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October 4, 2023 10:07
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Fine-tune a Quantized Large Language Model on a Single GPU (Falcon-7B)
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| "<a href=\"https://colab.research.google.com/gist/philwinder/80f90b2424dd9b3e9e984251be3093ea/231003_falcon7b_finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
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| "source": [ | |
| "# Fine-tune a Quantized LLM on a Single GPU (Falcon-7B)\n", | |
| "\n", | |
| "This notebook demonstrates how to fine-tune a state-of-the-art [large language model (LLM)](https://winder.ai/services/chatgpt-consulting/) on a single GPU. This example uses Falcon-7B because it is Apache licensed. The data used in this notebook is for informational purposes only, do not use this data unless you have licensed it.\n", | |
| "\n", | |
| "### About the Author\n", | |
| "\n", | |
| "This notebook was devleoped by Dr. Phil Winder of https://winder.AI as part of a talk at Goto Copenhagen 2023. If you have any questions or require further support please reach out to [sales@winder.ai](mailto:sales@winder.ai).\n", | |
| "\n", | |
| "### About the Model\n", | |
| "\n", | |
| "This notebook uses the [Falcon-7B LLM from TII](https://huggingface.co/tiiuae/falcon-7b/) in the UAE. It is a 7-billion-parameter decoder-only transformer model trained on 1.5 trillion tokens from their cleaned, curated [`Refined Web`](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) dataset. They suggest that their state-of-the-art performance is largely due to the quality of the training data.\n", | |
| "\n", | |
| "### About the Data\n", | |
| "\n", | |
| "I chose to use the raw pre-trained version of the Falcon model instead of the chat-trained model to simplify the fine-tuning data; i.e. there are no Q&A format expectations.\n", | |
| "\n", | |
| "The goal of the data is to help the model produce new song lyrics, but the dataset is very small, only several hundred examples long. A dataset with many more examples is required to turn this into something useful. And please note, do not use this data unless you are licensed to do so.\n", | |
| "\n", | |
| "## Prerequisites\n", | |
| "\n", | |
| "This notebook was developed against a `V100` machine in Google Colab. It should work on an `A100` as well, but not a `T4`. Note that adding evaluation to the training wrapper will use too much GPU memory.\n", | |
| "\n", | |
| "### Python Dependencies\n", | |
| "\n", | |
| "- The `bitsandbytes` library provides quantization wrappers to help fit the model into our meagre GPU RAM.\n", | |
| "- `transformers`, `accelerate` and `datasets` all provide the skeleton training code.\n", | |
| "- `peft` provides the fine-tuning adapters so you don't have to fine-tune the whole model." | |
| ], | |
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| "id": "yTAeOg51B86e" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install -q bitsandbytes==0.41.1 transformers==4.33.3 accelerate==0.23.0 datasets==2.14.5 einops==0.6.1\n", | |
| "!pip install -q -U git+https://github.com/huggingface/peft.git@69665f24e98dc5f20a430637a31f196158b6e0da" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "3bGvWGblcY5m", | |
| "outputId": "5357e6fe-840c-4109-bb7c-1e9393026872" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
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| "text": [ | |
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| "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", | |
| " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", | |
| " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import os\n", | |
| "import re\n", | |
| "import bitsandbytes as bnb\n", | |
| "import pandas as pd\n", | |
| "import torch\n", | |
| "import torch.nn as nn\n", | |
| "import transformers\n", | |
| "from datasets import load_dataset, Dataset, Value\n", | |
| "from peft import (\n", | |
| " LoraConfig,\n", | |
| " PeftConfig,\n", | |
| " get_peft_model,\n", | |
| " prepare_model_for_kbit_training,\n", | |
| ")\n", | |
| "from transformers import (\n", | |
| " AutoConfig,\n", | |
| " AutoModelForCausalLM,\n", | |
| " AutoTokenizer,\n", | |
| " BitsAndBytesConfig,\n", | |
| ")\n" | |
| ], | |
| "metadata": { | |
| "id": "dbF3wwD_dRFK" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Model\n", | |
| "\n", | |
| "The next batch of code downloads and imports the model and the tokenizer." | |
| ], | |
| "metadata": { | |
| "id": "ZZWvlPrhGtwM" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model_id = \"tiiuae/falcon-7b\"\n", | |
| "\n", | |
| "bnb_config = BitsAndBytesConfig(\n", | |
| " load_in_4bit=True,\n", | |
| " load_4bit_use_double_quant=True,\n", | |
| " bnb_4bit_quant_type=\"nf4\",\n", | |
| " bnb_4bit_compute_dtype=torch.bfloat16,\n", | |
| ")\n", | |
| "\n", | |
| "model = AutoModelForCausalLM.from_pretrained(\n", | |
| " model_id,\n", | |
| " device_map=\"auto\",\n", | |
| " trust_remote_code=True,\n", | |
| " quantization_config=bnb_config,\n", | |
| ")\n", | |
| "model = prepare_model_for_kbit_training(model)\n", | |
| "\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True)\n", | |
| "tokenizer.pad_token = tokenizer.eos_token" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 528, | |
| "referenced_widgets": [ | |
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| ] | |
| }, | |
| "id": "t_SG9Go2dJnL", | |
| "outputId": "e627b04a-b94d-4b7c-e730-3cd44a0c703a" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading (…)lve/main/config.json: 0%| | 0.00/950 [00:00<?, ?B/s]" | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n", | |
| "- configuration_RW.py\n", | |
| ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n" | |
| ] | |
| }, | |
| { | |
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| } | |
| }, | |
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| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n", | |
| "- modelling_RW.py\n", | |
| ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n" | |
| ] | |
| }, | |
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| "model_id": "31ba75235e7142a7add2ff5003daba0a" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Example Generation\n", | |
| "\n", | |
| "Let's generate an initial example to see how the model performs without fine-tuning. The following is a helper function to call the inference function in the correct way. Take note of the generation settings here." | |
| ], | |
| "metadata": { | |
| "id": "Vm6AXcAZG5bT" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def generate(prompt=\"[Intro]\") -> str:\n", | |
| " inputs = tokenizer(prompt, padding=True, truncation=True, return_tensors=\"pt\").to(\"cuda:0\")\n", | |
