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Apply Quantization on my Llama-2 7B Fine-tuned Python Code Generator using GPTQ with AutoGPT and Huggingface"],"metadata":{"id":"6oG8K3rUljhU"}},{"cell_type":"markdown","source":["In my previous [notebook](https://github.com/edumunozsala/llama-2-7B-4bit-python-coder/blob/main/Llama-2-finetune-qlora-python-coder.ipynb), I showed you how to instruction fine-tune the new Llama 2 model, just released by Meta AI, to build a Python code generator in a few lines of code. This time we will describe how to quantize this model using the GPTQ quantization, now that it is integrated with the transformers.\n","\n","Last week Hugging Face Hugging Face announced the compatibility of its transformers libraries with the AutoGPTQ library which allows us to quantize a large language model in 2, 3, or 4 bits using the GPTQ methodology"],"metadata":{"id":"ydi4YePTgDId"}},{"cell_type":"markdown","source":["This notebook contains three sections:\n","- Load the original model, fine-tuned with QLoRa in 4-bits, and evaluate the performance when runing on a T4 instance in Colab.\n","- Quantize this original model to 4 bits using GPTQ with transformers and AUTOGPT and save it to Hugging Face Hub.\n","- Load the quantized model and evaluate the performance in a T4."],"metadata":{"id":"5PLimQ1dgyGJ"}},{"cell_type":"markdown","source":["## Load the Llama-2 7B model in 4-bit without AutoGPTQ"],"metadata":{"id":"ombnYtn0nAzA"}},{"cell_type":"markdown","source":["### Installing the libraries"],"metadata":{"id":"BrH2vBhBgveG"}},{"cell_type":"code","source":["! pip install \"transformers==4.31.0\" \"bitsandbytes==0.40.2\" \"accelerate==0.21.0\""],"metadata":{"id":"2z5ncbjtpHA3"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["We load the model in 4 bits with bitsandbytes quantization"],"metadata":{"id":"GgRRvvLUhq_A"}},{"cell_type":"code","source":["import torch\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","\n","# Load the entire model on the GPU 0\n","device_map = {\"\": 0}\n","\n","# If not defined\n","hf_model_repo='edumunozsala/llama-2-7b-int4-python-code-20k'\n","\n","# Get the tokenizer\n","tokenizer = AutoTokenizer.from_pretrained(hf_model_repo)\n","# Load the model\n","model = AutoModelForCausalLM.from_pretrained(hf_model_repo, load_in_4bit=True, torch_dtype=torch.float16,\n","                                             device_map=device_map)\n"],"metadata":{"id":"wPnSh37zm9Jw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Lets check the memory usage"],"metadata":{"id":"oBLuojWSntwa"}},{"cell_type":"code","source":["!nvidia-smi"],"metadata":{"id":"za8UeVc5nwqm"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Test Inference performance"],"metadata":{"id":"MCzBjFaWT4KJ"}},{"cell_type":"markdown","source":["Install the dataset library"],"metadata":{"id":"82nMdE8rinU0"}},{"cell_type":"code","source":["!pip install \"datasets==2.13.0\""],"metadata":{"id":"91cYIH-tT_sM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from datasets import load_dataset\n","from random import randrange\n","import time"],"metadata":{"id":"eiVpSg50UGQb"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Load the dataset with the instruction set"],"metadata":{"id":"HJjE6hP3vt_Z"}},{"cell_type":"markdown","source":["First, we load the dataset from huggingface. The dataset is [python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)\n","\n","The dataset contains problem descriptions and code in python language. This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style.\n"],"metadata":{"id":"xC4odqJTh7qO"}},{"cell_type":"code","source":["# The instruction dataset to use\n","dataset_name = \"iamtarun/python_code_instructions_18k_alpaca\"\n","# Load dataset from the hub\n","dataset = load_dataset(dataset_name, split=\"train[:10%]\")\n","# Show dataset size\n","print(f\"dataset size: {len(dataset)}\")\n","# Show an example\n","print(dataset[randrange(len(dataset))])\n"],"metadata":{"id":"qbbH23N9vXh2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Check the dataset structure\n","print(dataset)"],"metadata":{"id":"uQTTAnGnHG8A"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["We would apply inference on those examples longer than 500 characters:"],"metadata":{"id":"xlg-AcdEdMo-"}},{"cell_type":"code","source":["# Sample the dataset\n","#sample_dataset= dataset[randrange(0,100,5)]\n","# Filter the dataset to get those which instruction is longer than 100 characters\n","responses_test = dataset.filter(lambda x: len(x['instruction'])>500)\n","\n","print(len(responses_test))"],"metadata":{"id":"R2UTHzWpU28O"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Now, we run a for loop where we make inference and collect execution time and the result"],"metadata":{"id":"alWCABfToHQV"}},{"cell_type":"code","source":["# Process the test examples\n","times = []\n","solutions = []\n","\n","for example in responses_test:\n","  start = time.time()\n","  prompt = f\"\"\"### Instruction:\n","  Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.