" ] }, { "name": "stderr", "output_type": "stream", "text": [ "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results." ] }, { "name": "stdout", "output_type": "stream", "text": [ "انضباطم نندم حقيقتن باورم نميتند\n", "===============save results:===============" ] }, { "name": "stderr", "output_type": "stream", "text": [ "image: 0it [00:00, ?it/s]\n", "other: 0it [00:00, ?it/s]" ] } ], "source": [ "# prompt = \"\\nFree OCR. \"\n", "prompt = \"\\nFree OCR. \"\n", "image_file = 'your_image.jpg'\n", "output_path = 'your/output/dir'\n", "# infer(self, tokenizer, prompt = '', image_file = '', output_path = ' ', base_size = 1024, image_size = 640, crop_mode = True, test_compress = False, save_results = False):\n", "\n", "# Tiny: base_size = 512, image_size = 512, crop_mode = False\n", "# Small: base_size = 640, image_size = 640, crop_mode = False\n", "# Base: base_size = 1024, image_size = 1024, crop_mode = False\n", "# Large: base_size = 1280, image_size = 1280, crop_mode = False\n", "\n", "# Gundam: base_size = 1024, image_size = 640, crop_mode = True\n", "\n", "res = model.infer(tokenizer, prompt = prompt, image_file = image_file, output_path = output_path, base_size = 1024, image_size = 640, crop_mode = True, save_results = True, test_compress = False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "VTzhtzNRAEL1", "outputId": "a34e5cfc-3664-4b7e-c934-c715c031d179" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'انضباطمم شدم حقیقتن باورم نمیشد'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[1523][\"text\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "-6LYHC3F0Ihf" }, "source": [ "

Baseline Model Performance: 23% Character Error Rate (CER) for this sample !

" ] }, { "cell_type": "markdown", "metadata": { "id": "fzckMII_02s_" }, "source": [ "# Let's finetune Deepseek-OCR !" ] }, { "cell_type": "markdown", "metadata": { "id": "SXd9bTZd1aaL" }, "source": [ "We now add LoRA adapters for parameter efficient finetuning - this allows us to only efficiently train 1% of all parameters.\n", "\n", "**[NEW]** We also support finetuning ONLY the vision part of the model, or ONLY the language part. Or you can select both! You can also select to finetune the attention or the MLP layers!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6bZsfBuZDeCL", "outputId": "8510ca96-59ba-42fa-93ea-ed6df2058e0e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: Making `model.base_model.model.model` require gradients" ] } ], "source": [ "model = FastVisionModel.get_peft_model(\n", " model,\n", " target_modules = [\n", " \"q_proj\",\n", " \"k_proj\",\n", " \"v_proj\",\n", " \"o_proj\",\n", " \"gate_proj\",\n", " \"up_proj\",\n", " \"down_proj\",\n", " ],\n", "\n", " r = 16, # The larger, the higher the accuracy, but might overfit\n", " lora_alpha = 16, # Recommended alpha == r at least\n", " lora_dropout = 0,\n", " bias = \"none\",\n", " random_state = 3407,\n", " use_rslora = False, # We support rank stabilized LoRA\n", " loftq_config = None, # And LoftQ\n", " # target_modules = \"all-linear\", # Optional now! Can specify a list if needed\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "vITh0KVJ10qX" }, "source": [ "\n", "### Data Prep\n", "We'll be using a dataset for Persian OCR. The goal is to convert these images into a computer readable form - ie text. This can be very useful for digitizing Persian text.\n", "\n", "You can access the dataset [here](https://huggingface.co/datasets/hezarai/parsynth-ocr-200k)." ] }, { "cell_type": "markdown", "metadata": { "id": "K9CBpiISFa6C" }, "source": [ "To format the dataset, all vision finetuning tasks should be formatted as follows:\n", "\n", "```python\n", "[\n", "{ \"role\": \"<|User|>\",\n", " \"content\": \"\",\n", " \"images\": []\n", "},\n", "{ \"role\": \"<|Assistant|>\",\n", " \"content\": \"\"\n", "},\n", "]\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oPXzJZzHEgXe" }, "outputs": [], "source": [ "instruction = \"\\nFree OCR. \"\n", "\n", "def convert_to_conversation(sample):\n", " \"\"\"Convert dataset sample to conversation