{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "_JvlvnHxAVOn" }, "outputs": [], "source": [ "#Install required libraries\n", "!pip install unsloth\n", "!pip install --upgrade transformers peft accelerate bitsandbytes trl datasets\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "A78ek9O3Agzc", "outputId": "b2b5ab78-a16b-4109-e868-7f9a167e8be8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using device: Tesla T4\n" ] } ], "source": [ "# STEP 2: Import core libraries and detect the available GPU\n", "# This notebook is designed to run on Google Colab with a free T4 GPU.\n", "# The Tesla T4 has 16 GB VRAM β€” enough for Qwen3-8B in 4-bit quantization (~5 GB footprint).\n", "# If no GPU is found: Runtime > Change runtime type > T4 GPU.\n", "import os\n", "import json\n", "import torch\n", "import pandas as pd\n", "\n", "# Auto-detect GPU and print device name for verification\n", "if torch.cuda.is_available():\n", " DEVICE = torch.device(\"cuda\")\n", " print(f\"Using device: {torch.cuda.get_device_name(0)}\")\n", "else:\n", " DEVICE = torch.device(\"cpu\")\n", " print(\"No GPU found β€” check Runtime > Change runtime type > T4 GPU\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "5191357fa89e4abdad54c926bc24ede2", "54be084e90b54763b183db13bed0f636", "dec835663a9144438ebae8825737d2c2", "9f9bff050c5f4970a0ae3154ac254452", "b9d3cb2090ae4f029e23235bc443ee14", "569b2559af914597addc585b25e0966b", "ece9482b0b7d479ea88bc7edfffd8d0a", "3e18334861a24b418e7d7ae98832ddbf", "5382c7be72634f788890e950b9b8edd0", "8aaac5b9f77b4ab293dcace0cdd6a3b4", "20585eee6f1242629765c889bfbfbaee", "3a16888eec17477ea31e2c9b1658c69d", "6848d0d7f0b04f86ab2a9eebf253f097", "56e083059f45429e83d393e74648168a", "f8624677f4e94bd48126875cf6a51653", "d217892a9511479289ea7db22824545c", "b05bc7d064dc4b1fba8826f47dc44c1d", "afe0f553aaed43cc984f76cbc4873a2c", "1c94441279c640c1b9eee35b46f9da09", "d1213a20dade4fbb91a822eab2162e9b", "6f5706cfe9c648b3b8f7a7c262f28fcd", "c49a007989aa49ba840fc84909ef3924", "96ac4a4a043c485c93410da3268becbb", "8d8bb2dbde7d455bbf0f64565c7ca666", "e18a7b27d0f64ea0a213c8d2a7a961c0", "b5614bbccf134cb884adaabc6a85749b", "89e5a71dc7a04ba7949fecf30e5ab5c3", "be6e34aa567242cc8177b7130f6b771c", "b4f8f70ac33f407bb20fef46ca2978c2", "a138a39ec6ee4defbfb8f97e0d35ee8b", "e995ff6a5f4a4ff488dc8c9e229efb47", "5eb6a6dd99d4480faa23d9c6a55b6cbc", "d86796ea3d64487d8c0d7880ca8ffbfa" ] }, "id": "BbNhsYMSAhnm", "outputId": "64230ca5-0746-46bd-f7a5-73970339ee59" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==((====))== Unsloth 2026.3.3: Fast Qwen3 patching. Transformers: 5.2.0.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n", " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5191357fa89e4abdad54c926bc24ede2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading weights: 0%| | 0/399 [00:00.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3a16888eec17477ea31e2c9b1658c69d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/2000 [00:00\n", " \n", " \n", " [375/375 1:44:42, Epoch 3/3]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining Loss
102.013895
201.220427
300.926248
400.932706
500.910752
600.903756
700.888169
800.879890
900.880934
1000.924172
1100.867776
1200.866400
1300.864028
1400.847386
1500.857944
1600.830444
1700.869034
1800.864297
1900.856001
2000.870350
2100.828398
2200.839814
2300.867916
2400.869452
2500.862546
2600.776549
2700.823319
2800.828021
2900.830391
3000.815361
