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"import pandas as pd\n", "from peft import LoraConfig, AutoPeftModelForCausalLM, PeftModel, get_peft_model\n", "import sqlparse\n", "import bitsandbytes as bnb\n", "import os\n", "from huggingface_hub import hf_hub_download\n", "from huggingface_hub import HfFileSystem\n", "import re\n", "import json" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "s7RtbLxPwTx2", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "43d5ff02-3e33-439c-e3a9-c18ce99d237d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "# from google.colab import drive\n", "# from google.colab import userdata\n", "# drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "basaX_55Yf_D" }, "outputs": [], "source": [ "#transformers.logging.set_verbosity_info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bkkjgGdlrNcq" }, "outputs": [], "source": [ "WRITE_TOKEN = userdata.get('hf_write')\n", "READ_TOKEN = userdata.get('hf_read')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7CKnwlRfZj4V" }, "outputs": [], "source": [ "model_name = \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\"\n", "out_name = \"lleticiasilvaa/TinyLlama-1.1B-Chat-SchemaLinking-v1\"\n", "prev_checkpoint = \"checkpoint-5000\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9zs7nCmt-pMC" }, "outputs": [], "source": [ "#!huggingface-cli login" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PZdnxs8k-Cgl" }, "outputs": [], "source": [ "bird_id=\"NESPED-GEN/bird\"\n", "spider_id=\"NESPED-GEN/spider\"\n", "\n", "selector_id = \"NESPED-GEN/selector_testes\"" ] }, { "cell_type": "markdown", "metadata": { "id": "xT2iRdCN_MFH" }, "source": [ "### Load Data\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lLKgY40efdJo" }, "outputs": [], "source": [ "# bird = load_dataset(bird_id, split=\"train\").to_pandas()\n", "# spider = load_dataset(spider_id, split=\"train\").to_pandas()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "D3d_091Ud0BG" }, "outputs": [], "source": [ "# df = pd.concat([spider, bird], axis=0)\n", "# df = df.fillna('')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "niRTjA1tQIWr" }, "outputs": [], "source": [ "# df.reset_index(drop=True, inplace=True)" ] }, { "cell_type": "code", "source": [ "df = load_dataset(selector_id, split=\"trainSpider\").to_pandas()" ], "metadata": { "id": "LeL7EfmQVyBt", "colab": { "base_uri": "https://localhost:8080/", "referenced_widgets": [ "429a59d16b7542688903eeb2aff493a3", "dd788f02bd4445cfa28f8286bf439524", "0f50e3bb926a4b6dabf7426b131bddda", "8d09861c66cc4eccb3729013fcbe8d93", "e508e2655c114923b49e6ee553319bae", "6114c450ae0346aaade2f204a2ae167a", "66f261bee7e8483c877c7bf04cd2ecdc", "d1914810246447abaa0d8d0b12114b6b", "3ae02ddc8ad641cc94d0c1d5c3c7b3f2", "68768902902047fe9839e48ee51619c3", "d7f409b044d64177b06f3315929ef716", "25017a5635394842b6cefbdf0c1c3000", "8942bf5dae0a49149a6dee9cc5c851ec", "d3d0bfa5258948419afa2e2b665caf97", "2e1973a99c74456e96b5a2b39ab1b503", "154d0f6a08614876809fb43121a86035", "c0dac1edbae54a70bc8afceb8dbd08df", "35c69a79a6434d419ebd9fef4a99582c", "b9e40804e7aa40d583a8f86c29daac3b", "6209432ba23748ed8675ec4ad03ec45c", "9a3e8b078a1b473387d3826e4bbf96a9", "5c2225e347144af39993f24c520b9f66", "1b28002cb69a4eb3bac1b6edca047b40", "15a21d9b8f1d48eea283cbf92d026aa0", "43d4eecaf04848fd999adcf4cae3a9dd", "18b16ecd1d2d4cef9f21137b2cc9ce33", "32eee5a2e2e74e9798c102b01ec73569", "ab5152b752e64e9a9d44082aae01616f", "8bdd8990aa1143e49fa32552ee801620", "27085d0a3fe04bdb91e35052fef5414c", "acbed9119e4a40638eeb2ff642d69829", "998ac97df8184620a00bc7a3b1a78eca", "a573fd2eff9a4272963dd3b92fe1c2e5", "916454d677484c3582998db8b0fbe0f2", "25cb68f18a814fe797bc7cfa1017992f", "0d24e112279e4ce996648860e9746bb6", "45375f3cd44e47bcb5ad20d5987ebdf0", "0b02ad692c454d9