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AmarOk1412/fine_tune_gemma_model.ipynb

Created August 26, 2024 18:33
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fine_tune_gemma_model.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/AmarOk1412/edf86f72966768177b685099b76499a7/fine_tune_gemma_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZeKxtPb0_qdJ"
},
"source": [
"#### Step 0: Install libraries."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cQEjhcM0_q02",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4a294a18-7fc5-4041-f9d1-1e8c757aa614"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m183.4/183.4 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m150.9/150.9 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.6/104.6 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m527.3/527.3 kB\u001b[0m \u001b[31m34.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
"ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m279.7/279.7 kB\u001b[0m \u001b[31m19.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.6/536.6 kB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.4/166.4 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
"gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2023.10.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m131.1/131.1 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.5/8.5 MB\u001b[0m \u001b[31m110.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m97.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"!pip3 install -q -U bitsandbytes==0.42.0\n",
"!pip3 install -q -U peft==0.8.2\n",
"!pip3 install -q -U trl==0.7.10\n",
"!pip3 install -q -U accelerate==0.27.1\n",
"!pip3 install -q -U datasets==2.17.0\n",
"!pip3 install -q -U transformers==4.38.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fuSBT9IaABhG"
},
"source": [
"#### Import libraries."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "__2he7qQAC3u"
},
"outputs": [],
"source": [
"import os\n",
"import transformers\n",
"import torch\n",
"from datasets import load_dataset, Dataset, DatasetDict\n",
"from trl import SFTTrainer\n",
"from peft import LoraConfig, PeftModel\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
"from transformers import BitsAndBytesConfig, GemmaTokenizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
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},
"id": "yspg2acUAZpi",
"outputId": "903a8f27-b00b-4ce8-873a-6897f7aa9515"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2e6be3bd26564b9facccddf7e3158337"
}
},
"metadata": {}
}
],
"source": [
"# Login to Hugging Face account.\n",
"from huggingface_hub import notebook_login\n",
"notebook_login()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SiORMDrpzsfN"
},
"source": [
"#### Step 1: Download Gemma-2b-it base model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Zc-Y7EVWAr2P"
},
"outputs": [],
"source": [
"model_id = \"google/gemma-2b-it\"\n",
"bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_quant_type = \"nf4\",\n",
" bnb_4bit_compute_dtype = torch.bfloat16\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4DCkivEnA88x",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 528,
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"outputId": "9f28324b-83c2-46a6-d366-42f35306d5a1"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
" warnings.warn(\n",
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
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"metadata": {}
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{
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"metadata": {}
},
{
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"metadata": {}
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{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"version_major": 2,
"version_minor": 0,
"model_id": "1eb184900495453aa50053ae8d68d358"
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
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],
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"version_minor": 0,
"model_id": "efce3e4276f94542aaf724babf0b0806"
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"metadata": {}
}
],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
"model = AutoModelForCausalLM.from_pretrained(model_id,\n",
" quantization_config = bnb_config,\n",
" device_map={\"\":0})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-lSe_TerBL4B",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ab3a7687-f1cf-4382-d65a-3a7592369afb"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<bos>Complete the dialogue: I'll make him an offer, he ___________________.\n",
"\n",
"The correct answer is \"will accept\".\n",
"\n",
"The dialogue could be continued as follows:\n",
"\n",
"I'll make him an offer, he **will accept** it.<eos>\n"
]
}
],
"source": [
"text = \"Complete the dialogue: I'll make him an offer, he \"\n",
"device = \"cuda:0\"\n",
"inputs = tokenizer(text, return_tensors = \"pt\").to(device)\n",
"\n",
"outputs = model.generate(**inputs, max_new_tokens=100)\n",
"print(tokenizer.decode(outputs[0], skip_special_tokens=False))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KsKiqRtwz5V8"
},
"source": [
"#### Step 2: Configure LoRA settings for modules to be trained"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_w3TdQ20Dpjw"
},
"outputs": [],
"source": [
"os.environ[\"WANDB_DISABLE\"] = \"false\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ii06zZTfD-B_"
},
"outputs": [],
"source": [
"lora_config = LoraConfig(\n",
" r = 8,\n",
" target_modules = [\"q_proj\", \"o_proj\", \"k_proj\", \"v_proj\",\n",
" \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
" task_type = \"CAUSAL_LM\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r5H4lsvp0Gyf"
},
"source": [
"#### Step 3: Load the dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Y8oqQmfkEpb2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
"referenced_widgets": [
"014cacb5b45f43fb9b0ac3cdeef9444c",
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"4e72544d7d754b5db6bf9d8b91662463",
"595ff76a9c2841318ee9111a06323ba3",
"89432ea1ba3f49c99bb773b7b9940d69",
"c111337e176d4252ada9f0e1a3bdafc2",
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"40d170bb4b944637a97096b7596ee94a",
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"7797ede8530d439c84fb7fa083cd539a",
"95e41e4cad384aa48a0805feb290956f",
"308cdc4deb9b4a3a979dba6b35e97234",
"91c4d6ef2e97487ea9c8418c520687f7",
"469c67bb6bd44e6e83af2fba751ac284",
"a5735639a13949708a3dfb925307a0d7",
"818f54092df64679b8eedbbc87810d6c",
"0a601f67c2c549f28246b891c5b7684c",
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"05ea0a6542e845afbfc53c98c17ed611",
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]
},
"outputId": "7d34b98a-6a3d-4fc7-8d76-cfff3a0a0536"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading data: 0%| | 0.00/169k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "014cacb5b45f43fb9b0ac3cdeef9444c"
}
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"metadata": {}
},
{
"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": "95e41e4cad384aa48a0805feb290956f"
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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/1205 [00:00<?, ? examples/s]"
],
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"version_major": 2,
"version_minor": 0,
"model_id": "9a6ae422a9fe4a789fb2a4a183fe9460"
}
},
"metadata": {}
}
],
"source": [
"data = load_dataset(\"AmarOk1412/ingredients\")\n",
"data = data.map(lambda samples: tokenizer(samples[\"Question\"], samples[\"Context\"]), batched=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0t50PC1tE_pI"
},
"outputs": [],
"source": [
"prompt = \"\"\"\n",
"\n",
"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
"You're a bot parsing ingredients from recipes that needs to provide a structure to use in a list. The answer is a list of ingredients with optional quantrity and unit.\n",
"There can be several ingredients in the same input. This will result in a list with multiple ingredients. e.g. 'sel et poivre' will give \t\"sel\" and \"poivre\" as ingredients.\n",
"Remove useless words, e.g. \"sel au goût\", \"au goût\" is not a part of the ingredient and useless in the list.\n",
"\n",
"### Instruction:\n",
"{}\n",
"\n",
"### Input:\n",
"{}\n",
"\n",
"### Response:\n",
"{}\"\"\"\n",
"\n",
"def formatting_prompts_func(examples):\n",
" instructions = examples[\"Question\"]\n",
" inputs = examples[\"Context\"]\n",
" outputs = examples[\"Answer\"]\n",
" texts = []\n",
" for instruction, input, output in zip(instructions, inputs, outputs):\n",
" # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
" text = prompt.format(instruction, input, output) + \"<eos>\"\n",
" texts.append(text)\n",
" return texts"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FtFIEyU1JDjy"
},
"source": [
"#### Step 4: Configure supervised fine-tuning parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4MZERT9EGFiN",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 138,
"referenced_widgets": [
"da09faa137f44ceda6764c96c7c8b7b2",
"836281ec3b5c46c1b1396488e256a25c",
"a839009446c149499d95f036ea2fcf70",
"fda2778dc9394d088a90f0d6fbf9f234",
"6b6a2161dcf54707a0ad30afa3243613",
"800b7df8ba004fa79683633f2067d3ed",
"db8424829b4c4ab7b878652108e160a0",
"db3deeae1b754b96a4f6f41d78f0d5fc",
"89f492d1b3bd463aa0aa6e8b05f77a3d",
"ae6e0d2a282c4c2090a238d385ba2a22",
"a54c26177ed849f884a92f1100b6dd76"
]
},
"outputId": "cf207673-4b86-4d83-819a-84899968553d"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:223: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/1205 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "da09faa137f44ceda6764c96c7c8b7b2"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:290: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
" warnings.warn(\n"
]
}
],
"source": [
"trainer = SFTTrainer(\n",
" model = model,\n",
" train_dataset = data[\"train\"],\n",
" args = transformers.TrainingArguments(\n",
" per_device_train_batch_size = 2, # 4\n",
" gradient_accumulation_steps = 8, # 4\n",
" warmup_steps = 2,\n",
" max_steps = 255,\n",
" learning_rate = 2e-4,\n",
" fp16 = True,\n",
" logging_steps = 1,\n",
" output_dir = \"outputs\",\n",
" optim = \"paged_adamw_8bit\"\n",
" ),\n",
" peft_config = lora_config,\n",
" formatting_func = formatting_prompts_func\n",
"\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ct02smouJI7J"
},
"source": [
"#### Step 5: Start model fine-tuning."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FoA2nDjkGWD7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "1146a993-eb61-40fe-fbee-389d65dd0cdf"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='255' max='255' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [255/255 20:48, Epoch 3/4]\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>1</td>\n",
" <td>4.386100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>4.332900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>4.048700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>3.536500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>3.046000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>2.580700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>2.353600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.922100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>1.628200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>1.402800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>1.164100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>0.893300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>0.710800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>0.586100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.486100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.390900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>0.328800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>0.358900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>0.307200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.205200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.263500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>0.192300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.258600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>0.216600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>0.222800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>0.279900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>0.199600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.209000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.206000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.174300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>0.173200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>0.166300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>0.198700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>0.136400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>0.146100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>0.128300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>0.124800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>0.154600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>0.158200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>0.173700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>0.172100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>0.132600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>0.173600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>0.141000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>0.136000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>0.154900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>0.140400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>0.136900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>0.144000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>0.166200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>0.195200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>0.128300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>0.133200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>0.112900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>0.138100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>0.103300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>0.175000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>0.147500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>0.091700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>0.173500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>61</td>\n",