\n","\n","  ### Task:\n","  {example[\"instruction\"]}\n","\n","  ### Input:\n","  {example[\"input\"]}\n","\n","  ### Response:\n","  \"\"\"\n","  # Tokenize the input\n","  input_ids = tokenizer(prompt, return_tensors=\"pt\", truncation=True).input_ids.cuda()\n","  # Run the model to infere an output\n","  outputs = model.generate(input_ids=input_ids, max_new_tokens=200, do_sample=True, top_p=0.9,temperature=0.3)\n","\n","  # Collect the output and the inference time\n","  end = time.time()\n","  print(start-end)\n","  times.append(start-end)\n","  solutions.append(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):])\n","\n","print(times)"],"metadata":{"id":"LA7Q041vYo-8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["As a summary, we show the max, min and mean execution time"],"metadata":{"id":"2Ax-QwXDi38I"}},{"cell_type":"code","source":["import numpy as np"],"metadata":{"id":"8p8JEEdBi-5X"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print('Min time: ',np.min(times))\n","print('Max time: ',np.max(times))\n","print('Mean time: ',np.mean(times))"],"metadata":{"id":"4x9vPDbwjCjb"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["In average, **every inference takes about 17 - 19 seconds**."],"metadata":{"id":"rpaffhEYs98C"}},{"cell_type":"markdown","source":["## Quantize transformers model using auto-gptq, 🤗 transformers and optimum\n","\n","The GPTQ algorithm requires to calibrate the quantized weights of the model by doing inference on the quantized model. The detailed quantization algorithm is described in [the original paper](https://arxiv.org/pdf/2210.17323.pdf).\n","\n","For quantizing a model using auto-gptq, we need to pass a dataset to the quantizer. This can be achieved either by passing a supported default dataset among `['wikitext2','c4','c4-new','ptb','ptb-new']` or a list of strings that will be used as a dataset."],"metadata":{"id":"SK1eZ_VrvFdA"}},{"cell_type":"markdown","source":["Installing the libraries"],"metadata":{"id":"tY-aR_fJm0Po"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"dqp-C_NxlgX2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693808928283,"user_tz":-120,"elapsed":14749,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"9c4ab709-3ef7-43e9-c8cc-9186269ad0dd"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/7.5 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.1/7.5 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta 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an explicitd version, but probably you can run the simple installation"],"metadata":{"id":"dOhWKjR3jGJN"}},{"cell_type":"code","source":["!pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu117/\n","#!pip install auto-gptq"],"metadata":{"id":"M4b0vdcymUaa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693808944125,"user_tz":-120,"elapsed":6324,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"bae1be82-6763-4645-cf20-238d22397f5c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://huggingface.github.io/autogptq-index/whl/cu117/\n","Collecting auto-gptq\n","  Downloading https://huggingface.github.io/autogptq-index/whl/cu117/auto-gptq/auto_gptq-0.4.2%2Bcu117-cp310-cp310-linux_x86_64.whl (1.6 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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(2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->auto-gptq) (2023.3)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->auto-gptq) (1.3.0)\n","Installing collected packages: rouge, auto-gptq\n","Successfully installed auto-gptq-0.4.2+cu117 rouge-1.0.1\n"]}]},{"cell_type":"markdown","source":["In this example, we will try to quantize the model in 4-bit precision using the `\"c4\"` dataset and the tokenizer from our model. Supported precisions are `[2, 4, 6, 8]`. The other parameters takes the default value."],"metadata":{"id":"PVUwcX54vjYO"}},{"cell_type":"code","source":["from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig\n","import torch\n","\n","# Set the model to load\n","hf_model_repo='edumunozsala/llama-2-7b-int4-python-code-20k'\n","# Load the tokenizer\n","tokenizer = AutoTokenizer.from_pretrained(hf_model_repo, use_fast=True)\n","# Set quantization configuration\n","quantization_config = GPTQConfig(\n","     bits=4,\n","     group_size=128,\n","     dataset=\"c4\",\n","     desc_act=False,\n","     tokenizer=tokenizer\n",")\n","# Load the model from HF\n","quant_model = AutoModelForCausalLM.from_pretrained(hf_model_repo, quantization_config=quantization_config, 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?