@@ -0,0 +1,6712 @@
1+{
2+ "cells": [
3+ {
4+ "cell_type": "markdown",
5+ "metadata": {
6+ "id": "_5rfduRD1CSF"
7+ },
8+ "source": [
9+ "# A guide to fine-tuning Code Llama\n",
10+ "\n",
11+ "**In this guide I show you how to fine-tune Code Llama to become a beast of an SQL developer. For coding tasks, you can generally get much better performance out of Code Llama than Llama 2, especially when you specialise the model on a particular task:**\n",
12+ "\n",
13+ "- I use the [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) which is a bunch of text queries and their corresponding SQL queries\n",
14+ "- A Lora approach, quantizing the base model to int 8, freezing its weights and only training an adapter\n",
15+ "- Much of the code is borrowed from [alpaca-lora](https://github.com/tloen/alpaca-lora), but I refactored it quite a bit for this\n"
16+ ]
17+ },
18+ {
19+ "cell_type": "markdown",
20+ "metadata": {
21+ "id": "HFQ9XeBI1CSG"
22+ },
23+ "source": [
24+ "### 2. Pip installs\n"
25+ ]
26+ },
27+ {
28+ "cell_type": "code",
29+ "execution_count": null,
30+ "metadata": {
31+ "colab": {
32+ "base_uri": "https://localhost:8080/"
33+ },
34+ "id": "Y0x4OWfq1CSG",
35+ "outputId": "0cd5de72-1e8e-4e78-a99d-91bc439b6a25"
36+ },
37+ "outputs": [],
38+ "source": [
39+ "!pip install git+https://github.com/huggingface/transformers.git@main bitsandbytes accelerate==0.20.3 # we need latest transformers for this\n",
40+ "!pip install git+https://github.com/huggingface/peft.git@e536616888d51b453ed354a6f1e243fecb02ea08\n",
41+ "!pip install datasets==2.10.1\n",
42+ "import locale # colab workaround\n",
43+ "locale.getpreferredencoding = lambda: \"UTF-8\" # colab workaround\n",
44+ "!pip install wandb"
45+ ]
46+ },
47+ {
48+ "cell_type": "markdown",
49+ "metadata": {
50+ "id": "mCmGzYg51CSH"
51+ },
52+ "source": [
53+ "I used an A100 GPU machine with Python 3.10 and cuda 11.8 to run this notebook. It took about an hour to run."
54+ ]
55+ },
56+ {
57+ "cell_type": "markdown",
58+ "metadata": {
59+ "id": "0mu9JczX1CSH"
60+ },
61+ "source": [
62+ "### Loading libraries\n"
63+ ]
64+ },
65+ {
66+ "cell_type": "code",
67+ "execution_count": null,
68+ "metadata": {
69+ "id": "oTeYW8z51CSH"
70+ },
71+ "outputs": [],
72+ "source": [
73+ "from datetime import datetime\n",
74+ "import os\n",
75+ "import sys\n",
76+ "\n",
77+ "import torch\n",
78+ "from peft import (\n",
79+ " LoraConfig,\n",
80+ " get_peft_model,\n",
81+ " get_peft_model_state_dict,\n",
82+ " prepare_model_for_int8_training,\n",
83+ " set_peft_model_state_dict,\n",
84+ ")\n",
85+ "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForSeq2Seq\n"
86+ ]
87+ },
88+ {
89+ "cell_type": "markdown",
90+ "metadata": {
91+ "id": "32zH9-hM1CSH"
92+ },
93+ "source": [