" <td>0.136400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>62</td>\n",
" <td>0.077800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>63</td>\n",
" <td>0.090400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>64</td>\n",
" <td>0.135700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>65</td>\n",
" <td>0.132000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>66</td>\n",
" <td>0.099800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>67</td>\n",
" <td>0.105200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>68</td>\n",
" <td>0.101100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>69</td>\n",
" <td>0.182900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>70</td>\n",
" <td>0.131700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>71</td>\n",
" <td>0.155400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>72</td>\n",
" <td>0.184000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>73</td>\n",
" <td>0.123400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>74</td>\n",
" <td>0.120200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75</td>\n",
" <td>0.137000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>76</td>\n",
" <td>0.129100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>77</td>\n",
" <td>0.129600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>78</td>\n",
" <td>0.127400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>79</td>\n",
" <td>0.088900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>80</td>\n",
" <td>0.114500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>81</td>\n",
" <td>0.089800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>82</td>\n",
" <td>0.122300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>83</td>\n",
" <td>0.092800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>84</td>\n",
" <td>0.098500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>85</td>\n",
" <td>0.122000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>86</td>\n",
" <td>0.088000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>87</td>\n",
" <td>0.118800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>88</td>\n",
" <td>0.095100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>89</td>\n",
" <td>0.077900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>90</td>\n",
" <td>0.080700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>91</td>\n",
" <td>0.119900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>92</td>\n",
" <td>0.059100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>93</td>\n",
" <td>0.106000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>94</td>\n",
" <td>0.118600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>95</td>\n",
" <td>0.110800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>96</td>\n",
" <td>0.082600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>97</td>\n",
" <td>0.104300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>98</td>\n",
" <td>0.114800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>99</td>\n",
" <td>0.120300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>0.109500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>101</td>\n",
" <td>0.097900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>102</td>\n",
" <td>0.135000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>103</td>\n",
" <td>0.091700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>104</td>\n",
" <td>0.077800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>105</td>\n",
" <td>0.090500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>106</td>\n",
" <td>0.087000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>107</td>\n",
" <td>0.085900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>108</td>\n",
" <td>0.105200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>109</td>\n",
" <td>0.101900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>110</td>\n",
" <td>0.126400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>111</td>\n",
" <td>0.095100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>112</td>\n",
" <td>0.098400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>113</td>\n",
" <td>0.110000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>114</td>\n",
" <td>0.103800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>115</td>\n",
" <td>0.148800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>116</td>\n",
" <td>0.088100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>117</td>\n",
" <td>0.101700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>118</td>\n",
" <td>0.125300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>119</td>\n",
" <td>0.086800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>120</td>\n",
" <td>0.077400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>121</td>\n",
" <td>0.100700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>122</td>\n",
" <td>0.116200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>123</td>\n",
" <td>0.103500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>124</td>\n",
" <td>0.101200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>125</td>\n",
" <td>0.079000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>126</td>\n",
" <td>0.124100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>127</td>\n",
" <td>0.079300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>128</td>\n",
" <td>0.104700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>129</td>\n",
" <td>0.087800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>130</td>\n",
" <td>0.086200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>131</td>\n",
" <td>0.095700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>132</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>133</td>\n",
" <td>0.098900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>134</td>\n",
" <td>0.110100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>135</td>\n",
" <td>0.130700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>136</td>\n",
" <td>0.104000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>137</td>\n",
" <td>0.111300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>138</td>\n",
" <td>0.094300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>139</td>\n",
" <td>0.137000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>140</td>\n",
" <td>0.085700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>141</td>\n",
" <td>0.118500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>142</td>\n",
" <td>0.076400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>143</td>\n",
" <td>0.089100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>144</td>\n",
" <td>0.074500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>145</td>\n",
" <td>0.085500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>146</td>\n",
" <td>0.103600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>147</td>\n",
" <td>0.105500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>148</td>\n",
" <td>0.125200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>149</td>\n",
" <td>0.085900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>0.085300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>151</td>\n",
" <td>0.087800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>152</td>\n",
" <td>0.073900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>153</td>\n",
" <td>0.077700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>154</td>\n",
" <td>0.082800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>155</td>\n",
" <td>0.074000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>156</td>\n",
" <td>0.081500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>157</td>\n",
" <td>0.065900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>158</td>\n",
" <td>0.092400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>159</td>\n",
" <td>0.082400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>160</td>\n",
" <td>0.070300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>161</td>\n",
" <td>0.082200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>162</td>\n",
" <td>0.065600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>163</td>\n",
" <td>0.091000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>164</td>\n",
" <td>0.116800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>165</td>\n",
" <td>0.095400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>166</td>\n",
" <td>0.077700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>167</td>\n",
" <td>0.087100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>168</td>\n",
" <td>0.076700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>169</td>\n",
" <td>0.061800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>170</td>\n",
" <td>0.086700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>171</td>\n",
" <td>0.061400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>172</td>\n",
" <td>0.060200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>173</td>\n",
" <td>0.080500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>174</td>\n",
" <td>0.084400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>175</td>\n",
" <td>0.094500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>176</td>\n",
" <td>0.088000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>177</td>\n",
" <td>0.073100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>178</td>\n",
" <td>0.083400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>179</td>\n",
" <td>0.080500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>180</td>\n",
" <td>0.080300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>181</td>\n",
" <td>0.080400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>182</td>\n",
" <td>0.070700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>183</td>\n",
" <td>0.082500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>184</td>\n",
" <td>0.066400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>185</td>\n",
" <td>0.081800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>186</td>\n",
" <td>0.078000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>187</td>\n",
" <td>0.089400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>188</td>\n",
" <td>0.073400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>189</td>\n",
" <td>0.087800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>190</td>\n",
" <td>0.062700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>191</td>\n",
" <td>0.073600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>192</td>\n",
" <td>0.073800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>193</td>\n",
" <td>0.073600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>194</td>\n",
" <td>0.084700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>195</td>\n",
" <td>0.094100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>196</td>\n",
" <td>0.086500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>197</td>\n",
" <td>0.082500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>198</td>\n",
" <td>0.094700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>199</td>\n",
" <td>0.076100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>0.077100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>201</td>\n",
" <td>0.086700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>202</td>\n",
" <td>0.067100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>203</td>\n",