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"478ef91646ad485eaceb12cd1c7553a8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"624063323ccf419384464407d5b15b79"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5f07f1e39ef04fe780c41bad62765023"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d5007f25de30471a9e2a21d38ba485be"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ee03027bdb8a40f6a16e7eebeab5454a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"257f3cf8c2a44635b210f0bbc126c2bc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"79d4d6721de2446dbb5d6afa1ae8df80"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e0a842e621924ed6854ea0979a3ed5c2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"880a074ad4254a5a817c708cc3d7df5b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e09afe5eb3d24a4aa2c3af4f055cd991"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e9101f8cc981489f9aac12b403bbdba2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0c88e1815d5b4f6fa3368945784ae774"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Quantizing layers inside the block:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"167fd2b050bf4f1492ce719028573534"}},"metadata":{}}]},{"cell_type":"markdown","source":["The parameters defined:\n","\n","- **bits**: Precision of the quantization. You can set 4, 3, and 2.\n","- **dataset**: The dataset used for calibration. I would leave “c4“ which seems to yield reasonable results. Other datasets are supported according to the documentation.\n","- **desc_act**: Whether to quantize columns in order of decreasing activation size. Setting it to False can significantly speed up inference but the perplexity may become slightly worse. Also known as act-order.\n","- **tokenizer**: The tokenizer of Llama 2 7B that will be applied to c4."],"metadata":{"id":"MYBrlEz7wtl4"}},{"cell_type":"markdown","source":["Lets check the memory usage"],"metadata":{"id":"_TYmvPKGwEsJ"}},{"cell_type":"code","source":["!nvidia-smi"],"metadata":{"id":"nrZg5CoKwEsJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693811829216,"user_tz":-120,"elapsed":12,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"c8335f51-7143-4913-9d5a-ea26f7798dca"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mon Sep  4 07:17:10 2023       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   33C    P0    49W / 400W |  27249MiB / 40960MiB |      0%      Default |\n","|                               |                      |             Disabled |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","+-----------------------------------------------------------------------------+\n"]}]},{"cell_type":"markdown","source":["You can make sure the model has been correctly quantized by checking the attributes of the linear layers, they should contain `qweight` and `qzeros` attributes that should be in `torch.int32` dtype."],"metadata":{"id":"1NP3sd8X-QEo"}},{"cell_type":"code","source":["quant_model.model.decoder.layers[0].self_attn.q_proj.__dict__"],"metadata":{"id":"sJRv3bGyZtcB"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Now, we run a simple example"],"metadata":{"id":"-Kk5BlKFxdMx"}},{"cell_type":"code","source":["# Create an instruction\n","instruction=\"Write a Python function to display the first and last elements of a list.\"\n","input=\"\"\n","\n","prompt = f\"\"\"### Instruction:\n","Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.\n","\n","### Task:\n","{instruction}\n","\n","### Input:\n","{input}\n","\n","### Response:\n","\"\"\"\n","# Tokenize the input\n","input_ids = tokenizer(prompt, return_tensors=\"pt\", truncation=True).input_ids.cuda()\n","# Run the model to infere an output\n","outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.5)\n","\n","# Print the result\n","print(f\"Prompt:\\n{prompt}\\n\")\n","print(f\"Generated instruction:\\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}\")"],"metadata":{"id":"tWCxXGsuxdMy"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Share quantized models on 🤗 Hub"],"metadata":{"id":"QRQvt-zr_Neh"}},{"cell_type":"markdown","source":["After quantizing the model, it can be used out-of-the-box for inference or you can push the quantized weights