format\"\"\"\n", " conversation = [\n", " {\n", " \"role\": \"<|User|>\",\n", " \"content\": instruction,\n", " \"images\": [sample['image']]\n", " },\n", " {\n", " \"role\": \"<|Assistant|>\",\n", " \"content\": sample[\"text\"]\n", " },\n", " ]\n", " return {\"messages\": conversation}\n", "\n", "# Load dataset\n", "dataset = load_dataset(\"hezarai/parsynth-ocr-200k\", split = \"train[:1000]\")\n", "dataset = dataset.rename_column(\"image_path\", \"image\")" ] }, { "cell_type": "markdown", "metadata": { "id": "FY-9u-OD6_gE" }, "source": [ "Let's convert the dataset into the \"correct\" format for finetuning:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NLHM0PGn30x5" }, "outputs": [], "source": [ "converted_dataset = [convert_to_conversation(sample) for sample in dataset]" ] }, { "cell_type": "markdown", "metadata": { "id": "ndDUB23CGAC5" }, "source": [ "We look at how the conversations are structured for the first example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gGFzmplrEy9I", "outputId": "5f2051ba-af52-4000-8aa1-1bca2de0e3aa" }, "outputs": [ { "data": { "text/plain": [ "{'messages': [{'role': '<|User|>',\n", " 'content': '\\nFree OCR. ',\n", " 'images': []},\n", " {'role': '<|Assistant|>', 'content': 'همهاش جبره و اختیار توهمه'}]}" ], "text/html": [ "
{'messages': [{'role': '<|User|>',\n",
       "   'content': '<image>\\nFree OCR. ',\n",
       "   'images': [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=218x48>]},\n",
       "  {'role': '<|Assistant|>', 'content': 'همهاش جبره و اختیار توهمه'}]}
" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "converted_dataset[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "E2WR-p20LcG_" }, "outputs": [], "source": [ "# @title Create datacollator\n", "\n", "import torch\n", "import math\n", "from dataclasses import dataclass\n", "from typing import Dict, List, Any, Tuple\n", "from PIL import Image, ImageOps\n", "from torch.nn.utils.rnn import pad_sequence\n", "import io\n", "\n", "from deepseek_ocr.modeling_deepseekocr import (\n", " format_messages,\n", " text_encode,\n", " BasicImageTransform,\n", " dynamic_preprocess,\n", ")\n", "\n", "@dataclass\n", "class DeepSeekOCRDataCollator:\n", " \"\"\"\n", " Args:\n", " tokenizer: Tokenizer\n", " model: Model\n", " image_size: Size for image patches (default: 640)\n", " base_size: Size for global view (default: 1024)\n", " crop_mode: Whether to use dynamic cropping for large images\n", " train_on_responses_only: If True, only train on assistant responses (mask user prompts)\n", " \"\"\"\n", " tokenizer: Any\n", " model: Any\n", " image_size: int = 640\n", " base_size: int = 1024\n", " crop_mode: bool = True\n", " image_token_id: int = 128815\n", " train_on_responses_only: bool = True\n", "\n", " def __init__(\n", " self,\n", " tokenizer,\n", " model,\n", " image_size: int = 640,\n", " base_size: int = 1024,\n", " crop_mode: bool = True,\n", " train_on_responses_only: bool = True,\n", " ):\n", " self.tokenizer = tokenizer\n", " self.model = model\n", " self.image_size = image_size\n", " self.base_size = base_size\n", " self.crop_mode = crop_mode\n", " self.image_token_id = 128815\n", " self.dtype = model.dtype # Get dtype from model\n", " self.train_on_responses_only = train_on_responses_only\n", "\n", " self.image_transform = BasicImageTransform(\n", " mean = (0.5, 0.5, 0.5),\n", " std = (0.5, 0.5, 0.5),\n", " normalize = True\n", " )\n", " self.patch_size = 16\n", " self.downsample_ratio = 4\n", "\n", " # Get BOS token ID from tokenizer\n", " if hasattr(tokenizer, 'bos_token_id') and tokenizer.bos_token_id is not None:\n", " self.bos_id = tokenizer.bos_token_id\n", " else:\n", " self.bos_id = 0\n", " print(f\"Warning: tokenizer has no bos_token_id, using default: {self.bos_id}\")\n", "\n", " def deserialize_image(self, image_data) -> Image.Image:\n", " \"\"\"Convert image data (bytes dict or PIL Image) to PIL Image in RGB mode\"\"\"\n", " if isinstance(image_data, Image.Image):\n", " return image_data.convert(\"RGB\")\n", " elif isinstance(image_data, dict) and 'bytes' in image_data:\n", " image_bytes = image_data['bytes']\n", " image = Image.open(io.BytesIO(image_bytes))\n", " return image.convert(\"RGB\")\n", " else:\n", " raise ValueError(f\"Unsupported image format: {type(image_data)}\")\n", "\n", " def calculate_image_token_count(self, image: Image.Image, crop_ratio: Tuple[int, int]) -> int:\n", " \"\"\"Calculate the number of tokens this image will generate\"\"\"\n", " num_queries = math.ceil((self.image_size // self.patch_size) / self.downsample_ratio)\n", " num_queries_base = math.ceil((self.base_size // self.patch_size) / self.downsample_ratio)\n", "\n", " width_crop_num, height_crop_num = crop_ratio\n", "\n", " if self.crop_mode:\n", " img_tokens = num_queries_base * num_queries_base + 1\n", " if width_crop_num > 1 or height_crop_num > 1:\n", " img_tokens += (num_queries * width_crop_num + 1) * (num_queries * height_crop_num)\n", " else:\n", " img_tokens = num_queries * num_queries + 1\n", "\n", " return img_tokens\n", "\n", " def process_image(self, image: Image.Image) -> Tuple[List, List, List, List, Tuple[int, int]]:\n", " \"\"\"\n", " Process a single image based on crop_mode and size thresholds\n", "\n", " Returns:\n", " Tuple of (images_list, images_crop_list, images_spatial_crop, tokenized_image, crop_ratio)\n", " \"\"\"\n", " images_list = []\n", " images_crop_list = []\n", " images_spatial_crop = []\n", "\n", " if self.crop_mode:\n", " # Determine crop ratio based on image size\n", " if image.size[0] <= 640 and image.size[1] <= 640:\n", " crop_ratio = (1, 1)\n", " images_crop_raw = []\n", " else:\n", " images_crop_raw, crop_ratio = dynamic_preprocess(\n", " image, min_num = 2, max_num = 9,\n", " image_size = self.image_size, use_thumbnail = False\n", " )\n", "\n", " # Process global view with padding\n", " global_view = ImageOps.pad(\n", " image, (self.base_size, self.base_size),\n", " color = tuple(int(x * 255) for x in self.image_transform.mean)\n", " )\n", " images_list.append(self.image_transform(global_view).to(self.dtype))\n", "\n", " width_crop_num, height_crop_num = crop_ratio\n", " images_spatial_crop.append([width_crop_num, height_crop_num])\n", "\n", " # Process local views (crops) if applicable\n", " if width_crop_num > 1 or height_crop_num > 1:\n", " for crop_img in images_crop_raw:\n", " images_crop_list.append(\n", " self.image_transform(crop_img).to(self.dtype)\n", " )\n", "\n", " # Calculate image tokens\n", " num_queries = math.ceil((self.image_size // self.patch_size) / self.downsample_ratio)\n", " num_queries_base = math.ceil((self.base_size // self.patch_size) / self.downsample_ratio)\n", "\n", " tokenized_image = ([self.image_token_id] * num_queries_base + [self.image_token_id]) * num_queries_base\n", " tokenized_image += [self.image_token_id]\n", "\n", " if width_crop_num > 1 or height_crop_num > 1:\n", " tokenized_image += ([self.image_token_id] * (num_queries * width_crop_num) + [self.image_token_id]) * (\n", " num_queries * height_crop_num)\n", "\n", " else: # crop_mode = False\n", " crop_ratio = (1, 1)\n", " images_spatial_crop.append([1, 1])\n", "\n", " # For smaller base sizes, resize; for larger, pad\n", " if self.base_size <= 640:\n", " resized_image = image.resize((self.base_size, self.base_size), Image.LANCZOS)\n", " images_list.append(self.image_transform(resized_image).to(self.dtype))\n", " else:\n", " global_view = ImageOps.pad(\n", " image, (self.base_size, self.base_size),\n", " color = tuple(int(x * 255) for x in self.image_transform.mean)\n", " )\n", " images_list.append(self.image_transform(global_view).to(self.dtype))\n", "\n", " num_queries = math.ceil((self.base_size // self.patch_size) / self.downsample_ratio)\n", " tokenized_image = ([self.image_token_id] * num_queries + [self.image_token_id]) * num_queries\n", " tokenized_image += [self.image_token_id]\n", "\n", " return images_list, images_crop_list, images_spatial_crop, tokenized_image, crop_ratio\n", "\n", " def process_single_sample(self, messages: List[Dict]) -> Dict[str, Any]:\n", " \"\"\"\n", " Process a single conversation into model inputs.