94+ "(If you have import errors, try restarting your Jupyter kernel)\n"
95+ ]
96+ },
97+ {
98+ "cell_type": "markdown",
99+ "metadata": {
100+ "id": "4M9KyT0S1CSH"
101+ },
102+ "source": [
103+ "### Load dataset\n"
104+ ]
105+ },
106+ {
107+ "cell_type": "code",
108+ "execution_count": null,
109+ "metadata": {
110+ "colab": {
111+ "base_uri": "https://localhost:8080/",
112+ "height": 201,
113+ "referenced_widgets": [
114+ "8dfde52535634cf0898ca3ad97a2bebf",
115+ "022c65eb5f644441a7a8036cae95a091",
116+ "b7943275128c496c9c413e060dbc305e",
117+ "44581e62dd9e44dd83ff8213741aafad",
118+ "df34c464828d43d988bf739efe2567e0",
119+ "505adabf4efe4b27b3eff1d5bbcc0e47",
120+ "5ca855ef47fb4e82ac84ab0edbc0db1c",
121+ "7547e841c87a4edab5df09756fdb49a7",
122+ "e31759206e80422a8ac4e1e6acc14d20",
123+ "61d63eb2e8f040f2a10ccbe0c855ed58",
124+ "8e06afd88e59496d8989d3e474cbc55c",
125+ "06eb9f93479b4397803f7118db6fd499",
126+ "ceb68923d32f47f7b84b423611c28b59",
127+ "eefd12c168834f33ab007dbadec46d7a",
128+ "a46e7db7f01f4c58832f5d068e7dda1f",
129+ "16b8091de4b94f9f9c71dd7af3a45cec",
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168+ "93c14d8faaac497a974ef5ee3ccdc585"
169+ ]
170+ },
171+ "id": "w44O1EK-1CSH",
172+ "outputId": "01a36c98-1e43-44f6-d5c6-1a9bf908c933"
173+ },
174+ "outputs": [],
175+ "source": [
176+ "from datasets import load_dataset\n",
177+ "dataset = load_dataset(\"b-mc2/sql-create-context\", split=\"train\")\n",
178+ "train_dataset = dataset.train_test_split(test_size=0.1)[\"train\"]\n",
179+ "eval_dataset = dataset.train_test_split(test_size=0.1)[\"test\"]"
180+ ]
181+ },
182+ {
183+ "cell_type": "markdown",
184+ "metadata": {
185+ "id": "fJ54EffO1CSI"
186+ },
187+ "source": [
188+ "The above pulls the dataset from the Huggingface Hub and splits 10% of it into an evaluation set to check how well the model is doing through training. If you want to load your own dataset do this:\n",
189+ "\n",
190+ "```\n",
191+ "train_dataset = load_dataset('json', data_files='train_set.jsonl', split='train')\n",
192+ "eval_dataset = load_dataset('json', data_files='validation_set.jsonl', split='train')\n",
193+ "```\n",
194+ "\n",
195+ "And if you want to view any samples in the dataset just do something like:``` ```\n"
196+ ]
197+ },
198+ {
199+ "cell_type": "code",
200+ "execution_count": null,
201+ "metadata": {
202+ "colab": {
203+ "base_uri": "https://localhost:8080/"
204+ },
205+ "id": "fFbeaZzf1CSJ",
206+ "outputId": "90758279-19ee-48cc-8d52-8c96902e08c2"
207+ },
208+ "outputs": [],
209+ "source": [
210+ "print(train_dataset[3])"
211+ ]
212+ },
213+ {
214+ "cell_type": "markdown",
215+ "metadata": {
216+ "id": "VHdMYcu61CSJ"
217+ },
218+ "source": [
219+ "Each entry is made up of a text 'question', the sql table 'context' and the 'answer'."