on the 🤗 Hub to share your quantized model with the community"],"metadata":{"id":"iI5DICq-rQ8o"}},{"cell_type":"code","source":["!pip install python-dotenv\n","!pip install huggingface_hub"],"metadata":{"id":"MZFl7C5HxnzT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693811881608,"user_tz":-120,"elapsed":9624,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"0572d0ca-bca3-4752-e07e-3dca44afbcf3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting python-dotenv\n","  Downloading python_dotenv-1.0.0-py3-none-any.whl (19 kB)\n","Installing collected packages: python-dotenv\n","Successfully installed python-dotenv-1.0.0\n","Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.16.4)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.2)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n","Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.7.1)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.1)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.2.0)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.7.22)\n"]}]},{"cell_type":"markdown","source":["You can log in to Hugging Face Hub interactively"],"metadata":{"id":"Q2d87e0cx7W-"}},{"cell_type":"code","source":["from huggingface_hub import notebook_login\n","# Log in to HF Hub\n","notebook_login()\n"],"metadata":{"id":"W21qQZgMx-Xq"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Or you can provide .env file containing the Hugging Face token"],"metadata":{"id":"IKAk98tTyAJZ"}},{"cell_type":"code","source":["from huggingface_hub import login\n","from dotenv import load_dotenv\n","import os\n","\n","# Load the enviroment variables\n","load_dotenv()\n","# Login to the Hugging Face Hub\n","login(token=os.getenv(\"HF_HUB_TOKEN\"))"],"metadata":{"id":"H1G5E1Mgxz9l","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693811915017,"user_tz":-120,"elapsed":1268,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"4d99d48f-0399-48b4-95f0-4d65bdf8526f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n","Token is valid (permission: write).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}]},{"cell_type":"markdown","source":["Now, we can upload our quantize model to the Hugging Face Hub"],"metadata":{"id":"aZimqSZcybrq"}},{"cell_type":"code","source":["quant_model.push_to_hub(\"edumunozsala/llama-2-7b-int4-GPTQ-python-code-20k\")\n","tokenizer.push_to_hub(\"edumunozsala/llama-2-7b-int4-GPTQ-python-code-20k\")"],"metadata":{"id":"MEWp3ZdLrn6E","colab":{"base_uri":"https://localhost:8080/","height":101,"referenced_widgets":["de8908163af442a29ab7d32198098b85","3e2c36fff1284b91851be179deaa50a5","d722584f6ded43669e53c2a09867a83f","db656128395b458899c8395fa5d57196","4599b61ec6b54ce19c2193cdf5d77c26","aca0715471174477ab876a124272e707","da8afde37c684957ad107227f21f4e07","354a52feef2d4af481555dbd935f3bc5","2c14e49c96e143ebb555c78e014bcb6d","48f8d36007084dbd9fdd05d475f05066","743b827b57ef4f80967d1bab6ce95e26"]},"executionInfo":{"status":"ok","timestamp":1693812328555,"user_tz":-120,"elapsed":397276,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"4b846119-2b9b-4d68-d85e-2b1a4efb844e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["pytorch_model.bin:   0%|          | 0.00/3.90G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"de8908163af442a29ab7d32198098b85"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["CommitInfo(commit_url='https://huggingface.co/edumunozsala/llama-2-7b-int4-GPTQ-python-code-20k/commit/ed458634c5162e5d755532fb10177864b3fa3b5c', commit_message='Upload tokenizer', commit_description='', oid='ed458634c5162e5d755532fb10177864b3fa3b5c', pr_url=None, pr_revision=None, pr_num=None)"]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","source":["## Loading the GPTQ Model from Hugging Face Hub and run an example"],"metadata":{"id":"yzf8W8xDLHn5"}},{"cell_type":"markdown","source":["As usual, we import the libraries and load the tokenizer and quantized model"],"metadata":{"id":"TpHW8v4AMyT9"}},{"cell_type":"markdown","source":["Installing the libraries"],"metadata":{"id":"8zrmkZJSO7R4"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"eByzTMmaO7R5"},"outputs":[],"source":["!pip install -q -U transformers peft accelerate optimum"]},{"cell_type":"code","source":["#!pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu117/\n","!pip install auto-gptq"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693848513011,"user_tz":-120,"elapsed":4989,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"3df9a48c-e6bd-43bb-a580-427fce4d69d6","id":"w9WD16JMO7R5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: auto-gptq in /usr/local/lib/python3.10/dist-packages (0.4.2)\n","Requirement already satisfied: 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aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->auto-gptq) (1.3.1)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.31.0->auto-gptq) (3.4)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.31.0->auto-gptq) (2.