3100.783150
3200.808406
3300.766937
3400.798933
3500.781558
3600.800343
3700.803681

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Training complete! Loss: 0.8899\n", "Training time: 6310s\n" ] } ], "source": [ "# Load Qwen3-8B in 4-bit quantization, inject QLoRA adapters, and train\n", "\n", "from unsloth import FastLanguageModel\n", "from trl import SFTTrainer, SFTConfig\n", "from datasets import Dataset\n", "import json, torch\n", "\n", "# Base model: Qwen3-8B pre-quantized to 4-bit by Unsloth (faster download, less VRAM)\n", "BASE_MODEL = \"unsloth/Qwen3-8B-bnb-4bit\"\n", "OUTPUT_DIR = \"qlora_green_patent_adapter\" # destination for the LoRA adapter weights\n", "MAX_SEQ_LENGTH = 2048 # maximum token length per training example\n", "\n", "# Load the 4-bit quantized base model and its tokenizer\n", "# dtype=None lets Unsloth auto-select fp16 or bf16 based on GPU capability\n", "model_qlora, tokenizer_qlora = FastLanguageModel.from_pretrained(\n", " model_name=BASE_MODEL,\n", " max_seq_length=MAX_SEQ_LENGTH,\n", " dtype=None,\n", " load_in_4bit=True, # QLoRA requires the base model to be 4-bit quantized\n", ")\n", "\n", "model_qlora = FastLanguageModel.get_peft_model(\n", " model_qlora,\n", " r=16,\n", " target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", " \"gate_proj\", \"up_proj\", \"down_proj\"],\n", " lora_alpha=16,\n", " lora_dropout=0,\n", " bias=\"none\",\n", " use_gradient_checkpointing=\"unsloth\",\n", " random_state=42,\n", ")\n", "ALPACA_TEMPLATE = (\n", " \"Below is an instruction that describes a task, paired with an input that provides further context. \"\n", " \"Write a response that appropriately completes the request.\\n\\n\"\n", " \"### Instruction:\\n{instruction}\\n\\n\"\n", " \"### Input:\\n{input}\\n\\n\"\n", " \"### Response:\\n{output}\"\n", ")\n", "\n", "# EOS token is appended after each response so the model learns when to stop generating\n", "EOS_TOKEN = tokenizer_qlora.eos_token\n", "\n", "def format_alpaca(example):\n", " # Converts a raw JSON training record into a complete Alpaca prompt string\n", " return {\n", " \"text\": ALPACA_TEMPLATE.format(\n", " instruction=example[\"instruction\"],\n", " input=example[\"input\"],\n", " output=example[\"output\"]\n", " ) + EOS_TOKEN\n", " }\n", "\n", "\n", "with open(\"qlora_train_data.json\") as f:\n", " raw_data = json.load(f)\n", "\n", "dataset_qlora = Dataset.from_list(raw_data).map(format_alpaca)\n", "print(f\"Training dataset: {len(dataset_qlora)} examples\")\n", "\n", "trainer = SFTTrainer(\n", " model=model_qlora,\n", " tokenizer=tokenizer_qlora,\n", " train_dataset=dataset_qlora,\n", " args=SFTConfig(\n", " output_dir=OUTPUT_DIR,\n", " dataset_text_field=\"text\",\n", " max_seq_length=MAX_SEQ_LENGTH,\n", " dataset_num_proc=2,\n", " packing=False,\n", " per_device_train_batch_size=4,\n", " gradient_accumulation_steps=4,\n", " warmup_steps=10,\n", " num_train_epochs=3,\n", " learning_rate=2e-4,\n", " fp16=not torch.cuda.is_bf16_supported(),\n", " bf16=torch.cuda.is_bf16_supported(),\n", " logging_steps=10,\n", " optim=\"adamw_8bit\",\n", " weight_decay=0.01,\n", " lr_scheduler_type=\"linear\",\n", " seed=42,\n", " save_strategy=\"epoch\",\n", " ),\n", ")\n", "\n", "trainer_stats = trainer.train()\n", "print(f\"Training complete! Loss: {trainer_stats.training_loss:.4f}\")\n", "print(f\"Training time: {trainer_stats.metrics['train_runtime']:.0f}s\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "0f38688c7cd5454fbae4b9c32b19f0de", "ca8bedde997048e19b8f238477468be0", "d76446d828624519a545cf534ad4392e", "d1719166919143f29a4a185743355de3", "d1c96d0a67a140cfa3268c5909156bcd", "72e43644b2a84490a142f6503f092de7", "e0a9d1a3e5c04dd99c1c82893ee5814e", "95e07025faf242128593e6508c524e69", "30ea37677c9c4809a454f4e8ef46ccd7", "6ba4b2333bf647559d9da88980dd2a16", "bfb12efcca12435295dd8bfc0d649405", "6be3e71d5eb5417bb3d77ee13d680786", "10d00b311e3f4fd9a7fa9fd4719bf132", "6c1b1b9e13c14ec191163eb05cab50e0", "f95bbccb1117462898c8f4c2d53d1fb7", "04fd6a99c2df44c48f6f5469c8067396", "2f1adfbe051d4c42873cea220c34c783", "d28ed8d130b3447eb220bbb2e6e36312", "8907f281434e4a0c9a1ad8687e065e7b", "d34a4cb8ea2148be852ade0db075951d", "f4e11b7ade9847bdb68e9e31ca3c7fb2", "e715d19897df4e3c959e632ad24a1007", "c07386c8ffe94350bca85afed3cf68f1", "cd43ed38a2a949afa04e05b9abc5a474", "18c5bfefc8cf467c965864a950352a2a", "317775d02ce94b02991dd7f9cad22e5e", "3bb9ac7d5fdd431783c7009123481f11", "c4454289993443b18aa02871309ae988", "fa29b349356a456bb8db59b725b31687", "cf34433b4cff4a7486cd47241553ddf4", "436f1d35838b411a9ec5f2e97bf1e87f", "794400d81fc4410ab4ab882abab7e228", "e73b2cd46fe6415993897dd10cd2a1f1", "583abba435b94e4cb9bcd8860abf0f42", "f35d0ebfdcc1425189320d1b42c29131", "9a7c1bc4b28846a5af3403404348a0fe", "3c52f6267ae04710975326dfc301cfc5", "635889f759fd407492bcc12a85f7355f", "6e5ee4875885461ea320d1ccd8843d7a", "7a95edfb94604c7aa716336bf2906787", "7ff71121f75f4bb4ae94ec7a2f2ef927", "4d220799f502446eb31a8f070b48201d", "819984bfb892481ab68d32a6a210b178", "4e5e48b33dcf40abb732793b7488a04d", "38f8de0c3b1549c9a63d57944a59ce69", "67e1a9b16a05466abe580c2b585abcb3", "7284421bdeca406d93835a69eb71600b", "8860a9ed439b421999edadf3d03b6445", "c32cc68a5bcd456b8af2cc970de3afd6", "6efdbeb5caee4427921df323c1b5c5c3", "30c5c498ec084a79af0e23e140d07d0a", "5a0d8d6ded764edeba5c543274c13da8", "e22879b2760542998c1e7d34791eb75e", "b12784b2cded48bbb79b78b8bdc9819e", "b9b59b4f9b74455d96eb72c289741ec8", "cb0e8e34e27e47478cd26461e4d65812", "674efbc6715e4a74beacc194a24b4836", "7494b762d5ac47079f65999ac7272d42", "08f2f95b9bf04caab7ac1c0305e6a871", "99a1ec54926542848820a50706ca15d3", "2a1c3a69c6de46ab97e95bfbd5394c00", "e9757c4480274e6bbb0eb8e00daa37c5", "fc2501890f9141f2836f1558ee54cd53", "985ebe6281b440cdaf4abdc16897e29a", "6cbebc3d1f3d46f4b3d27ad85a259a3d", "5ae7b41a9f184a3ea52bb89768f713ca", "87a3ca83db6643cea9c3dd82908bccd9", "cd1ebc26f5e5498f9114e10f44c6a84d", "369244e29d084452a54a8ef6e31a94d2", "aaa579aac80045fb91dc19a3aa9fe79f", "5a3279c4063c4895bafa77ca0ee31511", "2809b61a97b54c42a3d3cc3fc3e48b9c", "370431d5960844908d65e71590a4226f", "b87e3bdfd1774a8fb233b7b9b063a13d", "7ae03a33234c454b8260b1a732852d83", "fcc269e40b0842859090290ee8278f0a", "6ed1af272b6d4482b34c512bfd83d903" ] }, "id": "WF50pQm-AlEY", "outputId": "80bb1008-fa01-486e-9114-57f1ccbbef2b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adapter saved to: qlora_green_patent_adapter/\n", "Unsloth: Merging model weights to 16-bit format...