220+ ]
221+ },
222+ {
223+ "cell_type": "markdown",
224+ "metadata": {
225+ "id": "6ig7NvWN1CSJ"
226+ },
227+ "source": [
228+ "### Load model\n",
229+ "I load code llama from huggingface in int8. Standard for Lora:"
230+ ]
231+ },
232+ {
233+ "cell_type": "code",
234+ "execution_count": null,
235+ "metadata": {
236+ "colab": {
237+ "base_uri": "https://localhost:8080/",
238+ "height": 387,
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361+ ]
362+ },
363+ "id": "rMnU93bY1CSJ",
364+ "outputId": "2081b3ab-4073-4687-d888-e1b7ec0e7548"
365+ },
366+ "outputs": [],
367+ "source": [
368+ "base_model = \"codellama/CodeLlama-7b-hf\"\n",
369+ "model = AutoModelForCausalLM.from_pretrained(\n",
370+ " base_model,\n",
371+ " load_in_8bit=True,\n",
372+ " torch_dtype=torch.float16,\n",
373+ " device_map=\"auto\",\n",
374+ ")\n",
375+ "tokenizer = AutoTokenizer.from_pretrained(\"codellama/CodeLlama-7b-hf\")"
376+ ]
377+ },
378+ {
379+ "cell_type": "markdown",
380+ "metadata": {
381+ "id": "F3OF-wtj1CSJ"
382+ },
383+ "source": [
384+ "torch_dtype=torch.float16 means computations are performed using a float16 representation, even though the values themselves are 8 bit ints.\n",
385+ "\n",
386+ "If you get error \"ValueError: Tokenizer class CodeLlamaTokenizer does not exist or is not currently imported.\" Make sure you have transformers version is 4.33.0.dev0 and accelerate is >=0.20.3.\n"
387+ ]
388+ },
389+ {
390+ "cell_type": "markdown",
391+ "metadata": {
392+ "id": "_2VXqJJe1CSJ"
393+ },
394+ "source": [
395+ "### 3. Check base model\n",
396+ "A very good common practice is to check whether a model can already do the task at hand. Fine-tuning is something you want to try to avoid at all cost:\n"
397+ ]
398+ },
399+ {
400+ "cell_type": "code",
401+ "execution_count": null,
402+ "metadata": {
403+ "colab": {
404+ "base_uri": "https://localhost:8080/"
405+ },
406+ "id": "uiyAff1a1CSJ",
407+ "outputId": "c26db817-df1a-4776-fc6e-7e3a78ba1238"
408+ },
409+ "outputs": [],
410+ "source": [
411+ "eval_prompt = \"\"\"You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.\n",
412+ "\n",
413+ "You must output the SQL query that answers the question.\n",
414+ "### Input:\n",
415+ "Which Class has a Frequency MHz larger than 91.5, and a City of license of hyannis, nebraska?\n",
416+ "\n",
417+ "### Context:\n",
418+ "CREATE TABLE table_name_12 (class VARCHAR, frequency_mhz VARCHAR, city_of_license VARCHAR)\n",
419+ "\n",
420+ "### Response:\n",
421+ "\"\"\"\n",
422+ "# {'question': 'Name the comptroller for office of prohibition', 'context': 'CREATE TABLE table_22607062_1 (comptroller VARCHAR, ticket___office VARCHAR)', 'answer': 'SELECT comptroller FROM table_22607062_1 WHERE ticket___office = \"Prohibition\"'}\n",
423+ "model_input = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
424+ "\n",
425+ "model.eval()\n",
426+ "with torch.no_grad():\n",
427+ " print(tokenizer.decode(model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))"
428+ ]
429+ },
430+ {
431+ "cell_type": "markdown",
432+ "metadata": {
433+ "id": "544rmsdU1CSJ"
434+ },
435+ "source": [
436+ "I get the output:\n",
437+ "```\n",
438+ "SELECT * FROM table_name_12 WHERE class > 91.5 AND city_of_license = 'hyannis, nebraska'\n",
439+ "```\n",
440+ "which is clearly wrong if the input is asking for just class!"