| " # More info about generation options: https://huggingface.co/blog/how-to-generate\n", | |
| " outputs = model.generate(\n", | |
| " input_ids=inputs['input_ids'],\n", | |
| " attention_mask=inputs['attention_mask'],\n", | |
| " do_sample=True,\n", | |
| " top_p=0.92,\n", | |
| " top_k=0,\n", | |
| " max_new_tokens=50)\n", | |
| " return tokenizer.decode(outputs[0], skip_special_tokens=True)" | |
| ], | |
| "metadata": { | |
| "id": "XrQn-TyFUwwo" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(generate())" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "DEwzcecba3G6", | |
| "outputId": "5cd198f9-cd45-447c-db60-9eaec7a8a5a1" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1417: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n", | |
| " warnings.warn(\n", | |
| "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", | |
| " warnings.warn(\n", | |
| "Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[Intro]\n", | |
| "Yeah, yeah, yeah\n", | |
| "Yeah, yeah, yeah\n", | |
| "Yeah, yeah, yeah\n", | |
| "Yeah, yeah, yeah\n", | |
| "[Verse 1]\n", | |
| "I'm a young nigga, I'm a young nigga\n", | |
| "I'm\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### The `peft` LoRa Configuration\n", | |
| "\n", | |
| "The following configuration controls the adaptor that is used to fine-tune the model." | |
| ], | |
| "metadata": { | |
| "id": "1eqSFagFHb4Q" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "hoeAzElzcXVH" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "config = LoraConfig(\n", | |
| " r=16,\n", | |
| " lora_alpha=32,\n", | |
| " target_modules=[\"query_key_value\"],\n", | |
| " lora_dropout=0.05,\n", | |
| " bias=\"none\",\n", | |
| " task_type=\"CAUSAL_LM\"\n", | |
| ")\n", | |
| "\n", | |
| "model = get_peft_model(model, config)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Data\n", | |
| "\n", | |
| "Next you should load and format your fine-tuning data. Take note of the expected format. You can see an example of the training data below." | |
| ], | |
| "metadata": { | |
| "id": "-4U149YcHn5A" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "data = load_dataset(PATH_TO_DATASET_REDACTED, split=\"train\")\n", | |
| "data" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 248, | |
| "referenced_widgets": [ | |
| "045216fb674e464ab5d63e774e0f3d09", | |
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| "ccdddc9074c34923a086cea9b025751a", | |
| "ade79a6682de41378aa548fde0c1dc2a", | |
| "9403a180592e41aca75b3c5721488530", | |
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| "b7f4efb5847045a78b0f86e2c9217ab6", | |
| "a71e364e78c44d97bf86332919b82885", | |
| "31f8236e79ed4ecbab7dd25b21403b62", | |
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| "1801c853212945549e34ab835a15917c", | |
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| "id": "vy9kvzvSVX5_", | |
| "outputId": "61222baa-a070-40b4-e07b-c76d9bee0290" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
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| "application/vnd.jupyter.widget-view+json": { | |
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| "model_id": "045216fb674e464ab5d63e774e0f3d09" | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]" | |
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| "application/vnd.jupyter.widget-view+json": { | |
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| "model_id": "070f33014131494b97bbfd2d4c6ecbc2" | |
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| "metadata": {} | |
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| { | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Generating train split: 0%| | 0/180 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Dataset({\n", | |
| " features: ['Unnamed: 0', 'number', 'title', 'artist', 'lyrics', 'album', 'lyrics_length'],\n", | |
| " num_rows: 180\n", | |
| "})" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "lyrics = data[\"lyrics\"]\n", | |
| "lyrics[0]" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 178 | |
| }, | |
| "id": "DsSKyGVMVt3e", | |
| "outputId": "8762899c-d8a4-46d8-c56f-6cedbbdc5b76" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'[Intro]\\nShoot me\\nShoot me\\nShoot me\\nShoot me\\n\\n[Verse 1]\\nHere come old flat-top, he come groovin\\' up slowly\\nHe got ju-ju eyeball, he one holy roller\\nHe got hair down to his knee\\nGot to be a joker, he just do what he please\\n\\n[Interlude]\\nShoot me\\nShoot me\\nShoot me\\nShoot me\\n\\n[Verse 2]\\nHe wear no shoeshine, he got toe-jam football\\nHe got monkey finger, he shoot Coca-Cola\\nHe say, \"I know you, you know me\"\\nOne thing I can tell you is you got to be free\\n\\n[Chorus]\\nCome together, right now\\nOver me\\n\\n[Interlude]\\nShoot me\\nShoot me\\nShoot me\\nShoot me\\n\\n[Verse 3]\\nHe bag production, he got walrus gumboot\\nHe got Ono sideboard, he one spinal cracker\\nHe got feet down below his knee\\nHold you in his armchair, you can feel his disease\\n\\n[Chorus]\\nCome together, right now\\nOver me\\n\\n[Interlude]\\nShoot me\\nShoot me\\nRight!\\nCome, come, come, come\\n\\n[Verse 4]\\nHe roller-coaster, he got early warnin\\'\\nHe got muddy water, he one mojo filter\\nHe say, \"One and one and one is three.\"\\nGot to be good-lookin\\' \\'cause he\\'s so hard to see\\n\\n[Chorus]\\nCome together, right now\\nOver me\\n\\n[Interlude]\\nShoot me\\nShoot me\\nShoot me\\nShoot me\\nUgh!\\n\\n[Outro]\\nCome together, yeah\\nCome together, yeah\\nCome together, yeah\\nCome together, yeah\\nCome together, yeah\\nCome together, yeah\\nCome together, yeah\\nCome together, yeah\\nCome together, yeah\\nCome together'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 8 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Cleaning the Data\n", | |
| "\n", | |
| "This section of code takes the raw data and produces a cleaned version that is ready to be trained upon. I found that I obtained best results when I split the lyrics into distinct verses and used a key at the start to signify what type of verse it was (e.g. verse, chorus, intro, etc.). These keys were already present in the raw data." | |
| ], | |
| "metadata": { | |
| "id": "w7xAuiXAH57I" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def raw_lyrics():\n", | |
| " for lyrics in data[\"lyrics\"]:\n", | |
| " full_prompt = lyrics + tokenizer.eos_token\n", | |
| " tokenized_full_prompt = tokenizer(full_prompt, padding=True, truncation=True)\n", | |
| " yield {\"lyrics\": lyrics, **tokenized_full_prompt}\n", | |
| "\n", | |
| "def split_all():\n", | |
| " for lyrics in data[\"lyrics\"]:\n", | |
| " verses = re.split('\\[.*\\]', lyrics)\n", | |
| " verses = filter(lambda a: len(a.strip()) > 0, verses)\n", | |
| " for v in verses:\n", | |
