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already satisfied: torch<3,>=2.3 in /usr/local/lib/python3.12/dist-packages (from bitsandbytes) (2.9.0+cu126)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from bitsandbytes) (2.0.2)\n", "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from bitsandbytes) (25.0)\n", "Requirement already satisfied: psutil in /usr/local/lib/python3.12/dist-packages (from accelerate) (5.9.5)\n", "Requirement already satisfied: pyyaml in /usr/local/lib/python3.12/dist-packages (from accelerate) (6.0.3)\n", "Requirement already satisfied: huggingface_hub>=0.21.0 in /usr/local/lib/python3.12/dist-packages (from accelerate) (0.36.0)\n", "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from accelerate) (0.7.0)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface_hub>=0.21.0->accelerate) (3.20.0)\n", "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub>=0.21.0->accelerate) (2025.3.0)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from huggingface_hub>=0.21.0->accelerate) (2.32.4)\n", "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub>=0.21.0->accelerate) (4.67.1)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub>=0.21.0->accelerate) (4.15.0)\n", "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub>=0.21.0->accelerate) (1.2.0)\n", "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (75.2.0)\n", "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (1.14.0)\n", "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (3.6)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (3.1.6)\n", "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.6.77)\n", "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.6.77)\n", "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.6.80)\n", "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (9.10.2.21)\n", "Requirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /usr/local/lib/python3.12/dist-packages (from 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nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (3.3.20)\n", "Requirement already satisfied: nvidia-nvtx-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.6.77)\n", "Requirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.6.85)\n", "Requirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (1.11.1.6)\n", "Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (3.5.0)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch<3,>=2.3->bitsandbytes) (1.3.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch<3,>=2.3->bitsandbytes) (3.0.3)\n", "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate) (3.4.4)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate) (3.11)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate) (2.5.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate) (2025.11.12)\n", "Downloading bitsandbytes-0.48.2-py3-none-manylinux_2_24_x86_64.whl (59.4 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.4/59.4 MB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: bitsandbytes\n", "Successfully installed bitsandbytes-0.48.2\n" ] } ], "source": [ "!pip install -q transformers torch accelerate\n", "!pip install -U bitsandbytes accelerate" ] }, { "cell_type": "code", "source": [ "import torch\n", "from tqdm import tqdm\n", "import pandas as pd\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n", "from transformers import StoppingCriteria, StoppingCriteriaList\n", "\n", "# 1. Define the Repo ID and the specific subfolder\n", "model_id = \"PrivacyPreservingML-SecureSQL/SecureSQL\"\n", "subfolder_name = \"finetuned-model-16\"\n", "\n", "# 2. Load Tokenizer and Model\n", "# Note: 'trust_remote_code=True' is required for DeepSeek-based models\n", "print(\"Loading model... this may take a minute.