692880921ab9f4362", "36b2344a52964ce99a53b26cafa6faee", "249c2598fbc34540bef80a62dc5f8b84", "88b70880f0ad49e3a31a9770a040fa93", "fc9ee1cc3e75498bbd84958b238dcadb", "33ef0b636640476c9b4976d557c242f2", "af139cb001a44d4082fccb6f36c79b1c", "5caa0f49a0e0453f918589b0f4c4db15", "d47cd4e49d064ad289ef568f2c6c2830", "e190265d332b4f5e9ca78a762e4b1b01", "933c4ec5c2034f4eaf1c98cda0646da8", "5cff514f785542afbad83c22df92f089", "3a86be5ed8ab4ca1b44a428d5cd3891c", "151d4c5431494b62abdeaf5acee59740", "f398a353b76345248581ed0149abfadc", "e75f25a1c64a4624878967f11a91dd26", "771172db7b0848bd852cc7d74b833a93", "a55181dcf7f142f496dc130c1d1d7e43", "f9bb9c72807d4265acc9861a9b5eaf1f", "ad448eb51ddd4192b7853b4d0768a764", "1217975d683443aaadecab05c67c97fd", "22968ed040df4861b5cc0670859964d5", "4d22a79c06984f85a277e320bf1f012b", "05da22add79d45a2ba34f1bb52b25946", "97d8d78ee7d54b289b67543851ca0aaa", "dd45e353260847b19319238d74f9fa69", "d9d64d0754414fa5849b3a468e2eff48", "bfaf88b229d74b5dbec22942a04d1589", "e75340e3442f4bd7883d90431debd73e", "df970712d4dd4c9d9079fbc9f144a22e", "349df86cfd7a4b7e9b1dc7fa25267330", "638d820fac5c415f8df865302467281c", "2b158e1e31b840b8b69598c4c89e24e6", "e3110d189c4d47b78b72c1d5c3c39fa8", "02889cd1d584424b8ed7e6c3e0115cae", "24c7bb51c2b24804b523abb2d926671c", "3a0a593589bf4e0caaab2022f4d67bef", "f637aa64d8934c71920fa16dbfa0bca4", "9991ee59d1c94348998b6b7b22e402c6", "568a6ec691e64ef8ad4f2842556f7196" ] }, "outputId": "72f38ba8-1d64-4e08-a4b1-d16dfe8b390e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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": [ "README.md: 0%| | 0.00/1.23k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "429a59d16b7542688903eeb2aff493a3" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "trainSpider-00000-of-00001.parquet: 0%| | 0.00/2.00M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "25017a5635394842b6cefbdf0c1c3000" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "devSpider-00000-of-00001.parquet: 0%| | 0.00/297k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "1b28002cb69a4eb3bac1b6edca047b40" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "testSpider-00000-of-00001.parquet: 0%| | 0.00/649k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "916454d677484c3582998db8b0fbe0f2" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating trainSpider split: 0%| | 0/8656 [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "5caa0f49a0e0453f918589b0f4c4db15" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating devSpider split: 0%| | 0/1034 [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "f9bb9c72807d4265acc9861a9b5eaf1f" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating testSpider split: 0%| | 0/2147 [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "df970712d4dd4c9d9079fbc9f144a22e" } }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "ON-hPsEYM1Bu" }, "source": [ "# Load Base Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yEAZpfzlNOHW" }, "outputs": [], "source": [ "def download_checkpoint(adapter_model_id, checkpoint):\n", "\n", "\n", " fs = HfFileSystem()\n", " for file in fs.ls(f'{adapter_model_id}/{checkpoint}', detail=False):\n", " file_name = file.split(checkpoint)[-1]\n", "\n", " hf_hub_download(repo_id=adapter_model_id, filename=(f'{checkpoint}{file_name}'), local_dir='out')\n", "\n", " for file in fs.ls(f'{adapter_model_id}/logs', detail=False):\n", " file_name = file.split(checkpoint)[-1]\n", "\n", " hf_hub_download(repo_id=adapter_model_id, filename=(f'logs/{file_name.split(\"/\")[-1]}'), local_dir='out')" ] }, { "cell_type": "code", "source": [ "# download_checkpoint(out_name, prev_checkpoint)" ], "metadata": { "id": "enUxjGXEqHxg" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "M7DoqQMlM_nW", "colab": { "base_uri": "https://localhost:8080/", "height": 