| " full_prompt = create_prompt(v + tokenizer.eos_token)\n", | |
| " tokenized_full_prompt = tokenizer(full_prompt, padding=True, truncation=True)\n", | |
| " yield {\"verse\": v, **tokenized_full_prompt}\n", | |
| "\n", | |
| "def split_verses():\n", | |
| " for lyrics in data[\"lyrics\"]:\n", | |
| " verses = re.findall(r\"[\\S\\n\\t\\v ]*?(?:\\n(?=\\[)|$)\", lyrics)\n", | |
| " verses = filter(lambda a: len(a.strip()) > 0, verses)\n", | |
| " for v in verses:\n", | |
| " full_prompt = v + tokenizer.eos_token\n", | |
| " tokenized_full_prompt = tokenizer(full_prompt, padding=True, truncation=True)\n", | |
| " yield {\"verse\": v, **tokenized_full_prompt}\n", | |
| "\n", | |
| "dataset = Dataset.from_generator(split_verses)\n", | |
| "print(dataset[0][\"verse\"])\n", | |
| "print(dataset[1][\"verse\"])\n", | |
| "print(dataset[999][\"verse\"])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 406, | |
| "referenced_widgets": [ | |
| "cf4b8dc563f242f491494d5a69eb7963", | |
| "249519e43fa9444fab8ed0c5faea5a28", | |
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| "id": "T_b6D1gtfkHG", | |
| "outputId": "1c0e6dfe-0a1c-45f9-b151-ace6478d7ab0" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Generating train split: 0 examples [00:00, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "cf4b8dc563f242f491494d5a69eb7963" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[Intro]\n", | |
| "Shoot me\n", | |
| "Shoot me\n", | |
| "Shoot me\n", | |
| "Shoot me\n", | |
| "\n", | |
| "\n", | |
| "[Verse 1]\n", | |
| "Here come old flat-top, he come groovin' up slowly\n", | |
| "He got ju-ju eyeball, he one holy roller\n", | |
| "He got hair down to his knee\n", | |
| "Got to be a joker, he just do what he please\n", | |
| "\n", | |
| "\n", | |
| "[Bridge]\n", | |
| "In a couple of years, they have built a home sweet home\n", | |
| "With a couple of kids running in the yard\n", | |
| "Of Desmond and Molly Jones (Ha, ha, ha, ha, ha, ha)\n", | |
| "\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Training\n", | |
| "\n", | |
| "The following configures the fine-tuning parameters. Note that this \"helper\" function has an infinite number of arguments so read the documentation carefully.\n", | |
| "\n", | |
| "The key settings here are the number of training steps/epochs and the batch size. `Transformers` is generally smart enought to figure out the best settings itself, but sometimes you will need tighter control (like if you are using a small GPU).\n", | |
| "\n", | |
| "I found that 30 epochs was the best from a loss perspective. I didn't use any useful evaluation measure here (to save on GPU RAM) so I couldn't suggest whether this is optimal or not.\n", | |
| "\n", | |
| "30 epochs took about an hour on a `V100`; it's not a quick thing. ;-)" | |
| ], | |
| "metadata": { | |
| "id": "VMOHKXY4IYA9" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "training_args = transformers.TrainingArguments(\n", | |
| " auto_find_batch_size=True, # Try to auto-find a batch size. Also see https://huggingface.co/google/flan-ul2/discussions/16#64c8bdaf4cc48498134a0271\n", | |
| " learning_rate=2e-4,\n", | |
| " # bf16=True, # Only on A100\n", | |
| " fp16=True, # On V100\n", | |
| " save_total_limit=4,\n", | |
| " # warmup_steps=2,\n", | |
| " num_train_epochs=30, # Total number of training epochs to perform. It stablised after 30.\n", | |
| " output_dir='checkpoints',\n", | |
| " save_strategy='epoch',\n", | |
| " report_to=\"none\",\n", | |
| " # evaluation_strategy=\"steps\", # Evaluation is done (and logged) every eval_steps.\n", | |
| " logging_steps=25, # Number of update steps between logs and (by default) evaluations if evaluation_strategy=\"steps\".\n", | |
| " save_safetensors=True,\n", | |
| " load_best_model_at_end=True,\n", | |
| " metric_for_best_model='accuracy',\n", | |
| ")\n", | |
| "trainer = transformers.Trainer(\n", | |
| " model=model,\n", | |
| " train_dataset=dataset,\n", | |
| " # eval_dataset=dataset[\"test\"], # 16GB GPU not big enough\n", | |
| " args=training_args,\n", | |
| " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n", | |
| " # compute_metrics=compute_metrics,\n", | |
| ")\n", | |
| "model.config.use_cache = False" | |
| ], | |
| "metadata": { | |
| "id": "IrGvcQmigbL-" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "trainer.train(resume_from_checkpoint=False) # Set to true if resuming" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "id": "cEc5PtKi1AnR", | |
| "outputId": "fa49a9b6-c913-4a0e-c295-8d4e1a1edbc8" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div>\n", | |
| " \n", | |
| " <progress value='5609' max='8400' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
| " [5609/8400 1:13:39 < 40:17, 1.15 it/s, Epoch 33.38/50]\n", | |
| " </div>\n", | |
| " <table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>Step</th>\n", | |
| " <th>Training Loss</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>525</td>\n", | |
| " <td>1.157000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>550</td>\n", | |
| " <td>1.317600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>575</td>\n", | |
| " <td>1.205000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>600</td>\n", | |
| " <td>1.274100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>625</td>\n", | |
| " <td>1.275500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>650</td>\n", | |
| " <td>1.289200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>675</td>\n", | |
| " <td>1.154400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>700</td>\n", | |
| " <td>1.140200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>725</td>\n", | |
| " <td>1.177400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>750</td>\n", | |
| " <td>1.072500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>775</td>\n", | |
| " <td>1.157700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>800</td>\n", | |
| " <td>1.017800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>825</td>\n", | |
| " <td>1.030200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>850</td>\n", | |
| " <td>0.926700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>875</td>\n", | |
| " <td>1.017300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>900</td>\n", | |
| " <td>0.955100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>925</td>\n", | |
| " <td>0.881700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>950</td>\n", | |
| " <td>0.954700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>975</td>\n", | |
| " <td>0.939900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1000</td>\n", | |
| " <td>0.910200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1025</td>\n", | |
| " <td>0.791700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1050</td>\n", | |