441+ ]
442+ },
443+ {
444+ "cell_type": "markdown",
445+ "metadata": {
446+ "id": "puuOXL2R1CSJ"
447+ },
448+ "source": [
449+ "### 4. Tokenization\n",
450+ "Setup some tokenization settings like left padding because it makes [training use less memory](https://ai.stackexchange.com/questions/41485/while-fine-tuning-a-decoder-only-llm-like-llama-on-chat-dataset-what-kind-of-pa):"
451+ ]
452+ },
453+ {
454+ "cell_type": "code",
455+ "execution_count": null,
456+ "metadata": {
457+ "id": "c1P0mphA1CSJ"
458+ },
459+ "outputs": [],
460+ "source": [
461+ "tokenizer.add_eos_token = True\n",
462+ "tokenizer.pad_token_id = 0\n",
463+ "tokenizer.padding_side = \"left\""
464+ ]
465+ },
466+ {
467+ "cell_type": "markdown",
468+ "metadata": {
469+ "id": "kpo66hMo1CSJ"
470+ },
471+ "source": [
472+ "Setup the tokenize function to make labels and input_ids the same. This is basically what [self-supervised fine-tuning](https://neptune.ai/blog/self-supervised-learning) is:"
473+ ]
474+ },
475+ {
476+ "cell_type": "code",
477+ "execution_count": null,
478+ "metadata": {
479+ "id": "gjSerml71CSJ"
480+ },
481+ "outputs": [],
482+ "source": [
483+ "def tokenize(prompt):\n",
484+ " result = tokenizer(\n",
485+ " prompt,\n",
486+ " truncation=True,\n",
487+ " max_length=512,\n",
488+ " padding=False,\n",
489+ " return_tensors=None,\n",
490+ " )\n",
491+ "\n",
492+ " # \"self-supervised learning\" means the labels are also the inputs:\n",
493+ " result[\"labels\"] = result[\"input_ids\"].copy()\n",
494+ "\n",
495+ " return result"
496+ ]
497+ },
498+ {
499+ "cell_type": "markdown",
500+ "metadata": {
501+ "id": "fNk_3THv1CSK"
502+ },
503+ "source": [
504+ "And run convert each data_point into a prompt that I found online that works quite well:"
505+ ]
506+ },
507+ {
508+ "cell_type": "code",
509+ "execution_count": null,
510+ "metadata": {
511+ "id": "MvO7A-ZF1CSK"
512+ },
513+ "outputs": [],
514+ "source": [
515+ "def generate_and_tokenize_prompt(data_point):\n",
516+ " full_prompt =f\"\"\"You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.\n",
517+ "\n",
518+ "You must output the SQL query that answers the question.\n",
519+ "\n",
520+ "### Input:\n",
521+ "{data_point[\"question\"]}\n",
522+ "\n",
523+ "### Context:\n",
524+ "{data_point[\"context\"]}\n",
525+ "\n",
526+ "### Response:\n",
527+ "{data_point[\"answer\"]}\n",
528+ "\"\"\"\n",
529+ " return tokenize(full_prompt)"
530+ ]
531+ },
532+ {
533+ "cell_type": "markdown",
534+ "metadata": {
535+ "id": "sLvwo-ax1CSK"
536+ },
537+ "source": [
538+ "Reformat to prompt and tokenize each sample:"
539+ ]
540+ },
541+ {
542+ "cell_type": "code",
543+ "execution_count": null,
544+ "metadata": {
545+ "colab": {
546+ "base_uri": "https://localhost:8080/",
547+ "height": 49,
548+ "referenced_widgets": [
549+ "9a7571edf1084386868cad49dcd5531f",
550+ "06093dfbb8a94b6bab91a02cb495132f",
551+ "6743ce31107d4a40b8f9c0f2bc3961fc",
552+ "b2584951af494392baa07bd36ad1210b",
553+ "9550d45f688a4d779c44599a60d10cdc",
554+ "878d089839204a6aaedcfdf9187f9962",
555+ "81ac30019317437f91d483808ec40ad8",
556+ "f54162a4f9d04c34b83ae752ababd0fd",
557+ "44d9da3f24694655a994d9336e2c3bee",