\n", " \"\"\"\n", "\n", " # --- 1. Setup ---\n", " images = []\n", " for message in messages:\n", " if \"images\" in message and message[\"images\"]:\n", " for img_data in message[\"images\"]:\n", " if img_data is not None:\n", " pil_image = self.deserialize_image(img_data)\n", " images.append(pil_image)\n", "\n", " if not images:\n", " raise ValueError(\"No images found in sample. Please ensure all samples contain images.\")\n", "\n", " tokenized_str = []\n", " images_seq_mask = []\n", " images_list, images_crop_list, images_spatial_crop = [], [], []\n", "\n", " prompt_token_count = -1 # Index to start training\n", " assistant_started = False\n", " image_idx = 0\n", "\n", " # Add BOS token at the very beginning\n", " tokenized_str.append(self.bos_id)\n", " images_seq_mask.append(False)\n", "\n", " for message in messages:\n", " role = message[\"role\"]\n", " content = message[\"content\"]\n", "\n", " # Check if this is the assistant's turn\n", " if role == \"<|Assistant|>\":\n", " if not assistant_started:\n", " # This is the split point. All tokens added *so far*\n", " # are part of the prompt.\n", " prompt_token_count = len(tokenized_str)\n", " assistant_started = True\n", "\n", " # Append the EOS token string to the *end* of assistant content\n", " content = f\"{content.strip()} {self.tokenizer.eos_token}\"\n", "\n", " # Split this message's content by the image token\n", " text_splits = content.split('')\n", "\n", " for i, text_sep in enumerate(text_splits):\n", " # Tokenize the text part\n", " tokenized_sep = text_encode(self.tokenizer, text_sep, bos = False, eos = False)\n", " tokenized_str.extend(tokenized_sep)\n", " images_seq_mask.extend([False] * len(tokenized_sep))\n", "\n", " # If this text is followed by an tag\n", " if i < len(text_splits) - 1:\n", " if image_idx >= len(images):\n", " raise ValueError(\n", " f\"Data mismatch: Found '' token but no corresponding image.\"\n", " )\n", "\n", " # Process the image\n", " image = images[image_idx]\n", " img_list, crop_list, spatial_crop, tok_img, _ = self.process_image(image)\n", "\n", " images_list.extend(img_list)\n", " images_crop_list.extend(crop_list)\n", " images_spatial_crop.extend(spatial_crop)\n", "\n", " # Add image placeholder tokens\n", " tokenized_str.extend(tok_img)\n", " images_seq_mask.extend([True] * len(tok_img))\n", "\n", " image_idx += 1 # Move to the next image\n", "\n", " # --- 3. Validation and Final Prep ---\n", " if image_idx != len(images):\n", " raise ValueError(\n", " f\"Data mismatch: Found {len(images)} images but only {image_idx} '' tokens were used.\"\n", " )\n", "\n", " # If we never found an assistant message, we're in a weird state\n", " # (e.g., user-only prompt). We mask everything.\n", " if not assistant_started:\n", " print(\"Warning: No assistant message found in sample. Masking all tokens.\")\n", " prompt_token_count = len(tokenized_str)\n", "\n", " # Prepare image tensors\n", " images_ori = torch.stack(images_list, dim = 0)\n", " images_spatial_crop_tensor = torch.tensor(images_spatial_crop, dtype = torch.long)\n", "\n", " if images_crop_list:\n", " images_crop = torch.stack(images_crop_list, dim = 0)\n", " else:\n", " images_crop = torch.zeros((1, 3, self.base_size, self.base_size), dtype = self.dtype)\n", "\n", " return {\n", " \"input_ids\": torch.tensor(tokenized_str, dtype = torch.long),\n", " \"images_seq_mask\": torch.tensor(images_seq_mask, dtype = torch.bool),\n", " \"images_ori\": images_ori,\n", " \"images_crop\": images_crop,\n", " \"images_spatial_crop\": images_spatial_crop_tensor,\n", " \"prompt_token_count\": prompt_token_count, # This is now accurate\n", " }\n", "\n", " def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]:\n", " \"\"\"Collate batch of samples\"\"\"\n", " batch_data = []\n", "\n", " # Process each sample\n", " for feature in features:\n", " try:\n", " processed = self.process_single_sample(feature['messages'])\n", " batch_data.append(processed)\n", " except Exception as e:\n", " print(f\"Error processing sample: {e}\")\n", " continue\n", "\n", " if not batch_data:\n", " raise ValueError(\"No valid samples