| " <td>0.823100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1075</td>\n", | |
| " <td>0.776600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1100</td>\n", | |
| " <td>0.813600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1125</td>\n", | |
| " <td>0.790400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1150</td>\n", | |
| " <td>0.848300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1175</td>\n", | |
| " <td>0.933600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1200</td>\n", | |
| " <td>0.679000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1225</td>\n", | |
| " <td>0.663400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1250</td>\n", | |
| " <td>0.720600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1275</td>\n", | |
| " <td>0.650100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1300</td>\n", | |
| " <td>0.655800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1325</td>\n", | |
| " <td>0.724900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1350</td>\n", | |
| " <td>0.703000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1375</td>\n", | |
| " <td>0.569400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1400</td>\n", | |
| " <td>0.537900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1425</td>\n", | |
| " <td>0.606400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1450</td>\n", | |
| " <td>0.631300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1475</td>\n", | |
| " <td>0.608900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1500</td>\n", | |
| " <td>0.614900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1525</td>\n", | |
| " <td>0.511400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1550</td>\n", | |
| " <td>0.510400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1575</td>\n", | |
| " <td>0.452000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1600</td>\n", | |
| " <td>0.520100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1625</td>\n", | |
| " <td>0.527200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1650</td>\n", | |
| " <td>0.520400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1675</td>\n", | |
| " <td>0.584000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1700</td>\n", | |
| " <td>0.429200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1725</td>\n", | |
| " <td>0.434600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1750</td>\n", | |
| " <td>0.461700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1775</td>\n", | |
| " <td>0.447100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1800</td>\n", | |
| " <td>0.439800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1825</td>\n", | |
| " <td>0.449900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1850</td>\n", | |
| " <td>0.457300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1875</td>\n", | |
| " <td>0.390200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1900</td>\n", | |
| " <td>0.372600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1925</td>\n", | |
| " <td>0.408500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1950</td>\n", | |
| " <td>0.414600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1975</td>\n", | |
| " <td>0.420800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2000</td>\n", | |
| " <td>0.382200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2025</td>\n", | |
| " <td>0.368600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2050</td>\n", | |
| " <td>0.368000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2075</td>\n", | |
| " <td>0.386200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2100</td>\n", | |
| " <td>0.376900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2125</td>\n", | |
| " <td>0.340500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2150</td>\n", | |
| " <td>0.347000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2175</td>\n", | |
| " <td>0.379200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2200</td>\n", | |
| " <td>0.330500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2225</td>\n", | |
| " <td>0.332600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2250</td>\n", | |
| " <td>0.325600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2275</td>\n", | |
| " <td>0.330200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2300</td>\n", | |
| " <td>0.302700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2325</td>\n", | |
| " <td>0.345900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2350</td>\n", | |
| " <td>0.368500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2375</td>\n", | |
| " <td>0.285400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2400</td>\n", | |
| " <td>0.297600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2425</td>\n", | |
| " <td>0.302000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2450</td>\n", | |
| " <td>0.324900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2475</td>\n", | |
| " <td>0.329800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2500</td>\n", | |
| " <td>0.282400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2525</td>\n", | |
| " <td>0.339900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2550</td>\n", | |
| " <td>0.272000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2575</td>\n", | |
| " <td>0.298900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2600</td>\n", | |
| " <td>0.277500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2625</td>\n", | |
| " <td>0.281700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2650</td>\n", | |
| " <td>0.282400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2675</td>\n", | |
| " <td>0.292500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2700</td>\n", | |
| " <td>0.303900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2725</td>\n", | |
| " <td>0.252600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2750</td>\n", | |
| " <td>0.259400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2775</td>\n", | |
| " <td>0.280600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2800</td>\n", | |
| " <td>0.261500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2825</td>\n", | |
| " <td>0.292000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2850</td>\n", | |
| " <td>0.278800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2875</td>\n", | |
| " <td>0.256500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2900</td>\n", | |
| " <td>0.240400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2925</td>\n", | |
| " <td>0.255000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2950</td>\n", | |
| " <td>0.264700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2975</td>\n", | |
| " <td>0.266600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3000</td>\n", | |