558+ "b2b40d850f734a2593cfc911c473a24d",
559+ "7f6456974e2a485e97631e73e6a03fa6"
560+ ]
561+ },
562+ "id": "SA2BqSzW1CSK",
563+ "outputId": "7d1c857f-f063-466b-e37d-f01d89c924b2"
564+ },
565+ "outputs": [],
566+ "source": [
567+ "tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt)\n",
568+ "tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt)"
569+ ]
570+ },
571+ {
572+ "cell_type": "markdown",
573+ "metadata": {
574+ "id": "Z5ByhitV1CSK"
575+ },
576+ "source": [
577+ "### 5. Setup Lora"
578+ ]
579+ },
580+ {
581+ "cell_type": "code",
582+ "execution_count": null,
583+ "metadata": {
584+ "id": "9q_26pz71CSK"
585+ },
586+ "outputs": [],
587+ "source": [
588+ "model.train() # put model back into training mode\n",
589+ "model = prepare_model_for_int8_training(model)\n",
590+ "\n",
591+ "config = LoraConfig(\n",
592+ " r=16,\n",
593+ " lora_alpha=16,\n",
594+ " target_modules=[\n",
595+ " \"q_proj\",\n",
596+ " \"k_proj\",\n",
597+ " \"v_proj\",\n",
598+ " \"o_proj\",\n",
599+ "],\n",
600+ " lora_dropout=0.05,\n",
601+ " bias=\"none\",\n",
602+ " task_type=\"CAUSAL_LM\",\n",
603+ ")\n",
604+ "model = get_peft_model(model, config)"
605+ ]
606+ },
607+ {
608+ "cell_type": "markdown",
609+ "metadata": {
610+ "id": "IvkQ3igC1CSK"
611+ },
612+ "source": [
613+ "To resume from a checkpoint, set resume_from_checkpoint to the path of the adapter_model.bin you want to resume from. This code'll replace the lora adapter attached to the model:"
614+ ]
615+ },
616+ {
617+ "cell_type": "code",
618+ "execution_count": null,
619+ "metadata": {
620+ "id": "TMTxR2JT1CSK"
621+ },
622+ "outputs": [],
623+ "source": [
624+ "resume_from_checkpoint = \"\" # set this to the adapter_model.bin file you want to resume from\n",
625+ "\n",
626+ "if resume_from_checkpoint:\n",
627+ " if os.path.exists(resume_from_checkpoint):\n",
628+ " print(f\"Restarting from {resume_from_checkpoint}\")\n",
629+ " adapters_weights = torch.load(resume_from_checkpoint)\n",
630+ " set_peft_model_state_dict(model, adapters_weights)\n",
631+ " else:\n",
632+ " print(f\"Checkpoint {resume_from_checkpoint} not found\")"
633+ ]
634+ },
635+ {
636+ "cell_type": "markdown",
637+ "metadata": {
638+ "id": "GW1wtvbP1CSK"
639+ },
640+ "source": [
641+ "Optional stuff to setup Weights and Biases to view training graphs:"
642+ ]
643+ },
644+ {
645+ "cell_type": "code",
646+ "execution_count": null,
647+ "metadata": {
648+ "id": "AC1_qWbS1CSK"
649+ },
650+ "outputs": [],
651+ "source": [
652+ "wandb_project = \"sql-try2-coder\"\n",
653+ "if len(wandb_project) > 0:\n",
654+ " os.environ[\"WANDB_PROJECT\"] = wandb_project\n"
655+ ]
656+ },
657+ {
658+ "cell_type": "code",
659+ "execution_count": null,
660+ "metadata": {
661+ "id": "5iv4txxu1CSK"
662+ },
663+ "outputs": [],
664+ "source": [
665+ "if torch.cuda.device_count() > 1:\n",
666+ " # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available\n",
667+ " model.is_parallelizable = True\n",
668+ " model.model_parallel = True"
669+ ]
670+ },
671+ {
672+ "cell_type": "markdown",
673+ "metadata": {
674+ "id": "pYSsnciQ1CSK"
675+ },
676+ "source": [
677+ "### 6. Training arguments\n",
