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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "db4208b9-5da4-46df-b77a-0f1836c9e4ec",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/raid/sourab/transformers/src/transformers/utils/hub.py:122: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
" warnings.warn(\n",
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
}
],
"source": [
"import os\n",
"\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
"from peft import PeftConfig, PeftModel\n",
"from peft import PeftModel, PeftConfig\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"from datasets import load_dataset\n",
"import torch\n",
"import random\n",
"\n",
"peft_model_id = \"smangrul/tinyllama_lora_norobots\"\n",
"device = \"cuda\"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_4bit=True, device_map=\"auto\")\n",
"tokenizer = AutoTokenizer.from_pretrained(peft_model_id)\n",
"model.resize_token_embeddings(len(tokenizer))\n",
"model = PeftModel.from_pretrained(model, peft_model_id, adapter_name=\"norobots\")\n",
"_ = model.load_adapter(\"smangrul/tinyllama_lora_sql\", adapter_name=\"sql\")\n",
"_ = model.load_adapter(\"smangrul/tinyllama_lora_adcopy\", adapter_name=\"adcopy\")"
]
},
{
"cell_type": "code",
"execution_count": 684,
"id": "541dab43-9675-42a2-8d90-7437df9f0fa0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 23.3 s, sys: 535 ms, total: 23.8 s\n",
"Wall time: 796 ms\n"
]
}
],
"source": [
"%%time\n",
"# [0.8, 0.1, 0.1] linear #[1.0, 0.2] 0.7 density dare_linear #[1.5, 0.3] 0.5 density ties #[0.8, 0.5] cat\n",
"adapters = [\"norobots\", \"adcopy\", \"sql\"]\n",
"weights = [2.0, 0.3, 0.7]\n",
"adapter_name = \"merge\"\n",
"density = 0.2\n",
"combination_type = \"ties\"\n",
"if adapter_name in model.peft_config:\n",
" model.delete_adapter(adapter_name)\n",
"model.add_weighted_adapter(adapters, weights, adapter_name, combination_type=combination_type, density=density)"
]
},
{
"cell_type": "code",
"execution_count": 685,
"id": "76596671-3677-47f0-9d66-81f40bc4d726",
"metadata": {},
"outputs": [],
"source": [
"model.eval()\n",
"model.set_adapter(\"merge\")"
]
},
{
"cell_type": "code",
"execution_count": 691,
"id": "9d59f9f3-6313-43d8-be36-4ca2bbb105b2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<s><|im_start|>user \n",
"Write an essay about Generative AI.<|im_end|> \n",
"<|im_start|>assistant \n",
"Generative Artificial Intelligence (GAI) is a type of artificial intelligence that uses machine learning to create art, music and other creations. It's like having a human artist who creates something new without the need for inspiration or motivation.<|im_end|>\n"
]
}
],
"source": [
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Write an essay about Generative AI.\"},\n",
"]\n",
"text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)\n",
"inputs = tokenizer(text, return_tensors=\"pt\") # , add_special_tokens=False)\n",
"inputs = {k: v.to(\"cuda\") for k, v in inputs.items()}\n",
"outputs = model.generate(\n",
" **inputs,\n",
" max_new_tokens=256,\n",
" do_sample=True,\n",
" top_p=0.95,\n",
" temperature=0.2,\n",
" repetition_penalty=1.2,\n",
" eos_token_id=tokenizer.eos_token_id,\n",
")\n",
"print(tokenizer.decode(outputs[0]))"
]
},
{
"cell_type": "code",
"execution_count": 689,
"id": "e5c1daeb-59c8-41d7-bebb-7abd052ab917",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<s><|im_start|>system \n",
"Create a text ad given the following product and description.<|im_end|> \n",
"<|im_start|>user \n",
"Product: Sony PS5 PlayStation Console\n",
"Description: The PS5™ console unleashes new gaming possibilities that you never anticipated.<|im_end|> \n",
"<|im_start|>assistant \n",
"Ad Text: Experience the next-gen power of the all-new Sony PS5 with its stunning visuals, innovative gameplay features, and more! Get ready to play in style as you experience the future of gaming on your own terms.<|im_end|>\n"
]
}
],
"source": [
"messages = [\n",
" {\"role\": \"system\", \"content\": \"Create a text ad given the following product and description.\"},\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"Product: Sony PS5 PlayStation Console\\nDescription: The PS5™ console unleashes new gaming possibilities that you never anticipated.\",\n",
" },\n",
"]\n",
"text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)\n",
"inputs = tokenizer(text, return_tensors=\"pt\") # , add_special_tokens=False)\n",
"inputs = {k: v.to(\"cuda\") for k, v in inputs.items()}\n",
"outputs = model.generate(\n",
" **inputs,\n",
" max_new_tokens=128,\n",
" do_sample=True,\n",
" top_p=0.95,\n",
" temperature=0.2,\n",
" repetition_penalty=1.2,\n",
" eos_token_id=tokenizer.eos_token_id,\n",
")\n",
"print(tokenizer.decode(outputs[0]))"
]
},
{
"cell_type": "code",
"execution_count": 690,
"id": "5bb08b46-90ae-48a8-8783-ca74b3e26e42",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<s> Table: 2-11365528-2\n",
"Columns: ['Team', 'Head Coach', 'President', 'Home Ground', 'Location']\n",
"Natural Query: Who is the Head Coach of the team whose President is Mario Volarevic?\n",
"SQL Query: SELECT Head Coach FROM 2-11365528-2 WHERE President = Mario Volarevic</s>\n"
]
}
],
"source": [
"text = \"\"\"Table: 2-11365528-2\n",
"Columns: ['Team', 'Head Coach', 'President', 'Home Ground', 'Location']\n",
"Natural Query: Who is the Head Coach of the team whose President is Mario Volarevic?\n",
"SQL Query:\"\"\"\n",
"\n",
"inputs = tokenizer(text, return_tensors=\"pt\") # , add_special_tokens=False)\n",
"inputs = {k: v.to(\"cuda\") for k, v in inputs.items()}\n",
"outputs = model.generate(\n",
" **inputs, max_new_tokens=64, repetition_penalty=1.1, eos_token_id=tokenizer(\"</s>\").input_ids[-1]\n",
")\n",
"print(tokenizer.decode(outputs[0]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc927536-bb58-4270-876f-10ff1a94802d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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