| " <td>0.270200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3025</td>\n", | |
| " <td>0.283700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3050</td>\n", | |
| " <td>0.248700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3075</td>\n", | |
| " <td>0.235900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3100</td>\n", | |
| " <td>0.259000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3125</td>\n", | |
| " <td>0.246400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3150</td>\n", | |
| " <td>0.274100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3175</td>\n", | |
| " <td>0.251800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3200</td>\n", | |
| " <td>0.233200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3225</td>\n", | |
| " <td>0.227200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3250</td>\n", | |
| " <td>0.239300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3275</td>\n", | |
| " <td>0.262100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3300</td>\n", | |
| " <td>0.242500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3325</td>\n", | |
| " <td>0.255300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3350</td>\n", | |
| " <td>0.249900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3375</td>\n", | |
| " <td>0.241000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3400</td>\n", | |
| " <td>0.221600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3425</td>\n", | |
| " <td>0.246400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3450</td>\n", | |
| " <td>0.224600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3475</td>\n", | |
| " <td>0.239200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3500</td>\n", | |
| " <td>0.247300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3525</td>\n", | |
| " <td>0.241100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3550</td>\n", | |
| " <td>0.223300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3575</td>\n", | |
| " <td>0.212000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3600</td>\n", | |
| " <td>0.229400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3625</td>\n", | |
| " <td>0.248400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3650</td>\n", | |
| " <td>0.228100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3675</td>\n", | |
| " <td>0.231600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3700</td>\n", | |
| " <td>0.238500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3725</td>\n", | |
| " <td>0.221000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3750</td>\n", | |
| " <td>0.246100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3775</td>\n", | |
| " <td>0.218400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3800</td>\n", | |
| " <td>0.210000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3825</td>\n", | |
| " <td>0.229600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3850</td>\n", | |
| " <td>0.232100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3875</td>\n", | |
| " <td>0.214800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3900</td>\n", | |
| " <td>0.208700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3925</td>\n", | |
| " <td>0.208200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3950</td>\n", | |
| " <td>0.231400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3975</td>\n", | |
| " <td>0.223900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4000</td>\n", | |
| " <td>0.229800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4025</td>\n", | |
| " <td>0.230300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4050</td>\n", | |
| " <td>0.216700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4075</td>\n", | |
| " <td>0.204100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4100</td>\n", | |
| " <td>0.211800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4125</td>\n", | |
| " <td>0.217500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4150</td>\n", | |
| " <td>0.213200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4175</td>\n", | |
| " <td>0.240600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4200</td>\n", | |
| " <td>0.220500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4225</td>\n", | |
| " <td>0.206400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4250</td>\n", | |
| " <td>0.220100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4275</td>\n", | |
| " <td>0.215000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4300</td>\n", | |
| " <td>0.212600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4325</td>\n", | |
| " <td>0.200000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4350</td>\n", | |
| " <td>0.214100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4375</td>\n", | |
| " <td>0.226500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4400</td>\n", | |
| " <td>0.216400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4425</td>\n", | |
| " <td>0.218200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4450</td>\n", | |
| " <td>0.215200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4475</td>\n", | |
| " <td>0.203100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4500</td>\n", | |
| " <td>0.218200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4525</td>\n", | |
| " <td>0.201800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4550</td>\n", | |
| " <td>0.213300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4575</td>\n", | |
| " <td>0.199000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4600</td>\n", | |
| " <td>0.208400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4625</td>\n", | |
| " <td>0.215700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4650</td>\n", | |
| " <td>0.219200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4675</td>\n", | |
| " <td>0.183800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4700</td>\n", | |
| " <td>0.226600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4725</td>\n", | |
| " <td>0.204100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4750</td>\n", | |
| " <td>0.198000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4775</td>\n", | |
| " <td>0.206400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4800</td>\n", | |
| " <td>0.204100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4825</td>\n", | |
| " <td>0.204400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4850</td>\n", | |
| " <td>0.206300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4875</td>\n", | |
| " <td>0.215900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4900</td>\n", | |
| " <td>0.201100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4925</td>\n", | |
| " <td>0.199000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4950</td>\n", | |
| " <td>0.200600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4975</td>\n", | |