" <td>0.076700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>204</td>\n",
" <td>0.109000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>205</td>\n",
" <td>0.081300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>206</td>\n",
" <td>0.098300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>207</td>\n",
" <td>0.073300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>208</td>\n",
" <td>0.071600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>209</td>\n",
" <td>0.071900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>210</td>\n",
" <td>0.087000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>211</td>\n",
" <td>0.102600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>212</td>\n",
" <td>0.076600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>213</td>\n",
" <td>0.059400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>214</td>\n",
" <td>0.073200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>215</td>\n",
" <td>0.082000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>216</td>\n",
" <td>0.109700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>217</td>\n",
" <td>0.080700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>218</td>\n",
" <td>0.077500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>219</td>\n",
" <td>0.058000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>220</td>\n",
" <td>0.071400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>221</td>\n",
" <td>0.069300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>222</td>\n",
" <td>0.061100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>223</td>\n",
" <td>0.054300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>224</td>\n",
" <td>0.096700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>225</td>\n",
" <td>0.084800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>226</td>\n",
" <td>0.086700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>227</td>\n",
" <td>0.068900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>228</td>\n",
" <td>0.072700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>229</td>\n",
" <td>0.055900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>230</td>\n",
" <td>0.069700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>231</td>\n",
" <td>0.052200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>232</td>\n",
" <td>0.072600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>233</td>\n",
" <td>0.073100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>234</td>\n",
" <td>0.063000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>235</td>\n",
" <td>0.076600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>236</td>\n",
" <td>0.061600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>237</td>\n",
" <td>0.061400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>238</td>\n",
" <td>0.060400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>239</td>\n",
" <td>0.067300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>240</td>\n",
" <td>0.072500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>241</td>\n",
" <td>0.054800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>242</td>\n",
" <td>0.072300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>243</td>\n",
" <td>0.049500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>244</td>\n",
" <td>0.073600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>245</td>\n",
" <td>0.048200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>246</td>\n",
" <td>0.064100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>247</td>\n",
" <td>0.071800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>248</td>\n",
" <td>0.109100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>249</td>\n",
" <td>0.064400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>0.083100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>251</td>\n",
" <td>0.073500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>252</td>\n",
" <td>0.056800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>253</td>\n",
" <td>0.090200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>254</td>\n",
" <td>0.060600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>255</td>\n",
" <td>0.059700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=255, training_loss=0.23117469955308764, metrics={'train_runtime': 1253.8114, 'train_samples_per_second': 3.254, 'train_steps_per_second': 0.203, 'total_flos': 9489129311846400.0, 'train_loss': 0.23117469955308764, 'epoch': 3.38})"
]
},
"metadata": {},
"execution_count": 12
}
],
"source": [
"trainer.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xMVhZIodL51T"
},
"source": [
"#### Step 6: Inference on the fine-tuned model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0IWodAZ-r-LW",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a6860dac-b6fd-4678-e575-60ceb60ef443"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
"You're a bot parsing ingredients from recipes that needs to provide a structure to use in a list. The answer is a list of ingredients with optional quantrity and unit.\n",
"There can be several ingredients in the same input. This will result in a list with multiple ingredients. e.g. 'sel et poivre' will give \t\"sel\" and \"poivre\" as ingredients.\n",
"Remove useless words, e.g. \"sel au goût\", \"au goût\" is not a part of the ingredient and useless in the list.\n",
"\n",
"### Instruction:\n",
"parse\n",
"\n",
"### Input:\n",
"50cl of green genepi\n",
"\n",
"### Response:\n",
"[{'ingredient': 'genepi', 'quantity': '50', 'unit': 'cl'}]\n"
]
}
],
"source": [
"text = \"50cl of green genepi\"\n",
"\n",
"device = \"cuda:0\"\n",
"\n",
"inputs = tokenizer(prompt.format(\"parse\", text, \"\"), return_tensors=\"pt\").to(device)\n",
"\n",
"outputs = model.generate(**inputs, max_new_tokens=100, eos_token_id=tokenizer.eos_token_id)\n",
"answer = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
"print(answer)"
]
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JENksiiQ_syW",
"outputId": "394423a7-4151-460a-8ee4-2d2191426ee5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qrhRssf4DVrh",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "03fb33f6-aa67-4deb-a694-4671f7acd383"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
"You're a bot parsing ingredients from recipes that needs to provide a structure to use in a list. The answer is a list of ingredients with optional quantrity and unit.\n",
"There can be several ingredients in the same input. This will result in a list with multiple ingredients. e.g. 'sel et poivre' will give \t\"sel\" and \"poivre\" as ingredients.\n",
"Remove useless words, e.g. \"sel au goût\", \"au goût\" is not a part of the ingredient and useless in the list.\n",
"\n",
"### Instruction:\n",
"parse\n",
"\n",
"### Input:\n",
"sel et poivre\n",
"\n",
"### Response:\n",
"[{'ingredient': 'sel', 'quantity': 'None', 'unit': 'None'}, {'ingredient': 'poivre', 'quantity': 'None', 'unit': 'None'}]\n"
]
}
],
"source": [
"text = \"sel et poivre\"\n",
"\n",
"device = \"cuda:0\"\n",
"\n",
"inputs = tokenizer(prompt.format(\"parse\", text, \"\"), return_tensors=\"pt\").to(device)\n",
"\n",
"outputs = model.generate(**inputs, max_new_tokens=100, eos_token_id=tokenizer.eos_token_id)\n",
"answer = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
"print(answer)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zntvhv61JfPc"
},
"source": [
"#### Step 7: Save the fine-tuned model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kVPc9Ge4JhtS",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"8d6e2b65e7b44f99a49b863557c2172a",
"76a9c2681aae4e53b796688c84743914",
"52a076ef7818499795043041ad1f6e30",
"f0a354db4dd44ee08fb9ddbfde4f68ee",
"d0a6bdd3d5e744ceb997fe76cf9872f3",
"242f32b499324b5b81158caf1ce0d0e3",
"da70cca1b4d34a96a3649d06b71e43d5",
"a720e596b8294cfc831678c357f941be",
"bafc4db7c57743e88f1d1b86e161a9e1",
"dea3fe544ebe4974a4204ac0d15e7828",
"bb527302caec4a9ba5c699f465446f2b"
]
},
"outputId": "6f367702-db3e-41a2-b591-616e1ef5e5ec"
},
"outputs": [
{
"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": "8d6e2b65e7b44f99a49b863557c2172a"
}
},
"metadata": {}
}
],
"source": [
"fine_tuned_model = \"fine_tuned_science_gemma2b-it_unmerged\"\n",
"trainer.model.save_pretrained(fine_tuned_model)\n",
"\n",
"# Push the model on Hugging Face.\n",
"base_model = AutoModelForCausalLM.from_pretrained(\n",
" model_id,\n",
" low_cpu_mem_usage = True,\n",
" return_dict = True,\n",
" torch_dtype = torch.float16,\n",
" device_map = {\"\": 0}\n",
")\n",
"\n",
"# Merge the fine-tuned model with LoRA adaption along with the base Gemma 2b-it model.\n",
"fine_tuned_merged_model = PeftModel.from_pretrained(base_model, fine_tuned_model)\n",
"fine_tuned_merged_model = fine_tuned_merged_model.merge_and_unload()\n",
"\n",
"# Save the fine-tuned merged model.\n",
"tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code = True)\n",
"fine_tuned_merged_model.save_pretrained(\"fine_tuned_science_gemma2b-it\", safe_serialization = True)\n",
"tokenizer.save_pretrained(\"fine_tuned_science_gemma2b-it\")\n",
"tokenizer.padding_side = \"right\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oQNZCsZuohAP"
},
"source": [
"#### Step 8: Convert model to MediaPipe format for on-device deployment\n",
"***Note: You might need to restart the runtime before running the below cell.***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IvUqwVFooo9k",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "71199b52-61ad-4f94-e4a7-f15b7fa4a0eb"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting mediapipe\n",
" Downloading mediapipe-0.10.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.7 kB)\n",
"Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from mediapipe) (1.4.0)\n",
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"Collecting protobuf<5,>=4.25.3 (from mediapipe)\n",
" Downloading protobuf-4.25.4-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes)\n",
"Collecting sounddevice>=0.4.4 (from mediapipe)\n",
" Downloading sounddevice-0.5.0-py3-none-any.whl.metadata (1.4 kB)\n",
"Requirement already satisfied: CFFI>=1.0 in /usr/local/lib/python3.10/dist-packages (from sounddevice>=0.4.4->mediapipe) (1.17.0)\n",
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"Downloading mediapipe-0.10.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (35.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.7/35.7 MB\u001b[0m \u001b[31m41.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hDownloading sounddevice-0.5.0-py3-none-any.whl (32 kB)\n",
"Installing collected packages: protobuf, sounddevice, mediapipe\n",
" Attempting uninstall: protobuf\n",
" Found existing installation: protobuf 3.20.3\n",
" Uninstalling protobuf-3.20.3:\n",
" Successfully uninstalled protobuf-3.20.3\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 4.25.4 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed mediapipe-0.10.14 protobuf-4.25.4 sounddevice-0.5.0\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.1+cu121)\n",
"Collecting torch\n",
" Downloading torch-2.4.0-cp310-cp310-manylinux1_x86_64.whl.metadata (26 kB)\n",
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"Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)\n",
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"Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)\n",
" Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
"Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)\n",
" Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
"Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch)\n",
" Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
"Collecting nvidia-cublas-cu12==12.1.3.1 (from torch)\n",
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"Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)\n",
" Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
"Collecting nvidia-curand-cu12==10.3.2.106 (from torch)\n",
" Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
"Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)\n",
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"Installing collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch\n",
" Attempting uninstall: triton\n",
" Found existing installation: triton 2.3.1\n",
" Uninstalling triton-2.3.1:\n",
" Successfully uninstalled triton-2.3.1\n",
" Attempting uninstall: torch\n",
" Found existing installation: torch 2.3.1+cu121\n",
" Uninstalling torch-2.3.1+cu121:\n",
" Successfully uninstalled torch-2.3.1+cu121\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"torchaudio 2.3.1+cu121 requires torch==2.3.1, but you have torch 2.4.0 which is incompatible.\n",
"torchvision 0.18.1+cu121 requires torch==2.3.1, but you have torch 2.4.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105 torch-2.4.0 triton-3.0.0\n",
"Collecting tensorrt\n",
" Downloading tensorrt-10.3.0.tar.gz (16 kB)\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Collecting tensorrt-cu12==10.3.0 (from tensorrt)\n",