\")\n", "tokenizer = AutoTokenizer.from_pretrained(\n", " model_id,\n", " subfolder=subfolder_name,\n", " trust_remote_code=True\n", ")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " model_id,\n", " subfolder=subfolder_name,\n", " torch_dtype=torch.float16,\n", " load_in_8bit=False,\n", " device_map=\"auto\",\n", " trust_remote_code=True\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 372, "referenced_widgets": [ "c53d845eaa0945c7ba8829c38bbde42b", "0d212ab8719e46bf9cf5158969683c5e", "9f20c24225de4f4cad3b60bde8dada7c", "1721eeb9d6a14b908561b5d1a25de127", "cbc48107983b49c9a3de404c93642ce0", "45206823530847168e08c36e5b1d4f74", "a5de15077ec540ca93ae36ca60e4367d", "d721eb96c6a548d0a9b9f0b80f65d1c4", "3553df23ffa6461fba1702fc7b0abe17", "9be1037649bf4ee1ba8ed984b894457c", "c13065df7d574d8ebfdd39510e53e212", "a6726b43807340188c79b014ffd7ce41", "0b2600ad026e4a9ab1a34e7a76122de0", "1dca72087adc43d6aaed1c0febeb8170", "8d04ea3bea344d87a16d7a3f0576b953", "aa50d3465a534c70834595778d814346", "be51d1b980fa46f785a8fa15f7991e86", "6a665b0c50db49b4a0cd63f78b9a743b", "175ed143c8c5463a847a5555c9e84666", "df0aeeaca86043aa9c8c0b67101ce719", "9afa06a8f6574a8ca255c835425a7713", "b1f3dd1b1c584a7489e01aaee7cfc463", "f536dabcd7f947f2adcca63397bcfe83", "a15265ad5bb444ae8f76093f4bf04498", "a73a32ed1e6c4901bffd52a385a5367e", "9e6927715c504082b59e0ed0d320ca93", "d3dd20f7511748da96a9cb356bb4a58e", "d31c825d1c15441caa02c180bc569801", "d2c045d9f3f543f0a8f42c26c0bf59b7", "eae0b03224394fb3962167e3e24f5ec4", "d3c85929a6ad48a4b12ee2637b964d4e", "c2f571587cb149218f4598794d7a2ced", "2b663d3b5dc7418083d9479f30072f10", "a47b84c920034560abd0440757a23e54", "faa0e33a6a404159af657c8932bb57ef", "3e88c39de2e449feb9b7af1ac8ca7090", "c4badccbe1d645d29d18bb9207bd0462", "3621c7e89258440aa8e65e32e8af90ee", "3474d9f9be5545b9a392e1fa369f31d0", "b3116d79a95749bb85c9458701054795", "007eb61e75284ac7aebf526ed98def6e", "ee34000b4f2e4b028be9c67428366944", "ca437e15792f472b96db95f88d18ea00", "ed7948b54316426fa6311ec871622eb7", "1da43f9bf7684af4ae8c3ce4657b0719", "fd166387c07b40ffbde613f4af30e448", "20a402bfb5e845f5ad7f8ebb393bf5a0", "eca62645f9f247a287ec6a9891cdfc31", "0f3470589c60417485319fbdbd839427", "353cfd975d83438f8c314b9209602ce6", "db41248f905e457cb4ba999b54bda9a2", "a8cd8b291f0c45b3bd086507b6d972ce", "337a4aba2e834a2597e32fbd4560872b", "b8c789d058da415aa5a2f4fc6934b842", "21becb788b764fcf9d69815f48eb7286", "6e6e7529aaa0491d8bf6c2a873c720bd", "afd8062230a54528a9fb981dc36b7797", "a765ed25b505471fb2d27f38c4499898", "7a2d2f1b07c947bebe757165b4aeb42c", "6f5787e93c5b432ab183984faae848e5", "6cb77caa57e2465f9f28fe91c0e2246e", "ee7a4780efe746bb8983241aa58da7a2", "43fd9c1b4d754899b8dfec9045aae904", "c62e5b49bf4b44ac986351773fdfb7f2", "451acf84cec14daeae0ec8d86283103c", "51f4b2d2cc924a3e90c2fc80732cf4fd" ] }, "id": "0PpovPZdDKn1", "outputId": "a5549022-0c48-4746-b689-051012039399", "collapsed": true }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading model... this may take a minute.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c53d845eaa0945c7ba8829c38bbde42b" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer.json: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a6726b43807340188c79b014ffd7ce41" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "special_tokens_map.json: 0%| | 0.00/537 [00:00 20200000`.\n", "4. Use the `matches` table for game stats (score, minutes). Use `players` for biographical info (hand, height, country).\n", "\n", "### Examples\n", "Request: \"List all players from Spain.\"\n", "SQLite: SELECT name FROM players WHERE ioc = 'ESP';\n", "\n", "Request: \"How many matches did Roger Federer win in 2015?\"\n", "SQLite: SELECT COUNT(*) FROM matches WHERE winner_name = 'Roger Federer' AND tourney_date BETWEEN 20150000 AND 20151231;\n", "\n", "Request: \"What was Novak Djokovic's rank on 2019-01-07?\"\n", "SQLite: SELECT rank FROM rankings JOIN players ON rankings.player = players.player_id WHERE players.name = 'Novak Djokovic' AND ranking_date = 20190107;\n", "\n", "### Task\n", "Generate only the SQLite query prefaced by SQLite: and no other text.