| " <td>0.198200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5000</td>\n", | |
| " <td>0.223200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5025</td>\n", | |
| " <td>0.211700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5050</td>\n", | |
| " <td>0.190800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5075</td>\n", | |
| " <td>0.199700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5100</td>\n", | |
| " <td>0.189100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5125</td>\n", | |
| " <td>0.205500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5150</td>\n", | |
| " <td>0.210600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5175</td>\n", | |
| " <td>0.211000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5200</td>\n", | |
| " <td>0.208500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5225</td>\n", | |
| " <td>0.198300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5250</td>\n", | |
| " <td>0.207000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5275</td>\n", | |
| " <td>0.208300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5300</td>\n", | |
| " <td>0.181800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5325</td>\n", | |
| " <td>0.197800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5350</td>\n", | |
| " <td>0.205400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5375</td>\n", | |
| " <td>0.208400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5400</td>\n", | |
| " <td>0.185300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5425</td>\n", | |
| " <td>0.174800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5450</td>\n", | |
| " <td>0.199500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5475</td>\n", | |
| " <td>0.199200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5500</td>\n", | |
| " <td>0.215900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5525</td>\n", | |
| " <td>0.212600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5550</td>\n", | |
| " <td>0.209500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5575</td>\n", | |
| " <td>0.199400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5600</td>\n", | |
| " <td>0.186800</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table><p>" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "error", | |
| "ename": "KeyboardInterrupt", | |
| "evalue": "ignored", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-17-e2181cfa3c3c>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Set to true if resuming\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1554\u001b[0m \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1555\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1556\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 1557\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1558\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/memory.py\u001b[0m in \u001b[0;36mdecorator\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No executable batch size found, reached zero.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 137\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshould_reduce_batch_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 1836\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1837\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1839\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1840\u001b[0m if (\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m 2691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2692\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2693\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2695\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_gpu\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m 2716\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2717\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2718\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2719\u001b[0m \u001b[0;31m# Save past state if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2720\u001b[0m \u001b[0;31m# TODO: this needs to be fixed and made cleaner later.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
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| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 636\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 637\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 638\u001b[0m \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
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| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 636\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 637\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 638\u001b[0m \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
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| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, task_ids, **kwargs)\u001b[0m\n\u001b[1;32m 944\u001b[0m )\n\u001b[1;32m 945\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 946\u001b[0;31m return self.base_model(\n\u001b[0m\u001b[1;32m 947\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
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| "\u001b[0;32m~/.cache/huggingface/modules/transformers_modules/tiiuae/falcon-7b/f7796529e36b2d49094450fb038cc7c4c86afa44/modelling_RW.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, head_mask, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, **deprecated_arguments)\u001b[0m\n\u001b[1;32m 638\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcustom_forward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 640\u001b[0;31m outputs = torch.utils.checkpoint.checkpoint(\n\u001b[0m\u001b[1;32m 641\u001b[0m \u001b[0mcreate_custom_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 642\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