" Downloading tensorrt-cu12-10.3.0.tar.gz (18 kB)\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Building wheels for collected packages: tensorrt, tensorrt-cu12\n",
" Building wheel for tensorrt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for tensorrt: filename=tensorrt-10.3.0-py2.py3-none-any.whl size=16337 sha256=a4bec31974d6da87648792e4cd65de9df2b09d03123f629475ee500efc4705c9\n",
" Stored in directory: /root/.cache/pip/wheels/e8/a8/32/bacdf7c14f627d34b90ce8586631e2f196e3fb0fd4eb9add55\n",
" Building wheel for tensorrt-cu12 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for tensorrt-cu12: filename=tensorrt_cu12-10.3.0-py2.py3-none-any.whl size=17554 sha256=f5350be858073ac64cb5328a74541cbd376301320c9a9933f0b0f023d40fb7f9\n",
" Stored in directory: /root/.cache/pip/wheels/14/31/c2/a3ecb74def7087b76ef2a701e6823f03e40ab43348c668a571\n",
"Successfully built tensorrt tensorrt-cu12\n",
"Installing collected packages: tensorrt-cu12, tensorrt\n",
"Successfully installed tensorrt-10.3.0 tensorrt-cu12-10.3.0\n"
]
}
],
"source": [
"!pip install -U mediapipe\n",
"!pip install -U torch\n",
"!pip install -U tensorrt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LjdhYMt-K50S"
},
"outputs": [],
"source": [
"from mediapipe.tasks.python.genai import converter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7RiB__Rto5BR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "20562fa7-1bab-4086-f84d-6c02d8986715"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model converted successfully.\n"
]
}
],
"source": [
"config = converter.ConversionConfig(\n",
" input_ckpt='/content/fine_tuned_science_gemma2b-it/',\n",
" ckpt_format=\"safetensors\",\n",
" model_type=\"GEMMA_2B\",\n",
" backend='gpu',\n",
" output_dir='/content/intermediate/fine_tuned_science_gemma2b-it/',\n",
" combine_file_only=False,\n",
" vocab_model_file=\"/content/fine_tuned_science_gemma2b-it/\",\n",
" output_tflite_file=f'/content/fine_tuned_science_gemma2b-it/scigemma.bin',\n",
")\n",
"\n",
"converter.convert_checkpoint(config)\n",
"\n",
"print(\"Model converted successfully.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JSDuNjIrOv7_"
},
"source": [
"#### Step 9: Push the fine-tuned model on Hugging Face."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EYzJRV137oBi"
},
"outputs": [],
"source": [
"from huggingface_hub import whoami\n",
"from pathlib import Path\n",
"\n",
"# Output directory.\n",
"output_dir = \"fine_tuned_science_gemma2b-it\"\n",
"username = whoami(token=Path(\"/root/.cache/huggingface/\"))[\"name\"]\n",
"repo_id = f\"{username}/{output_dir}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QvSa-fvEKuvo",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 244,
"referenced_widgets": [
"efa372c2cc7a47969a232e9021bb0d0e",
"16a9487de15e4ba3890f64d2a7164315",
"918dadc2f27f4cc8b98e701e7940c418",
"7748c662b5c445f8aad5cbd452fbc4a6",
"c6e25380f0934a61b39476107b865a3c",
"ff8213ed67f94e62802576102cc3758f",
"c70e58cad5fa4e1f986948d8d7a5339d",
"dc573df4db1a4d388eda018fedccfa8d",
"aaf4a794d23046bf98a6ac22b1ed91bb",
"84c68ad620d44c388f75a4983c21bedd",
"6e84ebcad30e46918c5bf7da6ecfce95",
"494d47f2a4bb4d908c0a20f9e8dbfa4c",
"33e5ab50b67a4854a3dc285fd11dd948",
"7c4e58330ec74ff2b659d953188faf90",
"96a451eba1ed4053b6d4f0edee9385d7",
"ae507f253209486180d6abb2cfb6248f",
"3a0cce954fb1455da57198af1a61f63e",
"76e903a3253c43cb89e85633be78910b",
"97e652d6f64e457bbeed3dee670fd4c0",
"fe257e71af194535bc32067a945054a0",
"8e9ff03b9247434f83560fbca694c7e1",
"7027e46d559b42998c91583937124fef",
"51394b59deea4cdea47dfc7892b3f376",
"351fc9d172fa471cba79ab76e5c74664",
"a77d72d138b6494385f91c129ab05ed0",
"9ba32c443fb34204a65cf14ce64e5135",
"49e9ae270dbe428091b2984a97046c3d",
"3cc53ebb1fff4e43af69c687439528c6",
"d2a9fc267072455ab4234b958f6ff653",
"aad88427a8964a09affd11b974f49087",
"68b2819a82334c0d8c5698b866c1888e",
"8e151e5ba8074731b4c282d9ebe9dae3",
"5a5e8c90f7594a2e82f1ba2479f43f2d",
"1e2899783c024d179c000a6240a7aa5d",
"a131345865e041f7aa059bd1ef81fb67",
"64cd1b75e8f94661a3df870452c44ee4",
"b1bf338d3fc94183988f0f596e01fd7b",
"cae1513730c7491390dde90535cf05cc",
"46235f80b7b847e89ded0d7ae91bdae3",
"293c10e5a68246c8afe594e849829ae1",
"f9542992a6e24795949141acc9565f2e",
"e7b108089f9044698c2191449719fdf8",
"0e3e0386b07d4c2c9811a6df79ff84c3",
"19cf72db33e14ba49ac49a174c5da0d1",
"778d618da1e14c89a46c99a7d6838e7b",
"ce964c45075f480a9d423a496f439116",
"5405c33ecf0c41f7a8e780595cd07e78",
"e52a7cdcaa4f4752bcc7129f27bf9d2b",
"8e2cd72c26114157a5fe87540c99f49f",
"003ada19ed0e454f92aa0fd9793a5ef3",
"fe66a5c7c6b74334a518b0dcbf726e6b",
"e80c32ef6bba4d6597c083c5f972d1a7",
"42a1e0f26ddf4d979c6184c647379cba",
"0f24b2df370f4f29b247a40912e92d59",
"9e328aa3caac44e7929cfed1d19e26b9",
"f1d17b57e8ca4811b819564151d3a1ac",
"e392aedf2306409d8b466761986b73d8",
"8519203b6f974631b0a93f534ad54cc5",
"ea65da01b328428490ea5397cf42bef1",
"7da6054d41dd405ba0b0b0499733a6c3",
"846e82a1f9cc42d68a89c293330cd58d",
"339197a8ff7544aba04a89ea1b9883c8",
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"2f5c468ac3584a629606dcc85bb338ad",
"c1a5897a75e24b1d923234ab5869d9d4",
"79efd19521e242a385e49e3e9aab9a74"
]
},
"outputId": "2fa07f4c-ce08-459a-85b4-2225cfce5e01"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"version_major": 2,
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}
},
"metadata": {}
},
{
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"version_major": 2,
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}
},
"metadata": {}
},
{
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],
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"version_major": 2,
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}
},
"metadata": {}
},
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
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}
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"metadata": {}
},
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"version_major": 2,
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}
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/AmarOk1412/fine_tuned_science_gemma2b-it/commit/4e8c7c26263173153d11425ca2f8283e084feef9', commit_message='Fine-tuned model pushed.', commit_description='', oid='4e8c7c26263173153d11425ca2f8283e084feef9', pr_url=None, pr_revision=None, pr_num=None)"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"from huggingface_hub import upload_folder, create_repo\n",
"\n",
"repo_id = create_repo(repo_id, exist_ok=True).repo_id\n",
"\n",
"\n",
"upload_folder(\n",
" repo_id=repo_id,\n",
" folder_path=output_dir,\n",
" commit_message=\"Fine-tuned model pushed.\",\n",
" ignore_patterns=[\"step_*\", \"epoch_*\"],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mrDK-p1VE1GD"
},
"source": [
"#### (Optional) Convert model to MLC format for on-device deployment\n",
"***Note: You might need to restart the runtime.***"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ojR3Zomu80mq"
},
"source": [
"\n",
"Install MLC LLM and TVM Unity Compiler"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "3PDnxgjt813K",
"outputId": "5431fde8-d1a0-4545-aaa2-d3afcf08ba27"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in links: https://mlc.ai/wheels\n",
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"\u001b[?25hInstalling collected packages: wcwidth, urllib3, typing-extensions, tqdm, tornado, sniffio, shortuuid, safetensors, regex, psutil, prompt-toolkit, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, mpmath, MarkupSafe, idna, h11, fsspec, filelock, exceptiongroup, distro, decorator, cloudpickle, click, charset-normalizer, certifi, attrs, annotated-types, uvicorn, triton, sympy, scipy, requests, pydantic-core, nvidia-cusparse-cu12, nvidia-cudnn-cu12, ml-dtypes, jinja2, httpcore, anyio, tiktoken, starlette, pydantic, nvidia-cusolver-cu12, mlc-ai-nightly-cu122, httpx, torch, openai, fastapi, mlc-llm-nightly-cu122\n",
" Attempting uninstall: wcwidth\n",
" Found existing installation: wcwidth 0.2.13\n",
" Uninstalling wcwidth-0.2.13:\n",
" Successfully uninstalled wcwidth-0.2.13\n",
" Attempting uninstall: urllib3\n",
" Found existing installation: urllib3 2.0.7\n",
" Uninstalling urllib3-2.0.7:\n",
" Successfully uninstalled urllib3-2.0.7\n",
" Attempting uninstall: typing-extensions\n",
" Found existing installation: typing_extensions 4.10.0\n",
" Uninstalling typing_extensions-4.10.0:\n",
" Successfully uninstalled typing_extensions-4.10.0\n",
" Attempting uninstall: tqdm\n",
" Found existing installation: tqdm 4.66.2\n",
" Uninstalling tqdm-4.66.2:\n",
" Successfully uninstalled tqdm-4.66.2\n",
" Attempting uninstall: tornado\n",
" Found existing installation: tornado 6.3.3\n",
" Uninstalling tornado-6.3.3:\n",
" Successfully uninstalled tornado-6.3.3\n",
" Attempting uninstall: sniffio\n",
" Found existing installation: sniffio 1.3.1\n",
" Uninstalling sniffio-1.3.1:\n",
" Successfully uninstalled sniffio-1.3.1\n",
" Attempting uninstall: safetensors\n",
" Found existing installation: safetensors 0.4.2\n",
" Uninstalling safetensors-0.4.2:\n",
" Successfully uninstalled safetensors-0.4.2\n",
" Attempting uninstall: regex\n",
" Found existing installation: regex 2023.12.25\n",
" Uninstalling regex-2023.12.25:\n",
" Successfully uninstalled regex-2023.12.25\n",
" Attempting uninstall: psutil\n",
" Found existing installation: psutil 5.9.5\n",
" Uninstalling psutil-5.9.5:\n",
" Successfully uninstalled psutil-5.9.5\n",
" Attempting uninstall: prompt-toolkit\n",
" Found existing installation: prompt-toolkit 3.0.43\n",
" Uninstalling prompt-toolkit-3.0.43:\n",
" Successfully uninstalled prompt-toolkit-3.0.43\n",
" Attempting uninstall: nvidia-nvtx-cu12\n",
" Found existing installation: nvidia-nvtx-cu12 12.1.105\n",
" Uninstalling nvidia-nvtx-cu12-12.1.105:\n",
" Successfully uninstalled nvidia-nvtx-cu12-12.1.105\n",
" Attempting uninstall: nvidia-nvjitlink-cu12\n",
" Found existing installation: nvidia-nvjitlink-cu12 12.4.99\n",
" Uninstalling nvidia-nvjitlink-cu12-12.4.99:\n",
" Successfully uninstalled nvidia-nvjitlink-cu12-12.4.99\n",
" Attempting uninstall: nvidia-nccl-cu12\n",
" Found existing installation: nvidia-nccl-cu12 2.19.3\n",
" Uninstalling nvidia-nccl-cu12-2.19.3:\n",
" Successfully uninstalled nvidia-nccl-cu12-2.19.3\n",
" Attempting uninstall: nvidia-curand-cu12\n",
" Found existing installation: nvidia-curand-cu12 10.3.2.106\n",
" Uninstalling nvidia-curand-cu12-10.3.2.106:\n",
" Successfully uninstalled nvidia-curand-cu12-10.3.2.106\n",
" Attempting uninstall: nvidia-cufft-cu12\n",
" Found existing installation: nvidia-cufft-cu12 11.0.2.54\n",
" Uninstalling nvidia-cufft-cu12-11.0.2.54:\n",
" Successfully uninstalled nvidia-cufft-cu12-11.0.2.54\n",
" Attempting uninstall: nvidia-cuda-runtime-cu12\n",
" Found existing installation: nvidia-cuda-runtime-cu12 12.1.105\n",
" Uninstalling nvidia-cuda-runtime-cu12-12.1.105:\n",
" Successfully uninstalled nvidia-cuda-runtime-cu12-12.1.105\n",
" Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
" Found existing installation: nvidia-cuda-nvrtc-cu12 12.1.105\n",
" Uninstalling nvidia-cuda-nvrtc-cu12-12.1.105:\n",
" Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.1.105\n",
" Attempting uninstall: nvidia-cuda-cupti-cu12\n",
" Found existing installation: nvidia-cuda-cupti-cu12 12.1.105\n",
" Uninstalling nvidia-cuda-cupti-cu12-12.1.105:\n",
" Successfully uninstalled nvidia-cuda-cupti-cu12-12.1.105\n",
" Attempting uninstall: nvidia-cublas-cu12\n",
" Found existing installation: nvidia-cublas-cu12 12.1.3.1\n",
" Uninstalling nvidia-cublas-cu12-12.1.3.1:\n",
" Successfully uninstalled nvidia-cublas-cu12-12.1.3.1\n",
" Attempting uninstall: numpy\n",
" Found existing installation: numpy 1.25.2\n",
" Uninstalling numpy-1.25.2:\n",
" Successfully uninstalled numpy-1.25.2\n",
" Attempting uninstall: networkx\n",
" Found existing installation: networkx 3.2.1\n",
" Uninstalling networkx-3.2.1:\n",
" Successfully uninstalled networkx-3.2.1\n",
" Attempting uninstall: mpmath\n",
" Found existing installation: mpmath 1.3.0\n",
" Uninstalling mpmath-1.3.0:\n",
" Successfully uninstalled mpmath-1.3.0\n",
" Attempting uninstall: MarkupSafe\n",
" Found existing installation: MarkupSafe 2.1.5\n",
" Uninstalling MarkupSafe-2.1.5:\n",
" Successfully uninstalled MarkupSafe-2.1.5\n",
" Attempting uninstall: idna\n",
" Found existing installation: idna 3.6\n",
" Uninstalling idna-3.6:\n",
" Successfully uninstalled idna-3.6\n",
" Attempting uninstall: fsspec\n",
" Found existing installation: fsspec 2023.6.0\n",
" Uninstalling fsspec-2023.6.0:\n",
" Successfully uninstalled fsspec-2023.6.0\n",
" Attempting uninstall: filelock\n",
" Found existing installation: filelock 3.13.3\n",
" Uninstalling filelock-3.13.3:\n",
" Successfully uninstalled filelock-3.13.3\n",
" Attempting uninstall: exceptiongroup\n",
" Found existing installation: exceptiongroup 1.2.0\n",
" Uninstalling exceptiongroup-1.2.0:\n",
" Successfully uninstalled exceptiongroup-1.2.0\n",
" Attempting uninstall: distro\n",