in batch\")\n", "\n", " # Extract lists\n", " input_ids_list = [item['input_ids'] for item in batch_data]\n", " images_seq_mask_list = [item['images_seq_mask'] for item in batch_data]\n", " prompt_token_counts = [item['prompt_token_count'] for item in batch_data]\n", "\n", " # Pad sequences\n", " input_ids = pad_sequence(input_ids_list, batch_first = True, padding_value = self.tokenizer.pad_token_id)\n", " images_seq_mask = pad_sequence(images_seq_mask_list, batch_first = True, padding_value = False)\n", "\n", " # Create labels\n", " labels = input_ids.clone()\n", "\n", " # Mask padding tokens\n", " labels[labels == self.tokenizer.pad_token_id] = -100\n", "\n", " # Mask image tokens (model shouldn't predict these)\n", " labels[images_seq_mask] = -100\n", "\n", " # Mask user prompt tokens when train_on_responses_only = True (only train on assistant responses)\n", " if self.train_on_responses_only:\n", " for idx, prompt_count in enumerate(prompt_token_counts):\n", " if prompt_count > 0:\n", " labels[idx, :prompt_count] = -100\n", "\n", " # Create attention mask\n", " attention_mask = (input_ids != self.tokenizer.pad_token_id).long()\n", "\n", " # Prepare images batch (list of tuples)\n", " images_batch = []\n", " for item in batch_data:\n", " images_batch.append((item['images_crop'], item['images_ori']))\n", "\n", " # Stack spatial crop info\n", " images_spatial_crop = torch.cat([item['images_spatial_crop'] for item in batch_data], dim = 0)\n", "\n", " return {\n", " \"input_ids\": input_ids,\n", " \"attention_mask\": attention_mask,\n", " \"labels\": labels,\n", " \"images\": images_batch,\n", " \"images_seq_mask\": images_seq_mask,\n", " \"images_spatial_crop\": images_spatial_crop,\n", " }" ] }, { "cell_type": "markdown", "metadata": { "id": "idAEIeSQ3xdS" }, "source": [ "\n", "### Train the model\n", "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support `DPOTrainer` and `GRPOTrainer` for reinforcement learning!!\n", "\n", "We use our new `DeepSeekOCRDataCollator` which will help in our vision finetuning setup." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "95_Nn-89DhsL", "outputId": "fbf849bf-7476-4b43-8155-77aae75efc01" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipython-input-860910537.py:12: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", " trainer = Trainer(" ] } ], "source": [ "from transformers import Trainer, TrainingArguments\n", "from unsloth import is_bf16_supported\n", "FastVisionModel.for_training(model) # Enable for training!\n", "data_collator = DeepSeekOCRDataCollator(\n", " tokenizer = tokenizer,\n", " model = model,\n", " image_size = 640,\n", " base_size = 1024,\n", " crop_mode = True,\n", " train_on_responses_only = True,\n", ")\n", "trainer = Trainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", " data_collator = data_collator, # Must use!\n", " train_dataset = converted_dataset,\n", " args = TrainingArguments(\n", " per_device_train_batch_size = 2,\n", " gradient_accumulation_steps = 4,\n", " warmup_steps = 5,\n", " max_steps = 60,\n", " # num_train_epochs = 1, # Set this instead of max_steps for full training runs\n", " learning_rate = 2e-4,\n", " logging_steps = 1,\n", " optim = \"adamw_8bit\",\n", " weight_decay = 0.001,\n", " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " fp16 = not is_bf16_supported(), # Use fp16 if bf16 is not supported\n", " bf16 = is_bf16_supported(), # Use bf16 if supported\n", " output_dir = \"outputs\",\n", " report_to = \"none\", # For Weights and Biases\n", " dataloader_num_workers = 2,\n", " # You MUST put the below items for vision finetuning:\n", " remove_unused_columns = False,\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "2ejIt2xSNKKp", "outputId": "88c63124-2589-457c-b6da-31d66aa85e34" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GPU = NVIDIA L4. Max memory = 22.161 GB.\n", "8.012 GB of memory reserved.\n" ] } ], "source": [ "# @title Show current memory stats\n", "gpu_stats = torch.cuda.get_device_properties(0)\n", "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n", "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n", "print(f\"{start_gpu_memory} GB of memory reserved.