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| "\u001b[0;32m~/.cache/huggingface/modules/transformers_modules/tiiuae/falcon-7b/f7796529e36b2d49094450fb038cc7c4c86afa44/modelling_RW.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, alibi, attention_mask, layer_past, head_mask, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0mvalue_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_kv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m \u001b[0mquery_layer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_rotary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery_layer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey_layer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlayer_past\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/.cache/huggingface/modules/transformers_modules/tiiuae/falcon-7b/f7796529e36b2d49094450fb038cc7c4c86afa44/modelling_RW.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, q, k)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseq_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhead_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0mcos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcos_sin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcos\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrotate_half\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0msin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcos\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrotate_half\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0msin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/.cache/huggingface/modules/transformers_modules/tiiuae/falcon-7b/f7796529e36b2d49094450fb038cc7c4c86afa44/modelling_RW.py\u001b[0m in \u001b[0;36mrotate_half\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrotate_half\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# dim=-1 triggers a bug in torch < 1.8.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mKeyboardInterrupt\u001b[0m: " | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "trainer.save_model(\"final_model\")" | |
| ], | |
| "metadata": { | |
| "id": "ymKzlz6cMGKP" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "transformers.logging.set_verbosity_error()" | |
| ], | |
| "metadata": { | |
| "id": "2LoR2ryty8pi" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Storage\n", | |
| "\n", | |
| "The following section exports the trained model to my personal GDrive for use in another inference notebook." | |
| ], | |
| "metadata": { | |
| "id": "zFShzv-aKGr7" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import tarfile\n", | |
| "import os.path\n", | |
| "\n", | |
| "def make_tarfile(output_filename, source_dir):\n", | |
| " with tarfile.open(output_filename, \"w:gz\") as tar:\n", | |
| " tar.add(source_dir, arcname=os.path.basename(source_dir))" | |
| ], | |
| "metadata": { | |
| "id": "zdxmwQpHMKfr" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "make_tarfile(\"final_model.tar.gz\", \"final_model\")" | |
| ], | |
| "metadata": { | |
| "id": "8zWfJNJ9NxK5" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import locale\n", | |
| "locale.getpreferredencoding = lambda: \"UTF-8\"" | |
| ], | |
| "metadata": { | |
| "id": "bUqXlo9FN2DJ" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!cp final_model.tar.gz path_to_save_directory" | |
| ], | |
| "metadata": { | |
| "id": "W2VBFqKGN5oW" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "_iu2wZIFHev1" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Lyric Inference\n", | |
| "\n", | |
| "Now it's time to try our model out. Let's load the saved model weights and recreate the necessary helper functions.\n", | |
| "\n", | |
| "### Untar the Fine-Tuned Weights" | |
| ], | |
| "metadata": { | |
| "id": "hN6L5dg0Kxb2" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!cp ./path_to_save_directory/final_model.tar.gz .\n", | |
| "import tarfile\n", | |
| "import os\n", | |
| "tar = tarfile.open(\"final_model.tar.gz\")\n", | |
| "tar.extractall()\n", | |
| "tar.close()" | |
| ], | |
| "metadata": { | |
| "id": "K8UWcfvhPiEz" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Install the Prerequisites" | |
| ], | |
| "metadata": { | |
| "id": "9pG8OfKpLD66" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ynyh3i8APR3C", | |
| "outputId": "f51389f2-fc6b-48aa-ba2f-bcdabf4bdc45" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", | |
| " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", | |
| " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!pip install -q bitsandbytes==0.41.1 transformers==4.33.3 accelerate==0.23.0 datasets==2.14.5 einops==0.6.1\n", | |
| "!pip install -q -U git+https://github.com/huggingface/peft.git@69665f24e98dc5f20a430637a31f196158b6e0da" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import os\n", | |
| "import re\n", | |
| "import bitsandbytes as bnb\n", | |
| "import pandas as pd\n", | |
| "import torch\n", | |
| "import torch.nn as nn\n", | |
| "import transformers\n", | |
| "from datasets import load_dataset, Dataset, Value\n", | |
| "from peft import (\n", | |
| " LoraConfig,\n", | |
| " PeftConfig,\n", | |
| " get_peft_model,\n", | |
| " PeftModel,\n", | |
| " prepare_model_for_kbit_training,\n", | |
| ")\n", | |
| "from transformers import (\n", | |
| " AutoConfig,\n", | |
| " AutoModelForCausalLM,\n", | |
| " AutoTokenizer,\n", | |
| " BitsAndBytesConfig,\n", | |
| ")\n" | |
| ], | |
| "metadata": { | |
| "id": "v5M81zMBPcTO" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Load the Base Model" | |
| ], | |
| "metadata": { | |
| "id": "2zKygQrQLIaq" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model_id = \"tiiuae/falcon-7b\"\n", | |
| "adapters_name = \"final_model\"\n", | |
| "\n", | |
| "print(f\"Starting to load the model {model_id} into memory\")\n", | |
| "\n", | |
| "bnb_config = BitsAndBytesConfig(\n", | |
| " load_in_4bit=True,\n", | |
| " load_4bit_use_double_quant=True,\n", | |
| " bnb_4bit_quant_type=\"nf4\",\n", | |
| " bnb_4bit_compute_dtype=torch.bfloat16,\n", | |
| ")\n", | |
| "\n", | |
| "model = AutoModelForCausalLM.from_pretrained(\n", | |
| " model_id,\n", | |
| " device_map=\"auto\",\n", | |
| " trust_remote_code=True,\n", | |
| " quantization_config=bnb_config,\n", | |
| ")\n", | |
| "model = prepare_model_for_kbit_training(model)\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True)\n", | |
| "tokenizer.pad_token = tokenizer.eos_token\n", | |
| "\n", | |
| "model = PeftModel.from_pretrained(model, adapters_name)\n", | |
| "model = model.merge_and_unload()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 173, | |
| "referenced_widgets": [ | |
| "7a947d4533e14c6680124c659950732a", | |
| "0b48734a358c4e298f1ae17acde4dd92", | |
| "70e7d7a1072143489b537927cf67763f", | |
| "aff0b650d7bb4114844e0c246906236c", | |
| "30e4276fd6084ef29d78b8590f772081", | |
| "a60b8b9c1b2446b3a6827015e4596b74", | |
| "a4b68b1ef0dd4e9189dc3fdef2975c67", | |
| "227ced1cf8d744d5aeb80e8ba0e247df", | |
| "c1dd5835ce9445bc8dc3dee52af15534", | |
| "a54af2e31ab347b5b906035683170788", | |
| "e141c3013d5d43579590dd30b44aacea" | |
| ] | |
| }, | |
| "id": "8u9Nkfz4QoAF", | |
| "outputId": "1a530510-d1f0-4a59-896a-15267ca28e29" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Starting to load the model tiiuae/falcon-7b into memory\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "WARNING:transformers_modules.tiiuae.falcon-7b.898df1396f35e447d5fe44e0a3ccaaaa69f30d36.configuration_falcon:\n", | |