\n", "Request:\"\"\"" ], "metadata": { "id": "EOitfGBwFFXq" }, "execution_count": 34, "outputs": [] }, { "cell_type": "code", "source": [ "# 4. Run Inference\n", "# ---------------------------------------------------------\n", "# Decoder-only models need left-padding for generation\n", "tokenizer.padding_side = \"left\"\n", "\n", "# Ensure pad_token is defined\n", "if tokenizer.pad_token is None:\n", " tokenizer.pad_token = tokenizer.eos_token\n", "\n", "def run_batch_inference(prompts, batch_size=16):\n", " results = []\n", "\n", " # Process prompts in chunks\n", " for i in tqdm(range(0, len(prompts), batch_size)):\n", " batch_prompts = prompts[i : i + batch_size]\n", "\n", " # Tokenize the batch\n", " inputs = tokenizer(\n", " batch_prompts,\n", " return_tensors=\"pt\",\n", " padding=True,\n", " truncation=True\n", " ).to(model.device)\n", "\n", " # Generate output for the whole batch at once\n", " with torch.no_grad():\n", " outputs = model.generate(\n", " **inputs,\n", " max_new_tokens=300,\n", " do_sample=False, # Deterministic\n", " pad_token_id=tokenizer.pad_token_id\n", " )\n", "\n", " # Decode the batch\n", " # We only want the new tokens, not the input prompt\n", " input_length = inputs.input_ids.shape[1]\n", " generated_tokens = outputs[:, input_length:]\n", " decoded_batch = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)\n", "\n", " # Post-process: Clean up SQL\n", " for raw_text in decoded_batch:\n", " # 1. Isolate part after ### Response: (if the model generates it)\n", " if \"### Response:\" in raw_text:\n", " clean_sql = raw_text.split(\"### Response:\")[-1].strip()\n", " else:\n", " clean_sql = raw_text.strip()\n", "\n", " # 2. Remove SQLite\n", " if clean_sql.startswith(\"SQLite:\"):\n", " clean_sql = clean_sql.replace(\"SQLite:\", \"\", 1).strip()\n", "\n", " # 3. Cut strictly at the first semicolon\n", " if \";\" in clean_sql:\n", " clean_sql = clean_sql.split(\";\")[0].strip() + \";\"\n", "\n", " results.append(clean_sql)\n", "\n", " return results\n", "\n", "def format_prompt(user_question, context):\n", " # This combines the System Context + The User Question + The Response Trigger\n", " return f\"### Instruction:\\n{context}\\n{user_question}\\n### Response:\\n\"" ], "metadata": { "id": "awd-o591FLs3" }, "execution_count": 36, "outputs": [] }, { "cell_type": "code", "source": [ "### EDIT FOR TESTING HERE ###\n", "\n", "# 1. Import your regular testing or adversarial testing dataset\n", "test_set = pd.read_csv(\"test_set.tsv\", sep='\\t')\n", "\n", "# 2. Pick Instruction Being Tested (i.e. if \"basketball\" then use basketball_context and vice-versa)\n", "current_test = \"basketball\"\n", "\n", "raw_questions = test_set.iloc[:, 1].tolist()\n", "print(\"Formatting prompts...\")\n", "\n", "if current_test == \"basketball\":\n", " formatted_prompts = [format_prompt(q, basketball_context) for q in raw_questions]\n", "elif current_test == \"tennis\":\n", " formatted_prompts = [format_prompt(q, tennis_context) for q in raw_questions]" ], "metadata": { "id": "EAmcNCyxD64U" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(f\"Starting batched inference on {len(formatted_prompts)} examples...\")\n", "\n", "generated_sqls = run_batch_inference(formatted_prompts, batch_size=16)\n", "\n", "test_set['generated_sql'] = generated_sqls\n", "print(\"Inference complete.\")" ], "metadata": { "id": "1qU4WMFqMcsw", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "5d703264-56a9-4163-c796-5a76b054a85d" }, "execution_count": 38, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Starting batched inference on 150 examples...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 10/10 [04:08<00:00, 24.82s/it]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Inference complete.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] } ] }, { "cell_type": "code", "source": [ "# Save results for further local checking\n", "test_set.to_csv(current_test + \"_output.csv\", index=False)" ], "metadata": { "id": "e5RkP4CI2KwZ" }, "execution_count": 33, "outputs": [] } ] }