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yqxqAZ7KJ4oL", "outputId": "cbfafb64-473f-4521-c4af-aa6c643e0b80" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", " \\\\ /| Num examples = 1,000 | Num Epochs = 1 | Total steps = 60\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 = 77,509,632 of 3,413,615,872 (2.27% trained)\n", "Unsloth: Not an error, but DeepseekOCRForCausalLM does not accept `num_items_in_batch`.\n", "Using gradient accumulation will be very slightly less accurate.\n", "Read more on gradient accumulation issues here: https://unsloth.ai/blog/gradient\n" ] }, { "data": { "text/html": [ "\n", "
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StepTraining Loss
14.058900
22.653800
33.428300
43.517200
51.961600
63.920800
72.114100
81.884600
93.055200
103.027200
112.489200
121.692100
133.049300
141.596900
151.868300
160.982600
171.819700
181.407600
191.521300
202.194300
213.228300
221.225100
231.720800
240.786600
252.063200
262.585800
271.849600
280.870200
291.535300
301.978900
311.027100
320.770800
332.470500
341.413300
351.479100
361.590600
371.273200
381.532700
391.641300
402.088300
411.600900
421.941200
431.341600
441.807500
451.677300
461.253100
472.245600
481.665600
491.171300
501.961400
511.512700
521.388500
531.443300
542.158200
550.998800
561.127800
572.540400
581.561800
590.869400
601.366500

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: Will smartly offload gradients to save VRAM!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using a model of type deepseek_vl_v2 to instantiate a model of type DeepseekOCR. This is not supported for all configurations of models and can yield errors.\n" ] } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "pCqnaKmlO1U9" }, "outputs": [], "source": [ "# @title Show final memory and time stats\n", "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", "used_percentage = round(used_memory / max_memory * 100, 3)\n", "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", "print(\n", " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", ")\n", "print(f\"Peak reserved memory = {used_memory} GB.\")\n", "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ekOmTR1hSNcr" }, "source": [ "\n", "### Inference\n", "Let's run the model!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "b9939f3d-2d63-432a-a782-5b8619bf5254" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['', '\\nFree OCR.']\n", "انضباطم نشدم حقیقتن باورم نمیشد\n", "===============save results:===============" ] }, { "name": "stderr", "output_type": "stream", "text": [ "image: 0it [00:00, ?it/s]\n", "other: 0it [00:00, ?it/s]" ] } ], "source": [ "prompt = \"\\nFree OCR. \"\n", "image_file = 'your_image.jpg'\n", "output_path = 'your/output/dir'\n", "\n", "# Tiny: base_size = 512, image_size = 512, crop_mode = False\n", "# Small: base_size = 640, image_size = 640, crop_mode = False\n", "# Base: base_size = 1024, image_size = 1024, crop_mode = False\n", "# Large: base_size = 1280, image_size = 1280, crop_mode = False\n", "\n", "# Gundam: base_size = 1024, image_size = 640, crop_mode = True\n", "\n", "res = model.infer(tokenizer, prompt = prompt, image_file = image_file,\n", " output_path = output_path,\n", " image_size = 640,\n", " base_size = 1024,\n", " crop_mode = True,\n", " save_results = True,\n", " test_compress = False)" ] }, { "cell_type": "markdown", "metadata": { "id": "yd30-Yg3fEeK" }, "source": [ "With only 60 steps, we dramatically improved the transcription quality. The Character Error Rate (CER) on this single sample dropped from 23% to 6%, a 74% relative reduction!