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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." ], "metadata": { "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", "name": "stdout", "text": [ "\u001b[2K 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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": [ "7e9fc2c5f36d4880bf2f90dea2d18d34", "29f1688610b74c19ac3a5a32af9a687b", "fc9828f476a04e3faf58ec1853635722", "c3bb3973a4e946cc8472f806ea929485", "76bbbd1218dc4c85982646e80d6189a0", "ee29ce60171549be874855abfda8b3f8", "bb208ed81c6643ceab04b0c7ec5f3f5c", "6697e1bc440144fc867fa2c7502ae986", "95ef818b392d4264b5027fdfcfb78c1f", "02ab77ec7cc0421190494f270ff0a2b9", "1427c337f68a4e7a90efe9dc8b5c3db9", "79307a9372794bdb8001544ffceef09f", "c89a537c4c5a4e979f73e87eca32d1a4", "bd6ce99f3df540a9b91d5aae20fc8130", "dce1f1a174604a1da6b7e9d94b456699", "507c90fb6f9c4789afde485330bd0c2b", "ac476c8784bb42a4942d4e7377440fcd", "08ac72ea44774eada1e124e4eb816d92", "ca959149d31844478652255db246fd33", "7cfbe37370d143879859513152acc636", "2d84e1a55e81491f97121679d7752b7f", "b606a58d95504ffda7187c7ca5cb908e", "ed12dbe21f524cd7aa4b0d10fd5a4322", "0fcdb0588e7240439a2ab135d509e30c", "4eae7a84ba324332877c00fa4690a798", "df9b364f522744d8b9b6b914db16ff79", "9a3df9a075e54f999c24deb2658fd788", "9069374a36d94445a40dbb19722fba7f", "394ea230eb5a491f877d14a7a140356b", "a6431a77618f40bda472bfed3acb354b", "123625b8a6a14611877ca8a824e3cff4", "9d0dc60b1d0d493ea9beb53f38819d97", "92f652055e26479d82ddc70d8b23d78e", "8a42f39cda1549f2975338a5e5fc74be", "e204dd11df764c1788681480f9b0814c", "024f2a59340e48edaaf75818ca61d3f9", "ee50f6b79b9c48a18ff3bfb28215c52e", "1311e5554c7e4ffd9e2b6aaf977fe9d0", "32a913dd69714a3b82f3c63553ef5f4c", "ac779011110c427a8cd2a108770036b5", "3cde604f16f74640a327de373518808b", "290137fa43a94001a78fd65f4fbddec2", "8fc403d45edf4b6c9212fbea3269325a", "0cd5c5fafa624d00b5a554086addf5fb", "7dfbbfe6d5a349d8b031a32a92bbe703", "cf60dc34e43f4c748eb72a299cf47f3d", "06677ef761fd4f7bab5d099558a95704", "90eed6fb6c0346d28efbd7cbbe06f2ab", "d81fced6f4f64223a1d62ea994476706", "ac3f8c8dce7540b18d65b300e0b1ffd9", "9ba8fe63c0904d43809709cd2199f2ed", "f0833453aa3440b29ad6fea5084ab81b", "d10a86eaf3cd4f0484fad065d75650f9", "0c8a774749744bcbab763a0fb810cd0a", "67ef87a26843441c8cc7822b0243976d", "b6e8e4ef467f468bb92cbbfc1683cb9d", "b66f6621fdcf41458b4c9c79375971e6", "fe3f0df027344d5d9bbc208e01486e9c", "78a7211279174e5098960010f93093c4", "5a75d8a497bb43328fa31da025d4e3b7", "b7efde1e16dd41d293377beba720845d", "2914d20737584146a0aff19f78b607f8", "7b61d487cf4642378e2b6c671cc9a206", "ca9c1b08fbef4152bc713311995126cf", "e846ccc460ae489a81cd258d9969353d", "b310acab7bb9439da23ab0a5ebf7ae2d", "a9909fea4e294bd0b453beb322d993cd", "10dddefc38b14c138e44438d762f7246", "0ae5624297c84a6aad3654b6e36f22c4", "1845ef0d733c48acaf2db74557975913", "b56ead7bc8ef456ca22ba5d98a97e30e", "09243b2fd64e43d29964c9ed3a44123a", "bb61eed234d248e68363bd439c02e71d", "bddf0aeb01cb4b7e8e53722b2d3c4566", "a75f90df16c74d11bd8f8dee8defeca4", "e9dfbe44885c4ec4b2baeedba30553de", "24690c6a6ef747cba00ac2f8d6f34c7d", "1b89df1ca5a446ff927215d10d570f40", "1e8ac6ca6d1a40208e39499f1225f2ba", "6feb7d56b1fd4fc7bbe131522c9f74b9", "96a9c98f739f4ec8886a8ceb5622ee86", "57d5d3c4136440c3909b2cc07b096d88", "0f1cf6724a944fc28fdb966edd995b2f", "a1a3571f4be64a5ca46ffa9b14d56260", "ed73267f86f44638a55d962cae105e3c", "20c45aaf7a4b4a0d92b8c97ce6c9c878", "a3dec06f78c24c278f48870173294458", "4af7ebbb6bcc4cb78f5e74088ab75e37", "8caa9adaa8e24490a398b7865baa4024", "128ec47421ee4a3daa560c526c3f6781", "d33ab01243384585b6ec95a612f099