" Found existing installation: distro 1.7.0\n",
" Uninstalling distro-1.7.0:\n",
" Successfully uninstalled distro-1.7.0\n",
" Attempting uninstall: decorator\n",
" Found existing installation: decorator 4.4.2\n",
" Uninstalling decorator-4.4.2:\n",
" Successfully uninstalled decorator-4.4.2\n",
" Attempting uninstall: cloudpickle\n",
" Found existing installation: cloudpickle 2.2.1\n",
" Uninstalling cloudpickle-2.2.1:\n",
" Successfully uninstalled cloudpickle-2.2.1\n",
" Attempting uninstall: click\n",
" Found existing installation: click 8.1.7\n",
" Uninstalling click-8.1.7:\n",
" Successfully uninstalled click-8.1.7\n",
" Attempting uninstall: charset-normalizer\n",
" Found existing installation: charset-normalizer 3.3.2\n",
" Uninstalling charset-normalizer-3.3.2:\n",
" Successfully uninstalled charset-normalizer-3.3.2\n",
" Attempting uninstall: certifi\n",
" Found existing installation: certifi 2024.2.2\n",
" Uninstalling certifi-2024.2.2:\n",
" Successfully uninstalled certifi-2024.2.2\n",
" Attempting uninstall: attrs\n",
" Found existing installation: attrs 23.2.0\n",
" Uninstalling attrs-23.2.0:\n",
" Successfully uninstalled attrs-23.2.0\n",
" Attempting uninstall: annotated-types\n",
" Found existing installation: annotated-types 0.6.0\n",
" Uninstalling annotated-types-0.6.0:\n",
" Successfully uninstalled annotated-types-0.6.0\n",
" Attempting uninstall: triton\n",
" Found existing installation: triton 2.2.0\n",
" Uninstalling triton-2.2.0:\n",
" Successfully uninstalled triton-2.2.0\n",
" Attempting uninstall: sympy\n",
" Found existing installation: sympy 1.12\n",
" Uninstalling sympy-1.12:\n",
" Successfully uninstalled sympy-1.12\n",
" Attempting uninstall: scipy\n",
" Found existing installation: scipy 1.11.4\n",
" Uninstalling scipy-1.11.4:\n",
" Successfully uninstalled scipy-1.11.4\n",
" Attempting uninstall: requests\n",
" Found existing installation: requests 2.31.0\n",
" Uninstalling requests-2.31.0:\n",
" Successfully uninstalled requests-2.31.0\n",
" Attempting uninstall: pydantic-core\n",
" Found existing installation: pydantic_core 2.16.3\n",
" Uninstalling pydantic_core-2.16.3:\n",
" Successfully uninstalled pydantic_core-2.16.3\n",
" Attempting uninstall: nvidia-cusparse-cu12\n",
" Found existing installation: nvidia-cusparse-cu12 12.1.0.106\n",
" Uninstalling nvidia-cusparse-cu12-12.1.0.106:\n",
" Successfully uninstalled nvidia-cusparse-cu12-12.1.0.106\n",
" Attempting uninstall: nvidia-cudnn-cu12\n",
" Found existing installation: nvidia-cudnn-cu12 8.9.2.26\n",
" Uninstalling nvidia-cudnn-cu12-8.9.2.26:\n",
" Successfully uninstalled nvidia-cudnn-cu12-8.9.2.26\n",
" Attempting uninstall: ml-dtypes\n",
" Found existing installation: ml-dtypes 0.2.0\n",
" Uninstalling ml-dtypes-0.2.0:\n",
" Successfully uninstalled ml-dtypes-0.2.0\n",
" Attempting uninstall: jinja2\n",
" Found existing installation: Jinja2 3.1.3\n",
" Uninstalling Jinja2-3.1.3:\n",
" Successfully uninstalled Jinja2-3.1.3\n",
" Attempting uninstall: anyio\n",
" Found existing installation: anyio 3.7.1\n",
" Uninstalling anyio-3.7.1:\n",
" Successfully uninstalled anyio-3.7.1\n",
" Attempting uninstall: pydantic\n",
" Found existing installation: pydantic 2.6.4\n",
" Uninstalling pydantic-2.6.4:\n",
" Successfully uninstalled pydantic-2.6.4\n",
" Attempting uninstall: nvidia-cusolver-cu12\n",
" Found existing installation: nvidia-cusolver-cu12 11.4.5.107\n",
" Uninstalling nvidia-cusolver-cu12-11.4.5.107:\n",
" Successfully uninstalled nvidia-cusolver-cu12-11.4.5.107\n",
" Attempting uninstall: torch\n",
" Found existing installation: torch 2.2.1+cu121\n",
" Uninstalling torch-2.2.1+cu121:\n",
" Successfully uninstalled torch-2.2.1+cu121\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"ipython 7.34.0 requires jedi>=0.16, which is not installed.\n",
"astropy 5.3.4 requires numpy<2,>=1.21, but you have numpy 2.0.0rc1 which is incompatible.\n",
"contourpy 1.2.0 requires numpy<2.0,>=1.20, but you have numpy 2.0.0rc1 which is incompatible.\n",
"cupy-cuda12x 12.2.0 requires numpy<1.27,>=1.20, but you have numpy 2.0.0rc1 which is incompatible.\n",
"datasets 2.17.0 requires fsspec[http]<=2023.10.0,>=2023.1.0, but you have fsspec 2024.3.1 which is incompatible.\n",
"gcsfs 2023.6.0 requires fsspec==2023.6.0, but you have fsspec 2024.3.1 which is incompatible.\n",
"google-colab 1.0.0 requires tornado==6.3.3, but you have tornado 6.4 which is incompatible.\n",
"ibis-framework 8.0.0 requires numpy<2,>=1, but you have numpy 2.0.0rc1 which is incompatible.\n",
"jupyter-server 1.24.0 requires anyio<4,>=3.1.0, but you have anyio 4.3.0 which is incompatible.\n",
"moviepy 1.0.3 requires decorator<5.0,>=4.0.2, but you have decorator 5.1.1 which is incompatible.\n",
"numba 0.58.1 requires numpy<1.27,>=1.22, but you have numpy 2.0.0rc1 which is incompatible.\n",
"pywavelets 1.5.0 requires numpy<2.0,>=1.22.4, but you have numpy 2.0.0rc1 which is incompatible.\n",
"shapely 2.0.3 requires numpy<2,>=1.14, but you have numpy 2.0.0rc1 which is incompatible.\n",
"statsmodels 0.14.1 requires numpy<2,>=1.18, but you have numpy 2.0.0rc1 which is incompatible.\n",
"tensorflow 2.15.0 requires ml-dtypes~=0.2.0, but you have ml-dtypes 0.4.0b1 which is incompatible.\n",
"tensorflow 2.15.0 requires numpy<2.0.0,>=1.23.5, but you have numpy 2.0.0rc1 which is incompatible.\n",
"torchaudio 2.2.1+cu121 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\n",
"torchtext 0.17.1 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\n",
"torchvision 0.17.1+cu121 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed MarkupSafe-2.1.5 annotated-types-0.6.0 anyio-4.3.0 attrs-23.2.0 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 cloudpickle-3.0.0 decorator-5.1.1 distro-1.9.0 exceptiongroup-1.2.0 fastapi-0.110.0 filelock-3.13.3 fsspec-2024.3.1 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 idna-3.6 jinja2-3.1.3 ml-dtypes-0.4.0b1 mlc-ai-nightly-cu122-0.15.dev223 mlc-llm-nightly-cu122-0.1.dev1058 mpmath-1.4.0a0 networkx-3.3rc0 numpy-2.0.0rc1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 openai-1.14.3 prompt-toolkit-3.0.43 psutil-5.9.8 pydantic-2.6.4 pydantic-core-2.16.3 regex-2023.12.25 requests-2.31.0 safetensors-0.4.2 scipy-1.13.0rc1 shortuuid-1.0.13 sniffio-1.3.1 starlette-0.36.3 sympy-1.12.1a2 tiktoken-0.6.0 torch-2.2.2 tornado-6.4 tqdm-4.66.2 triton-2.2.0 typing-extensions-4.11.0rc1 urllib3-2.2.1 uvicorn-0.29.0 wcwidth-0.2.13\n"
]
},
{
"data": {
"application/vnd.colab-display-data+json": {
"id": "6b26b35f331f43efa4a4e9963c8e1a7c",
"pip_warning": {
"packages": [
"certifi",
"decorator",
"prompt_toolkit",
"psutil",
"tornado",
"wcwidth"
]
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"!pip install --pre --force-reinstall mlc-ai-nightly-cu122 mlc-llm-nightly-cu122 -f https://mlc.ai/wheels"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 411
},
"id": "9ZZFiwFjVCC4",
"outputId": "32d77f50-c5e7-411d-d7e3-dff97eba9015"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting numpy==1.23.5\n",
" Downloading numpy-1.23.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.1/17.1 MB\u001b[0m \u001b[31m70.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: numpy\n",
" Attempting uninstall: numpy\n",
" Found existing installation: numpy 2.0.0rc1\n",
" Uninstalling numpy-2.0.0rc1:\n",
" Successfully uninstalled numpy-2.0.0rc1\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"chex 0.1.86 requires numpy>=1.24.1, but you have numpy 1.23.5 which is incompatible.\n",
"datasets 2.17.0 requires fsspec[http]<=2023.10.0,>=2023.1.0, but you have fsspec 2024.3.1 which is incompatible.\n",
"moviepy 1.0.3 requires decorator<5.0,>=4.0.2, but you have decorator 5.1.1 which is incompatible.\n",
"tensorflow 2.15.0 requires ml-dtypes~=0.2.0, but you have ml-dtypes 0.4.0b1 which is incompatible.\n",
"torchtext 0.17.1 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\n",
"torchvision 0.17.1+cu121 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed numpy-1.23.5\n"
]
},
{
"data": {
"application/vnd.colab-display-data+json": {
"id": "fcccba20bc304392bec60527f9df7fd2",
"pip_warning": {
"packages": [
"numpy"
]
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"!pip install numpy==1.23.5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rB9XA3mT9LpD"
},
"outputs": [],
"source": [
"import mlc_llm\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
"referenced_widgets": [
"28ad75e3d0524b0e984da22749bc2b7f",
"e6448231e20a47d3b8c7a7e02a4e1a51",
"9fa0aea0e90f488595ef0feb34b7933c",
"c06d1c45ca0840d7903f02657e6d2a3d",
"88854978a8a94dac867790ba63ccf106",
"080c917f6bcc403daa9db1e4b387fb14",
"4900e2c171e04a32a933439af6ddcc71",
"c0601d67953e4e8ead9bdee8564fbd4a",
"d9975ef0890a478eb8983702a838d62f",
"c9023f17c67e45168474475de5651731",
"7f74856428f6435ba090d420609fa33b"
]
},
"id": "G0IJ-vDI9aIW",
"outputId": "d71387f2-5264-45fe-f2a0-09b834898886"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "28ad75e3d0524b0e984da22749bc2b7f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Login to Hugging Face account.\n",
"from huggingface_hub import notebook_login\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DhhpFzI9-aPA"
},
"outputs": [],
"source": [
"# Configure model name and quantization type.\n",
"FINE_TUNED_MODEL_NAME = \"fine_tuned_science_gemma2b-it\"\n",
"QUANTIZATION= \"q4f16_1\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NnuI1hwz-xBC",
"outputId": "2b482b02-b9d1-4837-e741-3a18428b2096"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sun Mar 31 12:19:02 2024 \n",
"+---------------------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n",
"|-----------------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|=========================================+======================+======================|\n",
"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 39C P8 11W / 70W | 0MiB / 15360MiB | 0% Default |\n",
"| | | N/A |\n",
"+-----------------------------------------+----------------------+----------------------+\n",
" \n",
"+---------------------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=======================================================================================|\n",
"| No running processes found |\n",
"+---------------------------------------------------------------------------------------+\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"!nvidia-smi\n",
"\n",
"# Check if CUDA is available.\n",
"torch.cuda.is_available()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UjSpGwmO-9J6",
"outputId": "20f8510a-4f7b-4466-c8a2-85105e7562c1"
},
"outputs": [
{
"data": {
"text/plain": [
"device(type='cuda', index=0)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"device"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KJPjatyWFD8z"
},
"source": [
"#### Convert fine-tuned model weights."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "039UM4LbtobP"
},
"source": [
"*Note:* Go to **/usr/local/lib/python3.10/dist-packages/tvm/relax/frontend/nn/exporter.py**\", and comment Line 139:\n",
"\n",
" `assert rx.analysis.well_formed(mod)`"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FARyNolzXNJU"
},
"source": [
"*Note:* Go to **/usr/local/lib/python3.10/dist-packages/mlc_llm/loader/huggingface_loader.py**\", and add the following line before line 115:\n",
"\n",
" `preshared_funcs = {}`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hp7tQAG9-_t6",
"outputId": "e6b0239d-7903-4a35-9bdc-4d84fafff319"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-03-31 12:19:45] INFO auto_config.py:115: \u001b[92mFound\u001b[0m model configuration: /content/fine_tuned_science_gemma2b-it/config.json\n",
"[2024-03-31 12:19:48] INFO auto_device.py:76: \u001b[92mFound\u001b[0m device: cuda:0\n",
"[2024-03-31 12:19:50] INFO auto_device.py:85: \u001b[91mNot found\u001b[0m device: rocm:0\n",
"[2024-03-31 12:19:53] INFO auto_device.py:85: \u001b[91mNot found\u001b[0m device: metal:0\n",
"[2024-03-31 12:19:55] INFO auto_device.py:85: \u001b[91mNot found\u001b[0m device: vulkan:0\n",
"[2024-03-31 12:19:58] INFO auto_device.py:85: \u001b[91mNot found\u001b[0m device: opencl:0\n",
"[2024-03-31 12:19:58] INFO auto_device.py:33: Using device: \u001b[1mcuda:0\u001b[0m\n",
"[2024-03-31 12:19:58] INFO auto_weight.py:70: Finding weights in: /content/fine_tuned_science_gemma2b-it\n",
"[2024-03-31 12:19:58] INFO auto_weight.py:136: \u001b[91mNot found\u001b[0m Huggingface PyTorch\n",
"[2024-03-31 12:19:58] INFO auto_weight.py:143: \u001b[92mFound\u001b[0m source weight format: huggingface-safetensor. Source configuration: /content/fine_tuned_science_gemma2b-it/model.safetensors.index.json\n",
"[2024-03-31 12:19:58] INFO auto_weight.py:106: Using source weight configuration: \u001b[1m/content/fine_tuned_science_gemma2b-it/model.safetensors.index.json\u001b[0m. Use `--source` to override.\n",
"[2024-03-31 12:19:58] INFO auto_weight.py:110: Using source weight format: \u001b[1mhuggingface-safetensor\u001b[0m. Use `--source-format` to override.\n",