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.31.0->auto-gptq) (2023.7.22)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->auto-gptq) (2.1.3)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->auto-gptq) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->auto-gptq) (2023.3)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->auto-gptq) (1.3.0)\n"]}]},{"cell_type":"markdown","source":["Load the model, this time we do not need to especify any quantization mode. The model is already quantized, you can load models that have been quantized using the auto-gptq library out of the box from the 🤗 Hub directly using `from_pretrained` method.\n"],"metadata":{"id":"o8HV79U6lEeF"}},{"cell_type":"code","source":["import torch\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","\n","model_id = \"edumunozsala/llama-2-7b-int4-GPTQ-python-code-20k\"\n","\n","tokenizer = AutoTokenizer.from_pretrained(model_id)\n","\n","model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map=\"auto\")\n"],"metadata":{"id":"XLw90ireJzX7","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["bc5548608d40475a9992c9e2d5c3d60d","aa5a07f1a8df404e891df926d519deff","dea791fc7f4c4e64aa64465158fc72e2","18a91565a3f74fcda5eada8408945f38","1bebe6d8a480470386a948618bd081cc","9509be16f7764b30b1405023f1c57158","c04cfa5142c747698a70aa40ee53740f","35fd71e55e6d4c6fac4435712afc81bf","bbb55d80eea04b31a5ae17cb917c4daa","2e1f735d1f0047668a727f0950d75991","74a13736882c4eb6843a25bfdc3bb84a","46208757fef8410893f3e662591eed38","42e53395b9694e579a0e0961b2b20525","ea7934029dc94ca0a0e2f693e174e857","7a3af8a63c304e4cbc09f88003097d95","4537ff2497444c1fbe0dc24ab45751a9","95bbfab71f754dabab5663ba5385c489","a8ec3062e2a448ab8a2c7cfb95b53b6a","52a760a3455a410999574fa3ac53b5ad","3548c9b5d139472aa745d3e664b9a564","9ab6f14af2bf44a0b32f509e875f9f09","87131d92b5c3449db2797740c3dc1fb5"]},"executionInfo":{"status":"ok","timestamp":1693848611689,"user_tz":-120,"elapsed":37337,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"6c16a703-7ec2-440e-f637-2d94fecf4c6f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/3.90G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bc5548608d40475a9992c9e2d5c3d60d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/132 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"46208757fef8410893f3e662591eed38"}},"metadata":{}}]},{"cell_type":"markdown","source":["Once tokenizer and model has been loaded, let's generate some text. Before that, we can inspect the model to make sure it has loaded a quantized model, you can see the `QuantLinear()` layers."],"metadata":{"id":"64Q1kLX5g_Et"}},{"cell_type":"code","source":["print(model)"],"metadata":{"id":"bOigq7eFl6vq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693813219430,"user_tz":-120,"elapsed":377,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"e6c93f70-3d6f-41a3-d2ee-c9e9a285f741"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["LlamaForCausalLM(\n","  (model): LlamaModel(\n","    (embed_tokens): Embedding(32000, 4096, padding_idx=0)\n","    (layers): ModuleList(\n","      (0-31): 32 x LlamaDecoderLayer(\n","        (self_attn): LlamaAttention(\n","          (rotary_emb): LlamaRotaryEmbedding()\n","          (k_proj): QuantLinear()\n","          (o_proj): QuantLinear()\n","          (q_proj): QuantLinear()\n","          (v_proj): QuantLinear()\n","        )\n","        (mlp): LlamaMLP(\n","          (act_fn): SiLUActivation()\n","          (down_proj): QuantLinear()\n","          (gate_proj): QuantLinear()\n","          (up_proj): QuantLinear()\n","        )\n","        (input_layernorm): LlamaRMSNorm()\n","        (post_attention_layernorm): LlamaRMSNorm()\n","      )\n","    )\n","    (norm): LlamaRMSNorm()\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",")\n"]}]},{"cell_type":"markdown","source":["Lets check the GPU VRAM"],"metadata":{"id":"K-MAqYsSPyaP"}},{"cell_type":"code","source":["!nvidia-smi"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZnLTJh6gNIPO","executionInfo":{"status":"ok","timestamp":1693848639261,"user_tz":-120,"elapsed":419,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"70fd1836-e298-4d8e-f91b-230421c03627"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mon Sep  4 17:30:38 2023       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   45C    P0    29W /  70W |   5053MiB / 15360MiB |      0%      Default |\n","|                               |                      |                  N/A |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","+-----------------------------------------------------------------------------+\n"]}]},{"cell_type":"markdown","source":["Then we run an inference to test it"],"metadata":{"id":"nyT2wtYVNDKA"}},{"cell_type":"code","source":["instruction=\"Write a Python function to display the first and last elements of a list.