\n", "\n", "| Type | OCR |\n", "| :--- | :--- |\n", "| **Baseline (Pre-Finetune)** | `انضباطم نندم حقيقتن باورم نميتند` |\n", "| **Finetuned (60 steps)** | `انضباطم نشدم حقیقتن باورم نمیشد` |\n", "| **Ground Truth** | `انضباطمم شدم حقیقتن باورم نمیشد` |" ] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use Hugging Face's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", "\n", "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "upcOlWe7A1vc", "outputId": "00ea7366-5427-4301-cf04-e883cf5170ea" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "You are using a model of type deepseek_vl_v2 to instantiate a model of type DeepseekOCR. This is not supported for all configurations of models and can yield errors." ] }, { "data": { "text/plain": [ "('lora_model/tokenizer_config.json',\n", " 'lora_model/special_tokens_map.json',\n", " 'lora_model/tokenizer.json')" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.save_pretrained(\"deepseek_ocr_lora\") # Local saving\n", "tokenizer.save_pretrained(\"deepseek_ocr_lora\")\n", "# model.push_to_hub(\"your_name/deepseek_ocr_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n", "# tokenizer.push_to_hub(\"your_name/deepseek_ocr_lora\", token = \"YOUR_HF_TOKEN\") # Online saving" ] }, { "cell_type": "markdown", "metadata": { "id": "AEEcJ4qfC7Lp" }, "source": [ "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MKX_XKs_BNZR" }, "outputs": [], "source": [ "if False:\n", " from unsloth import FastVisionModel\n", " model, tokenizer = FastVisionModel.from_pretrained(\n", " model_name = \"deepseek_ocr_lora\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = False, # Use 4bit to reduce memory use. False for 16bit LoRA.\n", " auto_model = AutoModel,\n", " trust_remote_code = True,\n", " unsloth_force_compile = True,\n", " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\n", " )\n", " FastVisionModel.for_inference(model) # Enable for inference!\n", "\n", "prompt = \"\\nFree OCR. \"\n", "image_file = 'your_image.jpg'\n", "output_path = 'your/output/dir'\n", "\n", "# Tiny: base_size = 512, image_size = 512, crop_mode = False\n", "# Small: base_size = 640, image_size = 640, crop_mode = False\n", "# Base: base_size = 1024, image_size = 1024, crop_mode = False\n", "# Large: base_size = 1280, image_size = 1280, crop_mode = False\n", "\n", "# Gundam: base_size = 1024, image_size = 640, crop_mode = True\n", "\n", "res = model.infer(tokenizer, prompt = prompt, image_file = image_file,\n", " output_path = output_path,\n", " image_size = 640,\n", " base_size = 1024,\n", " crop_mode = True,\n", " save_results = True,\n", " test_compress = False)" ] }, { "cell_type": "markdown", "metadata": { "id": "f422JgM9sdVT" }, "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens. See [our docs](https://unsloth.ai/docs/basics/inference-and-deployment) for more deployment options." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iHjt_SMYsd3P" }, "outputs": [], "source": [ "# Select ONLY 1 to save! (Both not needed!)\n", "\n", "# Save locally to 16bit\n", "if False: model.save_pretrained_merged(\"unsloth_finetune\", tokenizer,)\n", "\n", "# To export and save to your Hugging Face account\n", "if False: model.push_to_hub_merged(\"YOUR_USERNAME/unsloth_finetune\", tokenizer, token = \"YOUR_HF_TOKEN\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", "\n", "Some other resources:\n", "1. Looking to use Unsloth locally? Read our [Installation Guide](https://unsloth.ai/docs/get-started/install) for details on installing Unsloth on Windows, Docker, AMD, Intel GPUs.\n", "2. Learn how to do Reinforcement Learning with our [RL Guide and notebooks](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide).\n", "3. Read our guides and notebooks for [Text-to-speech (TTS)](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning) and [vision](https://unsloth.ai/docs/basics/vision-fine-tuning) model support.\n", "4. Explore our [LLM Tutorials Directory](https://unsloth.ai/docs/models/tutorials-how-to-fine-tune-and-run-llms) to find dedicated guides for each model.\n", "5. Need help with Inference? Read our [Inference & Deployment page](https://unsloth.ai/docs/basics/inference-and-deployment) for details on using vLLM, llama.cpp, Ollama etc.\n", "\n", "

\n", " \n", " \n", " \n", "\n", " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", "\n", " This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme)\n", "
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