"[2024-03-31 12:19:58] INFO auto_config.py:153: \u001b[92mFound\u001b[0m model type: \u001b[1mgemma\u001b[0m. Use `--model-type` to override.\n",
"\u001b[1mWeight conversion with arguments:\u001b[0m\n",
" \u001b[1m--config\u001b[0m /content/fine_tuned_science_gemma2b-it/config.json\n",
" \u001b[1m--quantization\u001b[0m GroupQuantize(name='q4f16_1', kind='group-quant', group_size=32, quantize_dtype='int4', storage_dtype='uint32', model_dtype='float16', linear_weight_layout='NK', quantize_embedding=True, quantize_final_fc=True, num_elem_per_storage=8, num_storage_per_group=4, max_int_value=7)\n",
" \u001b[1m--model-type\u001b[0m gemma\n",
" \u001b[1m--device\u001b[0m cuda:0\n",
" \u001b[1m--source\u001b[0m /content/fine_tuned_science_gemma2b-it/model.safetensors.index.json\n",
" \u001b[1m--source-format\u001b[0m huggingface-safetensor\n",
" \u001b[1m--output\u001b[0m /content/fine_tuned_science_gemma2b-it-q4f16_1-MLC\n",
"[2024-03-31 12:19:58] INFO gemma_model.py:55: \u001b[1mcontext_window_size\u001b[0m not found in config.json. Falling back to \u001b[1mmax_position_embeddings\u001b[0m (8192)\n",
"[2024-03-31 12:19:58] INFO gemma_model.py:70: \u001b[1mprefill_chunk_size\u001b[0m defaults to \u001b[1mcontext_window_size\u001b[0m (8192)\n",
"Start storing to cache /content/fine_tuned_science_gemma2b-it-q4f16_1-MLC\n",
"[2024-03-31 12:19:59] INFO huggingface_loader.py:185: Loading HF parameters from: /content/fine_tuned_science_gemma2b-it/model-00001-of-00002.safetensors\n",
"[2024-03-31 12:20:07] INFO group_quantization.py:234: Compiling quantize function for key: ((256000, 2048), float16, cuda, axis=1, output_transpose=False)\n",
"[2024-03-31 12:20:10] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.embed_tokens.q_weight\u001b[0m\", shape: (256000, 256), dtype: uint32\n",
"[2024-03-31 12:20:11] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.embed_tokens.q_scale\u001b[0m\", shape: (256000, 64), dtype: float16\n",
"[2024-03-31 12:20:11] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.0.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:12] INFO group_quantization.py:234: Compiling quantize function for key: ((2048, 16384), float16, cuda, axis=1, output_transpose=False)\n",
"[2024-03-31 12:20:12] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.0.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:12] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.0.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:13] INFO group_quantization.py:234: Compiling quantize function for key: ((32768, 2048), float16, cuda, axis=1, output_transpose=False)\n",
"[2024-03-31 12:20:14] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.0.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:14] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.0.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:14] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.0.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:14] INFO group_quantization.py:234: Compiling quantize function for key: ((2560, 2048), float16, cuda, axis=1, output_transpose=False)\n",
"[2024-03-31 12:20:15] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.0.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:15] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.0.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:15] INFO group_quantization.py:234: Compiling quantize function for key: ((2048, 2048), float16, cuda, axis=1, output_transpose=False)\n",
"[2024-03-31 12:20:16] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.0.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:16] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.0.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:16] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.1.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:17] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.1.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:17] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.1.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:18] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.1.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:18] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.1.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:18] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.1.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:18] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.1.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:18] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.1.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:18] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.1.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:18] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.1.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:18] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.10.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:19] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.10.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:19] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.10.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:20] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.10.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:20] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.10.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:20] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.10.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:20] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.10.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:20] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.10.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:20] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.10.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:20] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.10.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:20] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.11.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:20] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.11.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:20] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.11.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:21] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.11.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:21] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.11.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:21] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.11.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:21] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.11.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:21] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.11.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:22] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.11.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:22] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.11.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:22] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.12.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:22] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.12.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:22] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.12.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:23] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.12.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:23] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.12.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:23] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.12.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:23] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.12.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:23] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.12.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:23] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.12.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:23] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.12.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:23] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.13.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:24] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.13.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:24] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.13.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:25] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.13.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:25] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.13.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:25] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.13.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:25] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.13.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:25] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.13.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:25] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.13.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:25] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.13.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:25] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.14.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:25] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.14.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:25] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.14.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:26] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.14.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:26] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.14.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:26] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.14.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:26] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.14.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:27] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.14.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:27] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.14.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:27] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.14.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:27] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.15.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:27] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.15.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:27] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.15.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:28] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.15.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:28] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.15.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:28] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.15.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:29] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.15.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:29] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.15.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:29] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.15.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:29] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.15.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:29] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.16.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:29] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.16.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:29] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.16.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:30] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.16.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:30] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.16.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:30] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.16.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:30] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.16.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:30] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.16.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:30] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.16.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:30] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.16.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:31] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.17.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:31] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.17.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:31] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.17.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:32] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.17.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:32] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.17.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:32] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.17.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:32] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.2.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