721, "referenced_widgets": [ "59b5ca633278447db162a0c49f76947f", "c0cb169bd86b4ff7878fa5c4710a3fdc", "55c7d90c78a04c4c8186af02e39142ea", "7ee76b4d117444dca918aa39d99c38c8", "1bf0fa99f5394a94a25a5866efb6620d", "bce38f4ebcbb420daaa2afafe2d53107", "665a5323ccba49a9bbcef3b3e4fbe3ad", "70c627612ce54653b6ebc6a02a96aa05", "28b77fc1dacf49c3b15a97d8366aafdc", "3b29241864624a20a49adbc7e1b23c49", "0fcb35cd95fa422895c4cb6d6b330c10", "71c01d7b65c447febefe708cf5e11005", "6cf21323d9b944ed89cd71a42a8a1d42", "7bc67d428431451fab406abad0d2a83a", "0de86178923d4d8c86072038ce566a7d", "95823f7a39d640f6b1ce00bb294760a1", "e466f6a60f4c443691563c60a4be2399", 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0%| | 0.00/500k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "cd19bdbf1717400f8ba93251c381facd" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer.json: 0%| | 0.00/1.84M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "94d5807b5d7948e4acc8318ffea28c0d" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "special_tokens_map.json: 0%| | 0.00/551 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "99c4320c7c8b4625a1844e594f3c390a" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/608 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "5b0c7a425655428ea834fa82dcf30cca" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/2.20G [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "ccdef26bd0ce4ebbb991fdda8c53dc76" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "generation_config.json: 0%| | 0.00/124 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "99264da13f0c4704a61bba78dd2f841b" } }, "metadata": {} } ], "source": [ "\n", "\n", "seed=14\n", "\n", "if (prev_checkpoint != None):\n", " try:\n", " download_checkpoint(out_name, prev_checkpoint)\n", " except:\n", " pass\n", "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, token=READ_TOKEN, map_device=\"auto\", add_eos_token=True, use_fast=True)\n", "\n", "if torch.cuda.is_bf16_supported():\n", " compute_dtype = torch.bfloat16\n", " attn_implementation = 'flash_attention_2'\n", "else:\n", " compute_dtype = torch.float16\n", " attn_implementation = 'sdpa'\n", "\n", "tokenizer.pad_token = tokenizer.eos_token\n", "tokenizer.padding_side = \"right\"\n", "\n", "\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " # load_in_8bit=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", " bnb_4bit_compute_dtype=compute_dtype,\n", " bnb_4bit_use_double_quant=False,\n", ")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " model_name,\n", " torch_dtype=compute_dtype,\n", " device_map=\"auto\",\n", " quantization_config=bnb_config,\n", "\n", " trust_remote_code=True,\n", " token=READ_TOKEN,\n", " # attn_implementation=attn_implementation\n", ")\n", "\n", "# model.resize_token_embeddings(max(len(tokenizer), model.config.vocab_size))" ] }, { "cell_type": "markdown", "metadata": { "id": "leGEqxURAL4r" }, "source": [ "#### Chat Template" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aHGTJNq2AmUs" }, "outputs": [], "source": [ "def to_sql(query):\n", " return sqlparse.format(query, reindent=True, keyword_case='upper')\n", "\n", "def replace_last_sql_content(text, new_content):\n", " matches = list(re.finditer(r'```sql (.*?)```', text, re.DOTALL))\n", " if not matches:\n", " return text\n", "\n", " last_match = matches[-1]\n", "\n", " start, end = last_match.span(1)\n", " return text[:start] + new_content + text[end:]\n", "\n", "# def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", "# question = row['question_en']\n", "# schema = row['schema_llm']\n", "# sql = to_sql(row['query_llm'])\n", "\n", "# system = \"Given a user question and the schema of a database, your task is to generate an SQL query that accurately answers the question based on the provided schema.