\"\n","input=\"\"\n","\n","prompt = f\"\"\"### Instruction:\n","Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.\n","\n","### Task:\n","{instruction}\n","\n","### Input:\n","{input}\n","\n","### Response:\n","\"\"\"\n","\n","input_ids = tokenizer(prompt, return_tensors=\"pt\", truncation=True).input_ids.cuda()\n","# with torch.inference_mode():\n","outputs = model.generate(input_ids=input_ids, max_new_tokens=128, do_sample=True, top_p=0.9,temperature=0.3)\n","\n","print(f\"Prompt:\\n{prompt}\\n\")\n","print(f\"Generated instruction:\\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O-AyryEHM_8a","executionInfo":{"status":"ok","timestamp":1693848677389,"user_tz":-120,"elapsed":5413,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"b95319f9-2f21-4594-af26-90dab044597a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"]},{"output_type":"stream","name":"stdout","text":["Prompt:\n","### Instruction:\n","Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.\n","\n","### Task:\n","Write a Python function to display the first and last elements of a list.\n","\n","### Input:\n","\n","\n","### Response:\n","\n","\n","Generated instruction:\n","def first_last(list):\n","    first = list[0]\n","    last = list[-1]\n","    return first, last\n","\n"]}]},{"cell_type":"markdown","source":["Everything looks fine."],"metadata":{"id":"wyxTDy43lf7q"}},{"cell_type":"markdown","source":["## Test Inference performance"],"metadata":{"id":"wvn1vRa8w1xh"}},{"cell_type":"code","source":["!pip install \"datasets==2.13.0\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"cHeeU887w1xi","executionInfo":{"status":"ok","timestamp":1693813269383,"user_tz":-120,"elapsed":6168,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"18f836bc-6402-44bc-c1b3-6d4f18e614ca"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting datasets==2.13.0\n","  Downloading datasets-2.13.0-py3-none-any.whl (485 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m485.6/485.6 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets==2.13.0) (1.23.5)\n","Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets==2.13.0) (9.0.0)\n","Collecting dill<0.3.7,>=0.3.0 (from datasets==2.13.0)\n","  Downloading dill-0.3.6-py3-none-any.whl (110 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta 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/usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets==2.13.0) (2023.7.22)\n","INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n","Collecting multiprocess (from datasets==2.13.0)\n","  Downloading multiprocess-0.70.14-py310-none-any.whl (134 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets==2.13.0) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets==2.13.0) (2023.3)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets==2.13.0) (1.16.0)\n","Installing collected packages: dill, multiprocess, datasets\n","  Attempting uninstall: dill\n","    Found existing installation: dill 0.3.7\n","    Uninstalling dill-0.3.7:\n","      Successfully uninstalled dill-0.3.7\n","  Attempting uninstall: multiprocess\n","    Found existing installation: multiprocess 0.70.15\n","    Uninstalling multiprocess-0.70.15:\n","      Successfully uninstalled multiprocess-0.70.15\n","  Attempting uninstall: datasets\n","    Found existing installation: datasets 2.14.4\n","    Uninstalling datasets-2.14.4:\n","      Successfully uninstalled datasets-2.14.4\n","Successfully installed datasets-2.13.0 dill-0.3.6 multiprocess-0.70.14\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["_multiprocess","datasets","dill","multiprocess"]}}},"metadata":{}}]},{"cell_type":"code","source":["from datasets import load_dataset\n","from random import randrange\n","import time"],"metadata":{"id":"L4qhIgsBw1xi"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Load the dataset with the instruction set"],"metadata":{"id":"a71FdN38w1xi"}},{"cell_type":"code","source":["# The instruction dataset to use\n","dataset_name = \"iamtarun/python_code_instructions_18k_alpaca\"\n","# Load dataset