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0f38688c7cd5454fbae4b9c32b19f0de", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config.json: 0%| | 0.00/754 [00:00\", line 198, in _run_module_as_main\n", " File \"\", line 88, in _run_code\n", " File \"/usr/local/lib/python3.12/dist-packages/colab_kernel_launcher.py\", line 37, in \n", " ColabKernelApp.launch_instance()\n", " File \"/usr/local/lib/python3.12/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n", " app.start()\n", " File \"/usr/local/lib/python3.12/dist-packages/ipykernel/kernelapp.py\", line 712, in start\n", " self.io_loop.start()\n", " File \"/usr/local/lib/python3.12/dist-packages/tornado/platform/asyncio.py\", line 211, in start\n", " self.asyncio_loop.run_forever()\n", " File \"/usr/lib/python3.12/asyncio/base_events.py\", line 645, in run_forever\n", " self._run_once()\n", " File \"/usr/lib/python3.12/asyncio/base_events.py\", line 1999, in _run_once\n", " handle._run()\n", " File \"/usr/lib/python3.12/asyncio/events.py\", line 88, in _run\n", " self._context.run(self._callback, *self._args)\n", " File \"/usr/local/lib/python3.12/dist-packages/ipykernel/kernelbase.py\", line 510, in dispatch_queue\n", " await self.process_one()\n", " File \"/usr/local/lib/python3.12/dist-packages/ipykernel/kernelbase.py\", line 499, in process_one\n", " await dispatch(*args)\n", " File \"/usr/local/lib/python3.12/dist-packages/ipykernel/kernelbase.py\", line 406, in dispatch_shell\n", " await result\n", " File \"/usr/local/lib/python3.12/dist-packages/ipykernel/kernelbase.py\", line 730, in execute_request\n", " reply_content = await reply_content\n", " File \"/usr/local/lib/python3.12/dist-packages/ipykernel/ipkernel.py\", line 383, in do_execute\n", " res = shell.run_cell(\n", " File \"/usr/local/lib/python3.12/dist-packages/ipykernel/zmqshell.py\", line 528, in run_cell\n", " return super().run_cell(*args, **kwargs)\n", " File \"/usr/local/lib/python3.12/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n", " result = self._run_cell(\n", " File \"/usr/local/lib/python3.12/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n", " return runner(coro)\n", " File \"/usr/local/lib/python3.12/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n", " coro.send(None)\n", " File \"/usr/local/lib/python3.12/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n", " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", " File \"/usr/local/lib/python3.12/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n", " if (await self.run_code(code, result, async_=asy)):\n", " File \"/usr/local/lib/python3.12/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n", " File \"/tmp/ipykernel_2012/1969294773.py\", line 21, in \n", " outputs = model_qlora.generate(\n", " File \"/usr/local/lib/python3.12/dist-packages/peft/peft_model.py\", line 2048, in generate\n", " outputs = self.base_model.generate(*args, **kwargs)\n", " File \"/usr/local/lib/python3.12/dist-packages/unsloth/models/llama.py\", line 2128, in unsloth_fast_generate\n", " output = self._old_generate(*args, **kwargs)\n", " File \"/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n", " return func(*args, **kwargs)\n", " File \"/usr/local/lib/python3.12/dist-packages/transformers/generation/utils.py\", line 2668, in generate\n", " result = decoding_method(\n", " File \"/usr/local/lib/python3.12/dist-packages/transformers/generation/utils.py\", line 2863, in _sample\n", " outputs = self._prefill(input_ids, generation_config, model_kwargs)\n", " File \"/usr/local/lib/python3.12/dist-packages/transformers/generation/utils.py\", line 3857, in _prefill\n", " return self(**model_inputs, return_dict=True)\n", " File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1776, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1787, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " File \"/usr/local/lib/python3.12/dist-packages/unsloth/models/llama.py\", line 1474, in _CausalLM_fast_forward\n", " outputs = self.model(\n", " File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1776, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1787, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " File \"/usr/local/lib/python3.12/dist-packages/unsloth/models/llama.py\", line 1058, in LlamaModel_fast_forward\n", " attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(\n", " File \"/usr/local/lib/python3.12/dist-packages/transformers/modeling_attn_mask_utils.py\", line 394, in _prepare_4d_causal_attention_mask_for_sdpa\n", " attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)\n", " File \"/usr/local/lib/python3.12/dist-packages/transformers/modeling_attn_mask_utils.py\", line 74, in __init__\n", " logger.warning_once(DEPRECATION_MESSAGE, FutureWarning)\n", " File \"/usr/local/lib/python3.12/dist-packages/transformers/utils/logging.py\", line 327, in warning_once\n", " self.warning(*args, **kwargs)\n", "Message: 'The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.'\n", "Arguments: (,)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "--- Test Claim 1 ---\n", "Claim: 1. A computer-implemented method for setting network models of an electronic device being executed by a processor of the electronic device, the method comprising: setting a default network model list ...\n", "Model Output: {\"classification\": 0, \"label\": \"Not Green\", \"confidence\": \"High\", \"rationale\": \"This patent claim describes general-purpose technology without specific relevance to climate change mitigation or green technology under Y02.\"}\n", "--------------------------------------------------\n", "\n", "--- Test Claim 2 ---\n", "Claim: 1. A method for cleaning a substrate comprising contacting the substrate in a cleaning cycle with an aqueous cleaning solution comprising an aqueous diluent and a detergent composition, the detergent ...\n", "Model Output: {\"classification\": 1, \"label\": \"Green\", \"confidence\": \"High\", \"rationale\": \"This patent claim describes technology that contributes to climate change mitigation or environmental sustainability, consistent with Y02 classification.\"}\n", "--------------------------------------------------\n", "\n", "--- Test Claim 3 ---\n", "Claim: 1. A method of operating a glassmelting furnace, comprising (A) providing a glassmelting furnace having walls defining a chamber that encloses a bath comprising glassmaking materials, a crown over sai...\n", "Model Output: {\"classification\": 1, \"label\": \"Green\", \"confidence\": \"High\", \"rationale\": \"This patent claim describes technology that contributes to climate change mitigation or environmental sustainability, consistent with Y02 classification.\"}\n", "--------------------------------------------------\n" ] } ], "source": [ "#Smoke-test the fine-tuned model on 3 high-risk patent claims\n", "\n", "from unsloth import FastLanguageModel\n", "\n", "# Enable fast inference kernels (Unsloth-specific optimisation for generation speed)\n", "FastLanguageModel.for_inference(model_qlora)\n", "\n", "# Load the 100 high-risk claims and test on the top 3 most uncertain ones\n", "df_high_risk = pd.read_csv(\"hitl_green_100_final.csv\")\n", "test_claims = df_high_risk[\"text\"].head(3).tolist()\n", "\n", "for i, claim in enumerate(test_claims):\n", " # Build the full Alpaca-format prompt β€” identical structure to training\n", " prompt = ALPACA_TEMPLATE.format(\n", " instruction=(\n", " \"You are an expert patent classifier specializing in green technology. \"\n", " \"Analyze the following patent claim and determine if it is related to \"\n", " \"Green/Climate Change Mitigation Technology (Y02 classification). \"\n", " \"Respond with a JSON object containing your classification and reasoning.