678+ "If you run out of GPU memory, change per_device_train_batch_size. The gradient_accumulation_steps variable should ensure this doesn't affect batch dynamics during the training run. All the other variables are standard stuff that I wouldn't recommend messing with:"
679+ ]
680+ },
681+ {
682+ "cell_type": "code",
683+ "execution_count": null,
684+ "metadata": {
685+ "id": "uvEi1kP21CSK"
686+ },
687+ "outputs": [],
688+ "source": [
689+ "batch_size = 128\n",
690+ "per_device_train_batch_size = 32\n",
691+ "gradient_accumulation_steps = batch_size // per_device_train_batch_size\n",
692+ "output_dir = \"sql-code-llama\"\n",
693+ "\n",
694+ "training_args = TrainingArguments(\n",
695+ " per_device_train_batch_size=per_device_train_batch_size,\n",
696+ " gradient_accumulation_steps=gradient_accumulation_steps,\n",
697+ " warmup_steps=100,\n",
698+ " max_steps=400,\n",
699+ " learning_rate=3e-4,\n",
700+ " fp16=True,\n",
701+ " logging_steps=10,\n",
702+ " optim=\"adamw_torch\",\n",
703+ " evaluation_strategy=\"steps\", # if val_set_size > 0 else \"no\",\n",
704+ " save_strategy=\"steps\",\n",
705+ " eval_steps=20,\n",
706+ " save_steps=20,\n",
707+ " output_dir=output_dir,\n",
708+ " # save_total_limit=3,\n",
709+ " load_best_model_at_end=False,\n",
710+ " # ddp_find_unused_parameters=False if ddp else None,\n",
711+ " group_by_length=True, # group sequences of roughly the same length together to speed up training\n",
712+ " report_to=\"wandb\", # if use_wandb else \"none\",\n",
713+ " run_name=f\"codellama-{datetime.now().strftime('%Y-%m-%d-%H-%M')}\", # if use_wandb else None,\n",
714+ " )\n",
715+ "\n",
716+ "trainer = Trainer(\n",
717+ " model=model,\n",
718+ " train_dataset=tokenized_train_dataset,\n",
719+ " eval_dataset=tokenized_val_dataset,\n",
720+ " args=training_args,\n",
721+ " data_collator=DataCollatorForSeq2Seq(\n",
722+ " tokenizer, pad_to_multiple_of=8, return_tensors=\"pt\", padding=True\n",
723+ " ),\n",
724+ ")"
725+ ]
726+ },
727+ {
728+ "cell_type": "markdown",
729+ "metadata": {
730+ "id": "1aJp6Jxl1CSK"
731+ },
732+ "source": [
733+ "Then we do some pytorch-related optimisation (which just make training faster but don't affect accuracy):"
734+ ]
735+ },
736+ {
737+ "cell_type": "code",
738+ "execution_count": null,
739+ "metadata": {
740+ "id": "6ycCHZZl1CSK"
741+ },
742+ "outputs": [],
743+ "source": [
744+ "model.config.use_cache = False\n",
745+ "\n",
746+ "old_state_dict = model.state_dict\n",
747+ "model.state_dict = (lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())).__get__(\n",
748+ " model, type(model)\n",
749+ ")\n",
750+ "if torch.__version__ >= \"2\" and sys.platform != \"win32\":\n",
751+ " print(\"compiling the model\")\n",
752+ " model = torch.compile(model)"
753+ ]
754+ },
755+ {
756+ "cell_type": "code",
757+ "execution_count": null,
758+ "metadata": {
759+ "id": "bF5oWKxK1CSL"
760+ },
761+ "outputs": [],
762+ "source": [
763+ "trainer.train()"
764+ ]
765+ },
766+ {
767+ "cell_type": "markdown",
768+ "metadata": {
769+ "id": "j1dRQLMT1CSU"
770+ },
771+ "source": [
772+ "### Load the final checkpoint\n",
773+ "Now for the moment of truth! Has our work paid off...?"