from the hub\n","dataset = load_dataset(dataset_name, split=\"train[:10%]\")\n","# Show dataset size\n","print(f\"dataset size: {len(dataset)}\")\n","# Show an example\n","print(dataset[randrange(len(dataset))])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":232,"referenced_widgets":["756b2ba64c2a4693baf8b4dea432d63d","dfed632517d141d98289d2ee6ee4af91","da2908ee17d54bb9be3581e355c38998","05af5c8f4e1c431ca16a66fad7044588","f6c5629651234c7c8803386d1e1eba63","843e964e967d4de6a9fd7367366c7998","d50076e45ea24f3aa8f31436bcb45529","153dea6e82f14df8abfe0d451c8990ef","ad5a7ad3cf8349208df1bc353f300e1a","203e36908ec44f8189327336b49280c4","3a5c928153b548ba96c77009d16ed3e8","fdd14af1cc034bc1935aa829087cecf6","79335b68378744eb940c9718f491d2da","6bc345ca267c4bf9ac2f732a632bfc86","a78c64c95afd41c7ba0f3e727112c6d7","ba51ddf873c745089ec230e291ad58f3","fb6e65f38b5f44e5902757f4d3f2cb5e","feae1fce9e9b47ffa09906976f8a7c25","a854406b145244928ffd558c943d9121","62be1fb695be4168bf17f82b9ec2edcd","17b3d0c8e8714ebfa7d7d3f85b9a1d20","4576c9d9aa3f4f8080493adde06eb3ff","a475254d401649ddbb0e0a601e5e5ac2","40eb4033fd024cd99744bcc9bd77206b","e63abcbd7f9045e794eda7bb80ac48fc","a4a5719ffa92458783fc22091434db10","2860e1e942624c03a10dd4e63d2dbc31","2e625e870fa84b94af711d6c9c907cda","5e3b22c1e38f43098ba664787f60daa5","125163cb6c6c4aad8ab076da0eb58243","95817164ee0f4a448ac0089b3fea6b04","ae95325a14d4464085e565a772261e42","c9b04dce9b9b4548a61d371db38c12a3","665b4498ad124bd0abafef679f40ca57","487696370dcf4b7ba2353b839e97e838","f334710a6d7e4fdca4bb7ddea125d748","c27cf94f4a144943ad035f6a6a7319e0","b4540f004e5842bc9ece9ef896d9ebd2","787d3a214bc24dc6a22860f12a9efeeb","a238618e995a4d95a2a9595edad332e3","fbb3e3e5728b45e7b04d8586c3c385b8","c73bd7b1f52544e687bc7e91a034b258","fcb8d4f22b504c2fa4c3e34fdc9ba57d","4b4b25db94724295ab9d738dc1cbc70a","447667e365e94d1d81d31b320b91c3ee","2e8a47aca57c4083af282f9b891922e7","3c901877e4ac4a02bb5c860d5aff806d","6c4680baefa94a35a494ad0db008c837","f4f8920b086948d491ffa5cd902f36db","08adf374c9cb4d2da55c34e2023f173b","120dab05c8d248ae86ca5f21068dc89a","58b7c6bfb41847dd84f09c52b812c08d","31a603b01a9a40bd914b50d7dcc94254","afb83aec06e64db3af08e3d96639227d","e0c784ee2d3f4adc9baac6f497fa88b6"]},"id":"7R11_psfw1xi","executionInfo":{"status":"ok","timestamp":1693813312522,"user_tz":-120,"elapsed":5754,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"ed1cc96a-115d-4cdd-bd83-82b38175cc23"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/905 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"756b2ba64c2a4693baf8b4dea432d63d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fdd14af1cc034bc1935aa829087cecf6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a475254d401649ddbb0e0a601e5e5ac2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"665b4498ad124bd0abafef679f40ca57"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/18612 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"447667e365e94d1d81d31b320b91c3ee"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["dataset size: 1861\n","{'instruction': 'Write a Python script for recognizing facial features using OpenCV and Haar features.', 'input': '<Sample_image.jpg>', 'output': '#Import necessary libraries\\nimport cv2\\nimport numpy as np\\n\\n#Load the classifier for frontal face detection\\nhaar_face_cascade = cv2.CascadeClassifier(\"haarcascade_frontal_face.xml\")\\n\\n#Create a VideoCapture object\\ncapture = cv2.VideoCapture(\"Sample_image.jpg\")\\nimg = capture.read()\\n\\n#Convert the image into grayscale\\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n\\n#Detect facial features\\nfaces = haar_face_cascade.detectMultiScale(gray, 1.3, 5)\\n\\n#Draw a rectangle around the faces\\nfor (x,y,w,h) in faces:\\n    cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0), 2)\\n\\n#Write the image into a file\\ncv2.imwrite(\\'face_detect.jpg\\',img)', 'prompt': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nWrite a Python script for recognizing facial features using OpenCV and Haar features.