\"\n", "\n", "\n", "# chat = [{'role': 'user', 'content': f\"# System:\\n{system}\\n\\n# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n", "# {'role': 'assistant', 'content': f\"```sql\\n{sql}\\n```\"}\n", "# ]\n", "\n", "# row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n", "\n", "# return row\n", "\n", "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", " question = row['question_en']\n", " schema = row['schema_llm_t']\n", " schema_linking = to_sql(row['selector_correct'])\n", "\n", " system = \"Given a user question and the schema of a database, your task is to generate an JSON with the the names of tables and columns of the schema that the question is referring to.\"\n", "\n", " chat = [{'role': 'user', 'content': f\"# System:\\n{system}\\n\\n# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n", " {'role': 'assistant', 'content': f\"```json\\n{schema_linking}\\n```\"}\n", " ]\n", "\n", " row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n", "\n", " return row" ] }, { "cell_type": "code", "source": [ "# spider_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/spider/spider_chain.json\", \"r\"))\n", "# bird_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/bird/bird_chain.json\", \"r\"))" ], "metadata": { "id": "QlA1kMj83Vql" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# df['chain'] = spider_chain + bird_chain" ], "metadata": { "id": "pCcD4TiC3rpb" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yvcMZAL3E3TB" }, "outputs": [], "source": [ "df = df.apply(apply_template, axis=1)" ] }, { "cell_type": "code", "source": [ "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 539 }, "id": "rQ0KUl2w58K9", "outputId": "04e15bf7-3bbe-400f-bc09-117e8bcc8573" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " db_id schema \\\n", "0 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n", "1 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n", "2 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n", "3 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n", "4 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n", "\n", " question_en hardness \\\n", "0 How many heads of the departments are older th... easy \n", "1 List the name, born state and age of the heads... medium \n", "2 List the creation year, name and budget of eac... medium \n", "3 What are the maximum and minimum budget of the... medium \n", "4 What is the average number of employees of the... easy \n", "\n", " schema_llm \\\n", "0 Table department (\\n department.Department_... \n", "1 Table department (\\n department.Department_... \n", "2 Table department (\\n department.Department_... \n", "3 Table department (\\n department.Department_... \n", "4 Table department (\\n department.Department_... \n", "\n", " query_llm \\\n", "0 SELECT count(*) FROM head WHERE age > 56 \n", "1 SELECT name , born_state , age FROM head ORD... \n", "2 SELECT creation , name , budget_in_billions ... \n", "3 SELECT max(budget_in_billions) , min(budget_i... \n", "4 SELECT avg(num_employees) FROM department WHER... \n", "\n", " selector \\\n", "0 {\\n 'head': ['head_id', 'age']\\n} \n", "1 {\\n 'head': ['head_id', 'name', 'born_state',... \n", "2 {\\n 'department': ['department_id', 'name', '... \n", "3 {\\n 'department': ['department_id', 'budget_i... \n", "4 {\\n 'department': ['department_id', 'ranking'... \n", "\n", " schema_llm_ct \\\n", "0 CREATE TABLE department (\\n department.Depa... \n", "1 CREATE TABLE department (\\n department.Depa... \n", "2 CREATE TABLE department (\\n department.Depa... \n", "3 CREATE TABLE department (\\n department.Depa... \n", "4 CREATE TABLE department (\\n department.Depa... \n", "\n", " schema_llm_columns_min \\\n", "0 Table department (\\n