774+ ]
775+ },
776+ {
777+ "cell_type": "code",
778+ "execution_count": null,
779+ "metadata": {
780+ "id": "sRdVgDTg1CSU"
781+ },
782+ "outputs": [],
783+ "source": [
784+ "import torch\n",
785+ "from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer\n",
786+ "\n",
787+ "base_model = \"codellama/CodeLlama-7b-hf\"\n",
788+ "model = AutoModelForCausalLM.from_pretrained(\n",
789+ " base_model,\n",
790+ " load_in_8bit=True,\n",
791+ " torch_dtype=torch.float16,\n",
792+ " device_map=\"auto\",\n",
793+ ")\n",
794+ "tokenizer = AutoTokenizer.from_pretrained(\"codellama/CodeLlama-7b-hf\")"
795+ ]
796+ },
797+ {
798+ "cell_type": "markdown",
799+ "metadata": {
800+ "id": "76UlHzhy1CSU"
801+ },
802+ "source": [
803+ "To load a fine-tuned Lora/Qlora adapter use PeftModel.from_pretrained. ```output_dir``` should be something containing an adapter_config.json and adapter_model.bin:"
804+ ]
805+ },
806+ {
807+ "cell_type": "code",
808+ "execution_count": null,
809+ "metadata": {
810+ "id": "rQrCR0os1CSU"
811+ },
812+ "outputs": [],
813+ "source": [
814+ "from peft import PeftModel\n",
815+ "model = PeftModel.from_pretrained(model, output_dir)"
816+ ]
817+ },
818+ {
819+ "cell_type": "markdown",
820+ "metadata": {
821+ "id": "roqy_WRi1CSU"
822+ },
823+ "source": [
824+ "Try the same prompt as before:"
825+ ]
826+ },
827+ {
828+ "cell_type": "code",
829+ "execution_count": null,
830+ "metadata": {
831+ "id": "GrCxouNp1CSU"
832+ },
833+ "outputs": [],
834+ "source": [
835+ "eval_prompt = \"\"\"You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.\n",
836+ "\n",
837+ "You must output the SQL query that answers the question.\n",
838+ "### Input:\n",
839+ "Which Class has a Frequency MHz larger than 91.5, and a City of license of hyannis, nebraska?\n",
840+ "\n",
841+ "### Context:\n",
842+ "CREATE TABLE table_name_12 (class VARCHAR, frequency_mhz VARCHAR, city_of_license VARCHAR)\n",
843+ "\n",
844+ "### Response:\n",
845+ "\"\"\"\n",
846+ "\n",
847+ "model_input = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
848+ "\n",
849+ "model.eval()\n",
850+ "with torch.no_grad():\n",
851+ " print(tokenizer.decode(model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))\n"
852+ ]
853+ },
854+ {
855+ "cell_type": "markdown",
856+ "metadata": {
857+ "id": "I6IcTOCq1CSU"
858+ },
859+ "source": [
860+ "And the model outputs:\n",
861+ "```\n",
862+ "SELECT class FROM table_name_12 WHERE frequency_mhz > 91.5 AND city_of_license = \"hyannis, nebraska\"\n",
863+ "```\n",
864+ "So it works! If you want to convert your this adapter to a Llama.cpp model to run locally follow my other [guide](https://ragntune.com/blog/A-guide-to-running-Llama-2-qlora-loras-on-Llama.cpp). If you have any questions, shoot me a message on [Elon Musk's website](https://twitter.com/samlhuillier_).\n"
865+ ]
866+ }
867+ ],
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