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"[2024-03-31 12:20:32] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.2.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:33] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.2.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:33] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.2.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:33] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.2.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:33] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.2.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:33] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.2.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:33] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.2.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:33] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.2.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:33] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.3.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:34] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.3.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:34] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.3.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:35] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.3.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:35] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.3.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:35] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.3.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:35] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.3.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:35] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.3.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:35] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.3.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:35] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.3.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:35] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.4.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:35] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.4.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:35] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.4.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:36] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.4.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:37] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.4.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:37] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.4.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:37] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.4.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:37] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.4.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:37] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.4.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:37] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.4.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:37] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.5.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:37] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.5.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:37] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.5.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:38] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.5.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:38] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.5.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:38] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.5.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:38] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.5.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:39] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.5.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:39] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.5.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:39] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.5.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:39] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.6.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:39] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.6.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:39] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.6.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:40] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.6.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:40] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.6.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:41] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.6.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:41] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.6.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:41] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.6.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:41] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.6.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:41] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.6.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:41] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.7.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:41] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.7.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:41] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.7.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:42] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.7.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:42] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.7.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:42] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.7.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:42] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.7.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:42] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.7.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:42] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.7.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:42] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.7.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:42] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.8.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:43] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.8.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:43] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.8.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:44] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.8.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:44] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.8.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:44] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.8.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:44] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.8.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:44] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.8.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:44] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.8.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:44] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.8.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:44] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.9.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:45] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.9.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:45] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.9.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:46] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.9.mlp.gate_up_proj.q_weight\u001b[0m\", shape: (32768, 256), dtype: uint32\n",
"[2024-03-31 12:20:46] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.9.mlp.gate_up_proj.q_scale\u001b[0m\", shape: (32768, 64), dtype: float16\n",
"[2024-03-31 12:20:46] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.9.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:46] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.9.self_attn.qkv_proj.q_weight\u001b[0m\", shape: (2560, 256), dtype: uint32\n",
"[2024-03-31 12:20:46] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.9.self_attn.qkv_proj.q_scale\u001b[0m\", shape: (2560, 64), dtype: float16\n",
"[2024-03-31 12:20:46] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.9.self_attn.o_proj.q_weight\u001b[0m\", shape: (2048, 256), dtype: uint32\n",
"[2024-03-31 12:20:46] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.9.self_attn.o_proj.q_scale\u001b[0m\", shape: (2048, 64), dtype: float16\n",
"[2024-03-31 12:20:46] INFO huggingface_loader.py:197: Unloading HF weight file: /content/fine_tuned_science_gemma2b-it/model-00001-of-00002.safetensors\n",
"[2024-03-31 12:20:46] INFO huggingface_loader.py:185: Loading HF parameters from: /content/fine_tuned_science_gemma2b-it/model-00002-of-00002.safetensors\n",
"[2024-03-31 12:20:46] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.17.input_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:47] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.17.mlp.down_proj.q_weight\u001b[0m\", shape: (2048, 2048), dtype: uint32\n",
"[2024-03-31 12:20:47] INFO huggingface_loader.py:167: [Quantized] Parameter: \"\u001b[1mmodel.layers.17.mlp.down_proj.q_scale\u001b[0m\", shape: (2048, 512), dtype: float16\n",
"[2024-03-31 12:20:47] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.layers.17.post_attention_layernorm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"[2024-03-31 12:20:47] INFO huggingface_loader.py:175: [Not quantized] Parameter: \"\u001b[1mmodel.norm.weight\u001b[0m\", shape: (2048,), dtype: float16\n",
"100% 110/110 [00:47<00:00, 2.31it/s]\n",
"[2024-03-31 12:20:47] INFO huggingface_loader.py:197: Unloading HF weight file: /content/fine_tuned_science_gemma2b-it/model-00002-of-00002.safetensors\n",
"[2024-03-31 12:20:47] INFO stats.py:76: \u001b[92mTime usage\u001b[0m: HF loading: 1.857 sec; Pre-quantization mapping: 30.845 sec; Quantization: 6.093 sec\n",
"[2024-03-31 12:20:47] INFO stats.py:90: \u001b[92mRAM usage\u001b[0m: Peak RAM: 4.606 GB. Total bytes loaded from disk: 4.668 GB\n",
"\n",
"All finished, 38 total shards committed, record saved to /content/fine_tuned_science_gemma2b-it-q4f16_1-MLC/ndarray-cache.json\n",
"[2024-03-31 12:20:47] INFO convert_weight.py:156: \u001b[92mParameter size\u001b[0m after quantization: 1.313 GB\n",
"[2024-03-31 12:20:47] INFO convert_weight.py:161: \u001b[92mTotal parameters\u001b[0m: 2,506,172,416\n",
"[2024-03-31 12:20:47] INFO convert_weight.py:162: \u001b[92mBits per parameter\u001b[0m: 4.500\n",
"[2024-03-31 12:20:47] INFO convert_weight.py:167: Saved to directory: \u001b[1m/content/fine_tuned_science_gemma2b-it-q4f16_1-MLC\u001b[0m\n"
]
}
],
"source": [
"!python -m mlc_llm convert_weight /content/$FINE_TUNED_MODEL_NAME/ --quantization $QUANTIZATION -o /content/$FINE_TUNED_MODEL_NAME-$QUANTIZATION-MLC/"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hcR3AEF9FHlb"
},
"source": [
"#### Generate token config files."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JqIPJS2KCwQo",
"outputId": "8ac31b51-b717-4220-c556-cbbc32e80a2e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-03-31 12:20:57] INFO auto_config.py:115: \u001b[92mFound\u001b[0m model configuration: /content/fine_tuned_science_gemma2b-it/config.json\n",
"[2024-03-31 12:20:57] INFO auto_config.py:153: \u001b[92mFound\u001b[0m model type: \u001b[1mgemma\u001b[0m. Use `--model-type` to override.\n",
"[2024-03-31 12:20:57] INFO gemma_model.py:55: \u001b[1mcontext_window_size\u001b[0m not found in config.json. Falling back to \u001b[1mmax_position_embeddings\u001b[0m (8192)\n",
"[2024-03-31 12:20:57] INFO gemma_model.py:70: \u001b[1mprefill_chunk_size\u001b[0m defaults to \u001b[1mcontext_window_size\u001b[0m (8192)\n",