\"\n", " ),\n", " input=f\"Patent Claim: {claim[:1500]}\",\n", " output=\"\" # left empty β€” the model completes this field\n", " )\n", "\n", " # Tokenize and move to GPU\n", " inputs = tokenizer_qlora(prompt, return_tensors=\"pt\").to(DEVICE)\n", "\n", " # Generate response with near-deterministic sampling (temperature=0.1)\n", " # max_new_tokens=200 is enough for a compact JSON response\n", " outputs = model_qlora.generate(\n", " **inputs, max_new_tokens=200, temperature=0.1, do_sample=True\n", " )\n", "\n", " # Decode only the newly generated tokens β€” slice off the input prompt tokens\n", " response = tokenizer_qlora.decode(\n", " outputs[0][inputs[\"input_ids\"].shape[1]:], skip_special_tokens=True\n", " )\n", "\n", " print(f\"\\n--- Test Claim {i+1} ---\")\n", " print(f\"Claim: {claim[:200]}...\")\n", " print(f\"Model Output: {response[:300]}\")\n", " print(\"-\" * 50)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Xcj5R9Xj6ca4", "outputId": "8063a975-3f8b-4f62-e9cb-79d8f0fd17eb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "--- qlora_green_patent_gguf ---\n", " chat_template.jinja (0.000 GB)\n", " tokenizer.json (0.011 GB)\n", " model-00003-of-00004.safetensors (4.641 GB)\n", " tokenizer_config.json (0.000 GB)\n", " model-00002-of-00004.safetensors (4.578 GB)\n", " model-00001-of-00004.safetensors (4.566 GB)\n", " config.json (0.000 GB)\n", " model-00004-of-00004.safetensors (1.472 GB)\n", " model.safetensors.index.json (0.000 GB)\n", "\n", "--- qlora_green_patent_gguf_gguf ---\n", " Qwen3-8B.Q4_K_M.gguf (4.682 GB)\n", " Modelfile (0.000 GB)\n" ] } ], "source": [ "#Verify output files and their sizes\n", "import os\n", "\n", "for folder in [\"qlora_green_patent_gguf\", \"qlora_green_patent_gguf_gguf\"]:\n", " if os.path.exists(folder):\n", " print(f\"\\n--- {folder} ---\")\n", " for f in os.listdir(folder):\n", " path = f\"{folder}/{f}\"\n", " if os.path.isfile(path):\n", " size = os.path.getsize(path) / (1024**3)\n", " print(f\" {f} ({size:.3f} GB)\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uysdnFbx6crr", "outputId": "8732d450-e0cf-4f01-b733-d678d8c15d47" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n", "Done!\n" ] } ], "source": [ "#Copy the GGUF model file to Google Drive for persistent storage\n", "from google.colab import drive\n", "drive.mount(\"/content/drive\")\n", "\n", "import shutil\n", "# Copy the quantized GGUF file (~4.7 GB) to Google Drive root\n", "shutil.copy(\n", " \"qlora_green_patent_gguf_gguf/Qwen3-8B.Q4_K_M.gguf\",\n", " \"/content/drive/MyDrive/Qwen3-8B.Q4_K_M.gguf\"\n", ")\n", "print(\"Done!\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gogvSavp6wqG", "outputId": "3d1f6447-0dfa-44d7-e7c5-d8a9a91ed018" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done!\n" ] } ], "source": [ "# Back up the LoRA adapter and the high-risk claims CSV to Google Drive\n", "\n", "import shutil, os\n", "\n", "# Back up the high-risk claims CSV (contains MAS debate results and gold labels)\n", 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