department.department_... \n", "1 Table department (\\n department.department_... \n", "2 Table department (\\n department.department_... \n", "3 Table department (\\n department.department_... \n", "4 Table department (\\n department.department_... \n", "\n", " schema_llm_columns_min_ct \\\n", "0 CREATE TABLE department (\\n department.depa... \n", "1 CREATE TABLE department (\\n department.depa... \n", "2 CREATE TABLE department (\\n department.depa... \n", "3 CREATE TABLE department (\\n department.depa... \n", "4 CREATE TABLE department (\\n department.depa... \n", "\n", " schema_llm_all_min \\\n", "0 Table department (\\n department.department_... \n", "1 Table department (\\n department.department_... \n", "2 Table department (\\n department.department_... \n", "3 Table department (\\n department.department_... \n", "4 Table department (\\n department.department_... \n", "\n", " schema_llm_all_min_ct \\\n", "0 CREATE TABLE department (\\n department.depa... \n", "1 CREATE TABLE department (\\n department.depa... \n", "2 CREATE TABLE department (\\n department.depa... \n", "3 CREATE TABLE department (\\n department.depa... \n", "4 CREATE TABLE department (\\n department.depa... \n", "\n", " schema_dict \\\n", "0 {\\n 'department': ['Department_ID', 'Name', '... \n", "1 {\\n 'department': ['Department_ID', 'Name', '... \n", "2 {\\n 'department': ['Department_ID', 'Name', '... \n", "3 {\\n 'department': ['Department_ID', 'Name', '... \n", "4 {\\n 'department': ['Department_ID', 'Name', '... \n", "\n", " selector_correct \\\n", "0 {\\n 'head': ['head_ID', 'age']\\n} \n", "1 {\\n 'head': ['head_ID', 'name', 'born_state',... \n", "2 {\\n 'department': ['Department_ID', 'Name', '... \n", "3 {\\n 'department': ['Department_ID', 'Budget_i... \n", "4 {\\n 'department': ['Department_ID', 'Ranking'... \n", "\n", " schema_llm_t \\\n", "0 TABLE department (\\n department.Department_... \n", "1 TABLE department (\\n department.Department_... \n", "2 TABLE 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<|user|>\\n# System:\\nGiven a user question and... \n", "4 <|user|>\\n# System:\\nGiven a user question and... 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2 | \n", "department_management | \n", "\\nCREATE TABLE department (\\n\\t\"Department_ID\"... | \n", "List the creation year, name and budget of eac... | \n", "medium | \n", "Table department (\\n department.Department_... | \n", "SELECT creation , name , budget_in_billions ... | \n", "{\\n 'department': ['department_id', 'name', '... | \n", "CREATE TABLE department (\\n department.Depa... | \n", "Table department (\\n department.department_... | \n", "CREATE TABLE department (\\n department.depa... | \n", "Table department (\\n department.department_... | \n", "CREATE TABLE department (\\n department.depa... | \n", "{\\n 'department': ['Department_ID', 'Name', '... | \n", "{\\n 'department': ['Department_ID', 'Name', '... | \n", "TABLE department (\\n department.Department_... | \n", "TABLE department (\\n department.department_... | \n", "TABLE department (\\n department.department_... | \n", "<|user|>\\n# System:\\nGiven a user question and... | \n", "
3 | \n", "department_management | \n", "\\nCREATE TABLE department (\\n\\t\"Department_ID\"... | \n", "What are the maximum and minimum budget of the... | \n", "medium | \n", "Table department (\\n department.Department_... | \n", "SELECT max(budget_in_billions) , min(budget_i... | \n", "{\\n 'department': ['department_id', 'budget_i... | \n", "CREATE TABLE department (\\n department.Depa... | \n", "Table department (\\n department.department_... | \n", "CREATE TABLE department (\\n department.depa... | \n", "Table department (\\n department.department_... | \n", "CREATE TABLE department (\\n department.depa... | \n", "{\\n 'department': ['Department_ID', 'Name', '... | \n", "{\\n 'department': ['Department_ID', 'Budget_i... | \n", "TABLE department (\\n department.Department_... | \n", "TABLE department (\\n department.department_... | \n", "TABLE department (\\n department.department_... | \n", "<|user|>\\n# System:\\nGiven a user question and... | \n", "