"[2024-03-31 12:20:57] INFO config.py:106: Overriding \u001b[1mcontext_window_size\u001b[0m from 8192 to 768\n",
"[2024-03-31 12:20:57] INFO config.py:106: Overriding \u001b[1mmax_batch_size\u001b[0m from 1 to 80\n",
"[2024-03-31 12:20:57] INFO gemma_model.py:78: Overriding \u001b[1mprefill_chunk_size\u001b[0m from 8192 to 768 (\u001b[1mcontext_window_size\u001b[0m)\n",
"[2024-03-31 12:20:57] INFO gen_config.py:133: [generation_config.json] Setting \u001b[1mbos_token_id\u001b[0m: 2\n",
"[2024-03-31 12:20:57] INFO gen_config.py:133: [generation_config.json] Setting \u001b[1meos_token_id\u001b[0m: 1\n",
"[2024-03-31 12:20:57] INFO gen_config.py:133: [generation_config.json] Setting \u001b[1mpad_token_id\u001b[0m: 0\n",
"[2024-03-31 12:20:57] INFO gen_config.py:145: \u001b[92mFound\u001b[0m tokenizer config: /content/fine_tuned_science_gemma2b-it/tokenizer.model. Copying to \u001b[1m/content/fine_tuned_science_gemma2b-it-q4f16_1-MLC/tokenizer.model\u001b[0m\n",
"[2024-03-31 12:20:57] INFO gen_config.py:145: \u001b[92mFound\u001b[0m tokenizer config: /content/fine_tuned_science_gemma2b-it/tokenizer.json. Copying to \u001b[1m/content/fine_tuned_science_gemma2b-it-q4f16_1-MLC/tokenizer.json\u001b[0m\n",
"[2024-03-31 12:20:57] INFO gen_config.py:147: \u001b[91mNot found\u001b[0m tokenizer config: /content/fine_tuned_science_gemma2b-it/vocab.json\n",
"[2024-03-31 12:20:57] INFO gen_config.py:147: \u001b[91mNot found\u001b[0m tokenizer config: /content/fine_tuned_science_gemma2b-it/merges.txt\n",
"[2024-03-31 12:20:57] INFO gen_config.py:147: \u001b[91mNot found\u001b[0m tokenizer config: /content/fine_tuned_science_gemma2b-it/added_tokens.json\n",
"[2024-03-31 12:20:57] INFO gen_config.py:145: \u001b[92mFound\u001b[0m tokenizer config: /content/fine_tuned_science_gemma2b-it/tokenizer_config.json. Copying to \u001b[1m/content/fine_tuned_science_gemma2b-it-q4f16_1-MLC/tokenizer_config.json\u001b[0m\n",
"[2024-03-31 12:20:57] INFO gen_config.py:75: [System default] Setting \u001b[1mtemperature\u001b[0m: 0.7\n",
"[2024-03-31 12:20:57] INFO gen_config.py:75: [System default] Setting \u001b[1mpresence_penalty\u001b[0m: 0.0\n",
"[2024-03-31 12:20:57] INFO gen_config.py:75: [System default] Setting \u001b[1mfrequency_penalty\u001b[0m: 0.0\n",
"[2024-03-31 12:20:57] INFO gen_config.py:75: [System default] Setting \u001b[1mrepetition_penalty\u001b[0m: 1.0\n",
"[2024-03-31 12:20:57] INFO gen_config.py:75: [System default] Setting \u001b[1mtop_p\u001b[0m: 0.95\n",
"[2024-03-31 12:20:57] INFO gen_config.py:75: [System default] Setting \u001b[1mmean_gen_len\u001b[0m: 128\n",
"[2024-03-31 12:20:57] INFO gen_config.py:75: [System default] Setting \u001b[1mmax_gen_len\u001b[0m: 512\n",
"[2024-03-31 12:20:57] INFO gen_config.py:75: [System default] Setting \u001b[1mshift_fill_factor\u001b[0m: 0.3\n",
"[2024-03-31 12:20:57] INFO gen_config.py:198: Dumping configuration file to: \u001b[1m/content/fine_tuned_science_gemma2b-it-q4f16_1-MLC/mlc-chat-config.json\u001b[0m\n"
]
}
],
"source": [
"!mlc_llm gen_config /content/$FINE_TUNED_MODEL_NAME/ --quantization $QUANTIZATION \\\n",
" --conv-template gemma_instruction --context-window-size 768 -o /content/$FINE_TUNED_MODEL_NAME-$QUANTIZATION-MLC/"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DOE3HXrRFLBr"
},
"source": [
"#### Compile the model to Android format."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kj13Cg1zDj07",
"outputId": "0603bb83-4ba2-47cf-83c2-f30937d4fadc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-03-31 12:21:07] INFO auto_config.py:69: \u001b[92mFound\u001b[0m model configuration: /content/fine_tuned_science_gemma2b-it-q4f16_1-MLC/mlc-chat-config.json\n",
"[2024-03-31 12:21:07] INFO auto_config.py:153: \u001b[92mFound\u001b[0m model type: \u001b[1mgemma\u001b[0m. Use `--model-type` to override.\n",
"[2024-03-31 12:21:07] WARNING auto_target.py:311: \u001b[1m--system-lib-prefix\u001b[0m is automatically picked from the filename, \u001b[1mgemma_q4f16_1_\u001b[0m, this allows us to use the filename as the model_lib in android/iOS builds. Please avoid renaming the .tar file when uploading the prebuilt.\n",
"\u001b[1mCompiling with arguments:\u001b[0m\n",
" \u001b[1m--config\u001b[0m GemmaConfig(hidden_size=2048, hidden_act='gelu', intermediate_size=16384, attention_bias=False, num_attention_heads=8, num_key_value_heads=1, head_dim=256, num_hidden_layers=18, rms_norm_eps=1e-06, vocab_size=256000, position_embedding_base=10000.0, context_window_size=768, prefill_chunk_size=768, tensor_parallel_shards=1, max_batch_size=80, kwargs={})\n",
" \u001b[1m--quantization\u001b[0m GroupQuantize(name='q4f16_1', kind='group-quant', group_size=32, quantize_dtype='int4', storage_dtype='uint32', model_dtype='float16', linear_weight_layout='NK', quantize_embedding=True, quantize_final_fc=True, num_elem_per_storage=8, num_storage_per_group=4, max_int_value=7)\n",
" \u001b[1m--model-type\u001b[0m gemma\n",
" \u001b[1m--target\u001b[0m {\"thread_warp_size\": 1, \"host\": {\"kind\": \"llvm\", \"tag\": \"\", \"keys\": [\"arm_cpu\", \"cpu\"], \"mtriple\": \"aarch64-linux-android\"}, \"texture_spatial_limit\": 16384, \"max_threads_per_block\": 256, \"max_function_args\": 128, \"max_num_threads\": 256, \"kind\": \"opencl\", \"max_shared_memory_per_block\": 16384, \"tag\": \"\", \"keys\": [\"opencl\", \"gpu\"]}\n",
" \u001b[1m--opt\u001b[0m flashinfer=0;cublas_gemm=0;faster_transformer=0;cudagraph=0;cutlass=0;ipc_allreduce_strategy=NONE\n",
" \u001b[1m--system-lib-prefix\u001b[0m \"gemma_q4f16_1_\"\n",
" \u001b[1m--output\u001b[0m /content/fine_tuned_science_gemma2b-it-q4f16_1-MLC/fine_tuned_science_gemma2b-it-q4f16_1-android.tar\n",
" \u001b[1m--overrides\u001b[0m context_window_size=None;sliding_window_size=None;prefill_chunk_size=None;attention_sink_size=None;max_batch_size=None;tensor_parallel_shards=None\n",
"[2024-03-31 12:21:07] INFO compile.py:137: Creating model from: GemmaConfig(hidden_size=2048, hidden_act='gelu', intermediate_size=16384, attention_bias=False, num_attention_heads=8, num_key_value_heads=1, head_dim=256, num_hidden_layers=18, rms_norm_eps=1e-06, vocab_size=256000, position_embedding_base=10000.0, context_window_size=768, prefill_chunk_size=768, tensor_parallel_shards=1, max_batch_size=80, kwargs={})\n",
"[2024-03-31 12:21:07] INFO compile.py:156: Exporting the model to TVM Unity compiler\n",
"[2024-03-31 12:21:08] INFO compile.py:162: Running optimizations using TVM Unity\n",
"[2024-03-31 12:21:08] INFO compile.py:176: Registering metadata: {'model_type': 'gemma', 'quantization': 'q4f16_1', 'context_window_size': 768, 'sliding_window_size': -1, 'attention_sink_size': -1, 'prefill_chunk_size': 768, 'tensor_parallel_shards': 1, 'kv_cache_bytes': 0}\n",
"[2024-03-31 12:21:09] INFO pipeline.py:50: Running TVM Relax graph-level optimizations\n",
"[2024-03-31 12:21:22] INFO pipeline.py:50: Lowering to TVM TIR kernels\n",
"[2024-03-31 12:21:24] INFO pipeline.py:50: Running TVM TIR-level optimizations\n",
"[2024-03-31 12:21:29] INFO pipeline.py:50: Running TVM Dlight low-level optimizations\n",
"[2024-03-31 12:21:31] INFO pipeline.py:50: Lowering to VM bytecode\n",
"[2024-03-31 12:21:32] INFO estimate_memory_usage.py:55: [Memory usage] Function `alloc_embedding_tensor`: 3.00 MB\n",
"[2024-03-31 12:21:32] INFO estimate_memory_usage.py:55: [Memory usage] Function `batch_decode`: 10.31 MB\n",
"[2024-03-31 12:21:32] INFO estimate_memory_usage.py:55: [Memory usage] Function `batch_prefill`: 99.00 MB\n",
"[2024-03-31 12:21:32] INFO estimate_memory_usage.py:55: [Memory usage] Function `batch_verify`: 99.00 MB\n",
"[2024-03-31 12:21:32] INFO estimate_memory_usage.py:55: [Memory usage] Function `create_tir_paged_kv_cache`: 0.00 MB\n",
"[2024-03-31 12:21:32] INFO estimate_memory_usage.py:55: [Memory usage] Function `decode`: 0.13 MB\n",
"[2024-03-31 12:21:32] INFO estimate_memory_usage.py:55: [Memory usage] Function `embed`: 3.00 MB\n",
"[2024-03-31 12:21:32] INFO estimate_memory_usage.py:55: [Memory usage] Function `prefill`: 99.00 MB\n",
"[2024-03-31 12:21:32] INFO estimate_memory_usage.py:55: [Memory usage] Function `softmax_with_temperature`: 0.00 MB\n",
"[2024-03-31 12:21:32] INFO pipeline.py:50: Compiling external modules\n",
"[2024-03-31 12:21:32] INFO pipeline.py:50: Compilation complete! Exporting to disk\n",
"[2024-03-31 12:21:39] INFO model_metadata.py:96: \u001b[92mTotal memory usage\u001b[0m: 1443.52 MB (Parameters: 1344.52 MB. KVCache: 0.00 MB. Temporary buffer: 99.00 MB)\n",
"[2024-03-31 12:21:39] INFO model_metadata.py:105: To reduce memory usage, tweak `prefill_chunk_size`, `context_window_size` and `sliding_window_size`\n",
"[2024-03-31 12:21:39] INFO compile.py:198: Generated: \u001b[1m/content/fine_tuned_science_gemma2b-it-q4f16_1-MLC/fine_tuned_science_gemma2b-it-q4f16_1-android.tar\u001b[0m\n"
]
}
],
"source": [
"!mlc_llm compile /content/$FINE_TUNED_MODEL_NAME-$QUANTIZATION-MLC/mlc-chat-config.json \\\n",
" --device android -o /content/$FINE_TUNED_MODEL_NAME-$QUANTIZATION-MLC/$FINE_TUNED_MODEL_NAME-$QUANTIZATION-android.tar"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PUscgEtrLV4L"
},
"source": [
"#### Push the quantized model to Hugging Face."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nbm0J54NLa3R",
"outputId": "bd974f90-6d22-499e-e8fe-2cd3f7063d87"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from huggingface_hub import whoami\n",
"from pathlib import Path\n",
"\n",
"# Output directory.\n",
"output_dir = \"fine_tuned_science_gemma2b-it-q4f16_1-MLC\"\n",
"repo_name = \"scigemma_fine_tuned_quantized_MLC\"\n",
"username = whoami(token=Path(\"/root/.cache/huggingface/\"))[\"name\"]\n",
"repo_id = f\"{username}/{repo_name}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
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"9a769b63e77a442996ca768eeb127308",
"651ec0f0ae6d4fe9956913ea447529d6",
"dc451ba629794749a38f92faf1628433",
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{
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"type": "string"
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"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/NSTiwari/scigemma_fine_tuned_quantized_MLC/commit/f6b97b9b3ede79b23f8f500f83294379ff3785cf', commit_message='Fine-tuned quantized Gemma 2b-it model.', commit_description='', oid='f6b97b9b3ede79b23f8f500f83294379ff3785cf', pr_url=None, pr_revision=None, pr_num=None)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from huggingface_hub import upload_folder, create_repo\n",
"\n",
"repo_id = create_repo(repo_id, exist_ok=True).repo_id\n",
"print(output_dir)\n",
"\n",
"upload_folder(\n",
" repo_id=repo_id,\n",
" folder_path=output_dir,\n",
" commit_message=\"Fine-tuned quantized Gemma 2b-it model.\",\n",
" ignore_patterns=[\"step_*\", \"epoch_*\"],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wGrbI7NrFOEb"
},
"source": [
"#### Step 10: Download the fine-tuned compiled model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tceHbQ9I_iX1"
},
"outputs": [],
"source": [
"!zip -r /content/gemma_2b_it_fine_tuned.zip /content/$FINE_TUNED_MODEL_NAME-$QUANTIZATION-MLC"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "M6U4bDoa_k6n"
},
"outputs": [],
"source": [
"from google.colab import files\n",
"files.download(\"/content/gemma_2b_it_fine_tuned.zip\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_WMK6EcntKTK"
},
"outputs": [],
"source": [
"from huggingface_hub import whoami\n",
"from pathlib import Path\n",
"\n",
"# Output directory.\n",
"output_dir = \"scigemma_fine_tuned_v0\"\n",
"repo_name = \"scigemma_fine_tuned_v0\"\n",
"username = whoami(token=Path(\"/root/.cache/huggingface/\"))[\"name\"]\n",
"repo_id = f\"{username}/{repo_name}\""
]
},
{
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"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69
},
"id": "naDV6yDttNli",
"outputId": "668a604b-b60a-43f1-a823-99bcf1c27812"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"scigemma_fine_tuned_v0\n"
]
},
{
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
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"CommitInfo(commit_url='https://huggingface.co/NSTiwari/scigemma_fine_tuned_v0/commit/0f06e2fea34decd766317c0faa84806dba4ed6ed', commit_message='Fine-tuned quantized Gemma 2b-it model.', commit_description='', oid='0f06e2fea34decd766317c0faa84806dba4ed6ed', pr_url=None, pr_revision=None, pr_num=None)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from huggingface_hub import upload_folder, create_repo\n",
"\n",
"repo_id = create_repo(repo_id, exist_ok=True).repo_id\n",
"print(output_dir)\n",
"\n",
"upload_folder(\n",
" repo_id=repo_id,\n",
" folder_path=output_dir,\n",
" commit_message=\"Fine-tuned quantized Gemma 2b-it model.\",\n",
" ignore_patterns=[\"step_*\", \"epoch_*\"],\n",
")"
]
}
],
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"accelerator": "GPU",
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"provenance": [],
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