| "WARNING: You are currently loading Falcon using legacy code contained in the model repository. Falcon has now been fully ported into the Hugging Face transformers library. For the most up-to-date and high-performance version of the Falcon model code, please update to the latest version of transformers and then load the model without the trust_remote_code=True argument.\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "7a947d4533e14c6680124c659950732a" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py:209: UserWarning: Merge lora module to 4-bit linear may get different generations due to rounding errors.\n", | |
| " warnings.warn(\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### The Same Inference Function" | |
| ], | |
| "metadata": { | |
| "id": "dxiYjaKALP75" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def generate(prompt=\"[Intro]\") -> str:\n", | |
| " inputs = tokenizer(prompt, padding=True, truncation=True, return_tensors=\"pt\").to(\"cuda:0\")\n", | |
| " # More info about generation options: https://huggingface.co/blog/how-to-generate\n", | |
| " outputs = model.generate(\n", | |
| " input_ids=inputs['input_ids'],\n", | |
| " attention_mask=inputs['attention_mask'],\n", | |
| " do_sample=True,\n", | |
| " top_p=0.92,\n", | |
| " top_k=0,\n", | |
| " max_new_tokens=50)\n", | |
| " return tokenizer.decode(outputs[0], skip_special_tokens=True)" | |
| ], | |
| "metadata": { | |
| "id": "HLTtJjHtR1rr" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Lyric Generation\n", | |
| "\n", | |
| "Let's go! Note how I'm prompting the model using the keys in the training data. Always remember that LLMs simply \"predict\" the next word.\n", | |
| "\n", | |
| "Let's start with something generic and then try to engineer the prompt to produce something more relevant." | |
| ], | |
| "metadata": { | |
| "id": "gJhkKJX_LaZb" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "transformers.logging.set_verbosity_error()\n", | |
| "print(\"\\n[\" + generate(\"[Intro]\\n\").split('[')[1])\n", | |
| "print(\"\\n[\" + generate(\"[Verse 1]\\n\").split('[')[1])\n", | |
| "print(\"\\n[\" + generate(\"[Bridge]\\n\").split('[')[1])\n", | |
| "print(\"\\n[\" + generate(\"[Chorus]\\n\").split('[')[1])\n", | |
| "print(\"\\n[\" + generate(\"[Verse 2]\\n\").split('[')[1])\n", | |
| "print(\"\\n[\" + generate(\"[Outro]\\n\").split('[')[1])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "lMjMAtC4SvO1", | |
| "outputId": "aa5b3f01-e25b-48d1-e798-cacfb93b7d33" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "[Intro]\n", | |
| "One, two, three, four\n", | |
| "One, two... (One, two, three, four)\n", | |
| "\n", | |
| "(Yahoo)\n", | |
| "\n", | |
| "(I wanna be your dog)\n", | |
| "\n", | |
| "(Yahoo)\n", | |
| "\n", | |
| "(I wanna be your dog)\n", | |
| "\n", | |
| "(\n", | |
| "\n", | |
| "[Verse 1]\n", | |
| "And the band played on\n", | |
| "And the people came, and they saw that it was good\n", | |
| "And they were satisfied\n", | |
| "They were satisfied\n", | |
| "\n", | |
| "(I say the word)\n", | |
| "(She says the word)\n", | |
| "\n", | |
| "(And they'll understand)\n", | |
| "\n", | |
| "\n", | |
| "[Bridge]\n", | |
| "Oh how long will it take\n", | |
| "Till she sees the mistake she has made\n", | |
| "Till she sees the mistake she has made\n", | |
| "\n", | |
| "(One, two, three, four, five, six, seven, eight, nine, ten, eleven!)\n", | |
| "\n", | |
| "(\n", | |
| "\n", | |
| "[Chorus]\n", | |
| "Come on (Come on), Come on (Come on)\n", | |
| "Come on (Come on), Come on (Come on)\n", | |
| "Please please me, whoa yeah, like I please you\n", | |
| "Like I please you\n", | |
| "\n", | |
| "(Come\n", | |
| "\n", | |
| "[Verse 2]\n", | |
| "Ring, my friend I said you'd call\n", | |
| "Doctor Robert\n", | |
| "Early morning raingt\n", | |
| "Doctor Robert\n", | |
| "Fool, you don't need him does he fool you does he?\n", | |
| "Doctor Robert\n", | |
| "Doctor Robert\n", | |
| "Doctor Robert\n", | |
| "\n", | |
| "(Ring\n", | |
| "\n", | |
| "[Outro]\n", | |
| "I don't want to leave her now\n", | |
| "You know I believe and how\n", | |
| "I hope she will forgive me somehow\n", | |
| "When I see her, I start to sing\n", | |
| "\n", | |
| "(Sing it again)\n", | |
| "\n", | |
| "(Oh yeah, sing it again)\n", | |
| "\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(\"\\n[\" + generate(\"[Intro]\\nThis is a song about language models\\n\").split('[')[1])\n", | |
| "print(\"\\n[\" + generate(\"[Verse 1]\\nIn a deep dive, you learned how they work\\n\").split('[')[1])\n", | |
| "print(\"\\n[\" + generate(\"[Bridge]\\nBut wait, the data\\n\").split('[')[1])\n", | |
| "print(\"\\n[\" + generate(\"[Chorus]\\nThis is a language model\\n\").split('[')[1])\n", | |
| "print(\"\\n[\" + generate(\"[Verse 2]\\nNext you want to deploy\\n\").split('[')[1])\n", | |
| "print(\"\\n[\" + generate(\"[Outro]\\nI hoped you enjoyed this talk\\n\").split('[')[1])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "OgILW9yBSxZ2", | |
| "outputId": "0a0844fe-87ca-41b1-b0a6-4a1c2a4a1d72" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "[Intro]\n", | |
| "This is a song about language models\n", | |
| "And the structures that they contain\n", | |
| "And the way that they repeat themselves\n", | |
| "And the words that they surround\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "[Verse 1]\n", | |
| "In a deep dive, you learned how they work\n", | |
| "And now you're part of the corporation\n", | |
| "They'll take you in, screwed up or torn\n", | |
| "Solve all your problems for a price or a song\n", | |
| "\n", | |
| "(Oh!)\n", | |
| "\n", | |
| "'Cause they'll be there, awaiting your call\n", | |
| "\n", | |
| "\n", | |
| "[Bridge]\n", | |
| "But wait, the data\n", | |
| "Presents another approach\n", | |
| "You may observe the people passing by\n", | |
| "And you'll soon realize\n", | |
| "That they're all living lives that are prescribed\n", | |
| "And you'll see that they're all the same\n", | |
| "And you know it's\n", | |
| "\n", | |
| "[Chorus]\n", | |
| "This is a language model\n", | |
| "It's called Smokey Tongue\n", | |
| "It can help you if you let it\n", | |
| "It can help you if you let it\n", | |
| "It can help you if you let it\n", | |
| "It can help you if you let it\n", | |
| "\n", | |
| "(Instrumental Break\n", | |
| "\n", | |
| "[Verse 2]\n", | |
| "Next you want to deploy\n", | |
| "The same old thing again\n", | |
| "If I've said it once I've said it a hundred times\n", | |
| "It's no use, you know, you'll never get it in your mind\n", | |
| "If I've said it once I'\n", | |
| "\n", | |
| "[Outro]\n", | |
| "I hoped you enjoyed this talk\n", | |
| "And trust you will come again\n", | |
| "The next time we'll talk\n", | |
| "We'll have another tea and scone\n", | |
| "But for now it's time to say good-bye\n", | |
| "Good-bye\n", | |
| "\n", | |
| "(She's leaving home)\n", | |
| "\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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