\\n\\n### Input:\\n<Sample_image.jpg>\\n\\n### Output:\\n#Import necessary libraries\\nimport cv2\\nimport numpy as np\\n\\n#Load the classifier for frontal face detection\\nhaar_face_cascade = cv2.CascadeClassifier(\"haarcascade_frontal_face.xml\")\\n\\n#Create a VideoCapture object\\ncapture = cv2.VideoCapture(\"Sample_image.jpg\")\\nimg = capture.read()\\n\\n#Convert the image into grayscale\\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n\\n#Detect facial features\\nfaces = haar_face_cascade.detectMultiScale(gray, 1.3, 5)\\n\\n#Draw a rectangle around the faces\\nfor (x,y,w,h) in faces:\\n    cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0), 2)\\n\\n#Write the image into a file\\ncv2.imwrite(\\'face_detect.jpg\\',img)'}\n"]}]},{"cell_type":"code","source":["# Check the dataset structure\n","print(dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JzdpQJuPw1xi","executionInfo":{"status":"ok","timestamp":1693813319668,"user_tz":-120,"elapsed":388,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"9d6eebe3-5512-4d2a-b577-8f919b832161"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Dataset({\n","    features: ['instruction', 'input', 'output', 'prompt'],\n","    num_rows: 1861\n","})\n"]}]},{"cell_type":"markdown","source":["We would apply inference on those examples longer than 500 characters:"],"metadata":{"id":"DgO-UUztw1xj"}},{"cell_type":"code","source":["# Sample the dataset\n","#sample_dataset= dataset[randrange(0,100,5)]\n","# Filter the dataset to get those which instruction is longer than 100 characters\n","responses_test = dataset.filter(lambda x: len(x['instruction'])>500)\n","\n","print(len(responses_test))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":66,"referenced_widgets":["7779bdb2940f47f2b06d624148e5bab6","f877a8098dc344b7b52f54d452f62ca3","a8b4052a61214cab8ea4960765397302","608fb834d88642dc995d9cee4caa56f5","dc36934896f84c7da7fa3fa825852c99","3d2b80089ca0461b9d04cafa85485bc8","d478a018354941a3afddf008f9ce1e89","d1d37b28ba904838afee34173e54f39b","b636da5d3a8d4cea88efb257563bb6e5","a30e450810264b9a83a4f3a2ea108905","1cc70c56d62243d7a96fca5f89af9db7"]},"id":"FbE_YNXzw1xj","executionInfo":{"status":"ok","timestamp":1693813325282,"user_tz":-120,"elapsed":555,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"30a717c5-0dc9-4350-f843-82a020390c31"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Filter:   0%|          | 0/1861 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7779bdb2940f47f2b06d624148e5bab6"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["12\n"]}]},{"cell_type":"markdown","source":["Now, we run this simple evaluation process"],"metadata":{"id":"qXlSnjQxw1xj"}},{"cell_type":"code","source":["# Process the test examples\n","times = []\n","solutions = []\n","\n","for example in responses_test:\n","  start = time.time()\n","  prompt = f\"\"\"### Instruction:\n","  Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.\n","\n","  ### Task:\n","  {example[\"instruction\"]}\n","\n","  ### Input:\n","  {example[\"input\"]}\n","\n","  ### Response:\n","  \"\"\"\n","  # Tokenize the input\n","  input_ids = tokenizer(prompt, return_tensors=\"pt\", truncation=True).input_ids.cuda()\n","  # Run the model to infere an output\n","  outputs = model.generate(input_ids=input_ids, max_new_tokens=200, do_sample=True, top_p=0.9,temperature=0.3)\n","\n","  # Collect the output and the inference time\n","  end = time.time()\n","  print(start-end)\n","  times.append(start-end)\n","  solutions.append(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):])\n","\n","print(times)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nsZTLJyDw1xj","executionInfo":{"status":"ok","timestamp":1693813457225,"user_tz":-120,"elapsed":95099,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"168c00c9-b8d3-46f1-b830-944adab0095e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"]},{"output_type":"stream","name":"stdout","text":["-9.108977317810059\n","-7.926240921020508\n","-7.896298408508301\n","-6.436765909194946\n","-7.823150396347046\n","-8.08249545097351\n","-8.073349237442017\n","-8.109416961669922\n","-8.120927333831787\n","-8.119935035705566\n","-6.791100978851318\n","-8.088157653808594\n","[-9.108977317810059, -7.926240921020508, -7.896298408508301, -6.436765909194946, -7.823150396347046, -8.08249545097351, -8.073349237442017, -8.109416961669922, -8.120927333831787, -8.119935035705566, -6.791100978851318, -8.088157653808594]\n"]}]},{"cell_type":"markdown","source":["Show the metrics"],"metadata":{"id":"bWnmIHo8ls2F"}},{"cell_type":"code","source":["import numpy as np"],"metadata":{"id":"Q9fbghKUw1xj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print('Min time: ',np.min(times))\n","print('Max time: ',np.max(times))\n","print('Mean time: ',np.mean(times))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v1_Nn72_w1xk","executionInfo":{"status":"ok","timestamp":1693813469874,"user_tz":-120,"elapsed":5,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"27c3fb46-9b6d-426e-f514-742e9f1e5124"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Min time:  -9.108977317810059\n","Max time:  -6.436765909194946\n","Mean time:  -7.881401300430298\n"]}]}]}