e8", "5a647144ee284d56a88e15d85730bedc", "e2402adb14674bb4bae6b5ce2d6478e4", "06ce7d4374aa438a826e0b4ac11894ca", "6a591cf67e4c4877adfb4c58ab71e4d7", "786daf98c12640e48964f038d0c1b062", "a5d4b5e4963840cc9a9430bd008b4824", "8cee4603628a47f2b5a2a277f3f2e8eb", "ac146674b0e54b788a50fd7a16cf0e7d", "ed614e15444e4b65bd1f75204892fdb2", "96a7ade0487a49c5a5bf745904f1d474", "fae48e463b9d4e66b7e8918adfd2493b", "09238cf5cfa34dcead1f5a977d4df5fd", "ac7a6f6f00ef42339f80b0801b03db2b", "53cc5770a07442bcb66da4f8daa89477", "925a18a286004952a6a69168eb155469", "1f108aedc3664bb3a794cf429f6db2e7", "09a7de9e1c9e4cb18e97ad55ead68458", "d883982af0b2495bad4983c8e7bfa498", "6a010cad26c54fefb75c5f971821842e", "ff2654ebb1204b4aba3bff7da0133868", "e9fe2a3e3d814957a88c05f55160daea", "707d49cbf5ba4389bcbb0f70a6c80d1b", "fe40e3ab9a4a42e592d406374a1b97a4", "f7e69337d0e042c3b7ea37f8fc4163fb", "40889a1595454439905c0ba0e8e32f8a", "6276c836e2444b7293773276fbf8a2c0", "7780924d09074621951317e7333b55ae", "0582ed11d5ba4905b8561ce9f098ee43", "4c9608ae07ef4f67acee238c97037aa7", "7849ca179047448197f7f3855507f861", "31ba75235e7142a7add2ff5003daba0a", "0ae8561032794128a85ee3111fda56a2", "5fbad335a7ba44949d2d8b522e4ab89d", "86c486d52e7245cb983ca85c3abc3ac5", "de1a14ba21d94f8992c1cf9ddb81f450", "8399da94b9874603ab07578489784336", "f0773dac18cc4a568f20f1aadc04d972", "4b194042239d43369eedc9595fae095e", "750fc755ff634331a2e810ddc967f3fc", "8e3ae9aae276402094480ddf505427e6", "7f1813982b09412e8958435dd6b16182" ] }, "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 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", "9c8f24f2ed1e40fda1f26731fd4cfebd", "ada00b849f984279985616986cd3e7b8", "c79f55e89b3748f8b176873ce9165661", "8bd704ce45fa4f3ea440be6800898498", "93e8d7ee244e4bc38144407bc42eb0d6", "ac4e0800a54b499b857fce0f7057c759", "4bb1bf2f40c34b788417211974b7aa99", "493c76b31fa14af1bf53dbf33283ee63", "a49b1746c7cf486a8b6df0aa2bc6bf91", "f8f18b7f9a7c41598273cb41765b8960", "070f33014131494b97bbfd2d4c6ecbc2", "ed823bfe29584c9aad0e8a3d7533c197", "acb41264136f420791c2e9d5a50c47cd", "b806da6313484c7a918f8af29e541b8c", "a9f332a16d124f39b343b3aee920c077", "9053e231af8b492aa2b3ca65e92ffa07", "6dbe50e8c265453ebef214dfe46aa227", "9f691b4a1c874d1b95db05a5297f4fdd", "16ace4daaeac40a8822bc67d6c63c431", "a0f06c90b5ee408fa7d5f1b783e36141", "09c7d49cb8354cdf9611630af8e674e6", "ab7e7ed432ac4458b1f02af891609b6f", "b5fed879c13848dcbb06c06500fc7991", "3d9d6881d14b498bb87892d7354c1716", "8c4e5ddc45584140b5d4eecdf7cf8b61", "0bae8254940b4d9784163f32566ab33d", "fcea70ff2aee422189c61ec1da7b6349", "8562f4b23c724b9a8de3bdd92dbe7989", "a6409048311d45e98cd3da596182116e", "19a840a9665e4678b48e1173600f71ba", "9697afc831ce4100b87cddcc88da11e3", "8a538895183b4d2596df41f4b589096b", "e66e77b7acc8411e80d5a90f29810fbc", "1635a67ea0cb45b0b6676d7843369d04", "13bf68b11fbe4bc39b13a6322584175d", "37e5e18598a44d36956b82e1eadf58e0", "ccec4c5673494a4e83f8a22fe7cc627b", "02e98795bc5b4dcabb0170de29203380", "815b0cc10c6042369e1d03780bbf8102", "8f555f2074b64af6afb670cadee98b8e", "ccdddc9074c34923a086cea9b025751a", "ade79a6682de41378aa548fde0c1dc2a", "9403a180592e41aca75b3c5721488530", "dc6e8c05e50a4afcb1e18d0f0d5f7ff7", "b7f4efb5847045a78b0f86e2c9217ab6", "a71e364e78c44d97bf86332919b82885", "31f8236e79ed4ecbab7dd25b21403b62", "3e7287fe92eb4f7890b2ddfd496ebca8", "06498b2273484a63982c124419592a9a", "edcf8845d7a14cc4a22a5be2c8de8ed0", "7c878d5382e14eb49c026a96ca33ea18", "b896803178674451b809baa4a0eda062", "1801c853212945549e34ab835a15917c", "13dc4f08efc8466197c52a93bb50d6e8" ] }, "id": "vy9kvzvSVX5_", "outputId": "61222baa-a070-40b4-e07b-c76d9bee0290" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading metadata: 0%| | 0.00/1.01k [00:00 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", "22a745f9ad524f09b72fa76e3ddb41f2", "eda7847f92cf4c1f95423b9f4bb33102", "98990e011df74196a3162610fe07d1e5", "e46f4719d4684759b295b975b3253617", "ffe9f186281e44acb2e61d5f16f1fdd0", "39fadca8ea284304b843e8576dfe1875", "42310f330a4c4c3db136c2e1298564ef", "27e094f4359346a9bb05e88f755f9f74", "3c9ebd7fb18c415793bc94893c346083" ] }, "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": [ "" ], "text/html": [ "\n", "
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StepTraining Loss
5251.157000
5501.317600
5751.205000
6001.274100
6251.275500
6501.289200
6751.154400
7001.140200
7251.177400
7501.072500
7751.157700
8001.017800
8251.030200
8500.926700
8751.017300
9000.955100
9250.881700
9500.954700
9750.939900
10000.910200
10250.791700
10500.823100
10750.776600
11000.813600
11250.790400
11500.848300
11750.933600
12000.679000
12250.663400
12500.720600
12750.650100
13000.655800
13250.724900
13500.703000
13750.569400
14000.537900
14250.606400
14500.631300
14750.608900
15000.614900
15250.511400
15500.510400
15750.452000
16000.520100
16250.527200
16500.520400
16750.584000
17000.429200
17250.434600
17500.461700
17750.447100
18000.439800
18250.449900
18500.457300
18750.390200
19000.372600
19250.408500
19500.414600
19750.420800
20000.382200
20250.368600
20500.368000
20750.386200
21000.376900
21250.340500
21500.347000
21750.379200
22000.330500
22250.332600
22500.325600
22750.330200
23000.302700
23250.345900
23500.368500
23750.285400
24000.297600
24250.302000
24500.324900
24750.329800
25000.282400
25250.339900
25500.272000
25750.298900
26000.277500
26250.281700
26500.282400
26750.292500
27000.303900
27250.252600
27500.259400
27750.280600
28000.261500
28250.292000
28500.278800
28750.256500
29000.240400
29250.255000
29500.264700
29750.266600
30000.270200
30250.283700
30500.248700
30750.235900
31000.259000
31250.246400
31500.274100
31750.251800
32000.233200
32250.227200
32500.239300
32750.262100
33000.242500
33250.255300
33500.249900
33750.241000
34000.221600
34250.246400
34500.224600
34750.239200
35000.247300
35250.241100
35500.223300
35750.212000
36000.229400
36250.248400
36500.228100
36750.231600
37000.238500
37250.221000
37500.246100
37750.218400
38000.210000
38250.229600
38500.232100
38750.214800
39000.208700
39250.208200
39500.231400
39750.223900
40000.229800
40250.230300
40500.216700
40750.204100
41000.211800
41250.217500
41500.213200
41750.240600
42000.220500
42250.206400
42500.220100
42750.215000
43000.212600
43250.200000
43500.214100
43750.226500
44000.216400
44250.218200
44500.215200
44750.203100
45000.218200
45250.201800
45500.213300
45750.199000
46000.208400
46250.215700
46500.219200
46750.183800
47000.226600
47250.204100
47500.198000
47750.206400
48000.204100
48250.204400
48500.206300
48750.215900
49000.201100
49250.199000
49500.200600
49750.198200
50000.223200
50250.211700
50500.190800
50750.199700
51000.189100
51250.205500
51500.210600
51750.211000
52000.208500
52250.198300
52500.207000
52750.208300
53000.181800
53250.197800
53500.205400
53750.208400
54000.185300
54250.174800
54500.199500
54750.199200
55000.215900
55250.212600
55500.209500
55750.199400
56000.186800

" ] }, "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\u001b[0m in \u001b[0;36m\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 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"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 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" ] } ] } ] }