4 | \n", "department_management | \n", "\\nCREATE TABLE department (\\n\\t\"Department_ID\"... | \n", "What is the average number of employees of the... | \n", "easy | \n", "Table department (\\n department.Department_... | \n", "SELECT avg(num_employees) FROM department WHER... | \n", "{\\n 'department': ['department_id', 'ranking'... | \n", "CREATE TABLE department (\\n department.Depa... | \n", "Table department (\\n department.department_... | \n", "CREATE TABLE department (\\n department.depa... | \n", "Table department (\\n department.department_... | \n", "CREATE TABLE department (\\n department.depa... | \n", "{\\n 'department': ['Department_ID', 'Name', '... | \n", "{\\n 'department': ['Department_ID', 'Ranking'... | \n", "TABLE department (\\n department.Department_... | \n", "TABLE department (\\n department.department_... | \n", "TABLE department (\\n department.department_... | \n", "<|user|>\\n# System:\\nGiven a user question and... | \n", "
Step | \n", "Training Loss | \n", "Validation Loss | \n", "
---|---|---|
5250 | \n", "0.022700 | \n", "0.069724 | \n", "
" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", " return fn(*args, **kwargs)\n", "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n" ] } ], "source": [ "steps=250\n", "strategy=\"steps\"\n", "\n", "trainer = SFTTrainer(\n", " model= model,\n", " tokenizer=tokenizer,\n", "\n", " train_dataset=train_dataset,\n", " eval_dataset=valid_dataset,\n", "\n", " peft_config=peft_config,\n", "\n", " dataset_text_field=\"text\",\n", " max_seq_length=2048,\n", " args= TrainingArguments(\n", " output_dir=\"out\",\n", "\n", " save_strategy=strategy,\n", " save_steps= steps,\n", "\n", " logging_strategy=strategy,\n", " logging_steps=steps,\n", " logging_dir=\"out/logs\",\n", "\n", " eval_strategy=strategy,\n", " eval_steps=steps,\n", "\n", " num_train_epochs=5,\n", " per_device_train_batch_size=1,\n", " per_device_eval_batch_size=1,\n", " gradient_accumulation_steps=8,\n", " gradient_checkpointing=True,\n", " fp16=True,\n", " bf16=False,\n", "\n", " optim=\"paged_adamw_8bit\",\n", " learning_rate=1e-4,\n", " weight_decay=0.001,\n", " max_grad_norm=0.3,\n", " max_steps=-1,\n", " warmup_ratio=0.03,\n", " group_by_length=True,\n", " lr_scheduler_type=\"cosine\",\n", " seed= seed,\n", "\n", "\n", " report_to=[\"tensorboard\"],\n", " push_to_hub=True,\n", " hub_strategy=\"all_checkpoints\",\n", " hub_model_id=out_name,\n", "\n", " label_names=[\"labels\"]\n", " )\n", "\n", ")\n", "if(prev_checkpoint != None):\n", " trainer.train(f\"out/{prev_checkpoint}\")\n", "else:\n", " trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "i39Ov4dsGv77", "colab": { "base_uri": "https://localhost:8080/", "height": 86 }, "outputId": "c633a093-5eff-4032-eb4c-30b074e0f6f2" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "CommitInfo(commit_url='https://huggingface.co/lleticiasilvaa/TinyLlama-1.1B-Chat-SchemaLinking-v1/commit/22feb0b4a972eeb9eedbd6f538cc53379d433191', commit_message='End of training', commit_description='', oid='22feb0b4a972eeb9eedbd6f538cc53379d433191', pr_url=None, pr_revision=None, pr_num=None)" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 30 } ], "source": [ "trainer.push_to_hub()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "default:Python", "language": "python", "name": "conda-env-default-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" }, 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