Datasets
Models
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"source": [
"To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
"<div class=\"align-center\">\n",
" <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
" <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
" <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join Discord if you need help + support us if you can!\n",
"</div>\n",
"\n",
"To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n",
"\n",
"You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\n",
"\n",
"**[NEW] Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "2eSvM9zX_2d3"
},
"outputs": [],
"source": [
"%%capture\n",
"import torch\n",
"major_version, minor_version = torch.cuda.get_device_capability()\n",
"# Must install separately since Colab has torch 2.2.1, which breaks packages\n",
"!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
"if major_version >= 8:\n",
" # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n",
" !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n",
"else:\n",
" # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n",
" !pip install --no-deps xformers trl peft accelerate bitsandbytes\n",
"pass\n",
"# Llama 3 Video Tutorial https://www.youtube.com/watch?v=aQmoog_s8HE"
]
},
{
"cell_type": "markdown",
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"id": "r2v_X2fA0Df5"
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"source": [
"* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n",
"* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n",
"* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
"* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
"* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models."
]
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"name": "stderr",
"text": [
"WARNING:xformers:WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
" PyTorch 2.3.0+cu121 with CUDA 1201 (you have 2.2.1+cu121)\n",
" Python 3.10.14 (you have 3.10.12)\n",
" Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n",
" Memory-efficient attention, SwiGLU, sparse and more won't be available.\n",
" Set XFORMERS_MORE_DETAILS=1 for more details\n",
"/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"
]
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"text": [
"==((====))== Unsloth: Fast Llama patching release 2024.4\n",
" \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform = Linux.\n",
"O^O/ \\_/ \\ Pytorch: 2.2.1+cu121. CUDA = 8.0. CUDA Toolkit = 12.1.\n",
"\\ / Bfloat16 = TRUE. Xformers = 0.0.26.post1. FA = True.\n",
" \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n"
]
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"pytorch_model.bin.index.json: 0%| | 0.00/26.8k [00:00<?, ?B/s]"
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"data": {
"text/plain": [
"Downloading shards: 0%| | 0/2 [00:00<?, ?it/s]"
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"data": {
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
],
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"version_major": 2,
"version_minor": 0,
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"text": [
"Some weights of the model checkpoint at nvidia/Llama3-ChatQA-1.5-8B were not used when initializing LlamaForCausalLM: ['model.layers.0.self_attn.rotary_emb.inv_freq', 'model.layers.1.self_attn.rotary_emb.inv_freq', 'model.layers.10.self_attn.rotary_emb.inv_freq', 'model.layers.11.self_attn.rotary_emb.inv_freq', 'model.layers.12.self_attn.rotary_emb.inv_freq', 'model.layers.13.self_attn.rotary_emb.inv_freq', 'model.layers.14.self_attn.rotary_emb.inv_freq', 'model.layers.15.self_attn.rotary_emb.inv_freq', 'model.layers.16.self_attn.rotary_emb.inv_freq', 'model.layers.17.self_attn.rotary_emb.inv_freq', 'model.layers.18.self_attn.rotary_emb.inv_freq', 'model.layers.19.self_attn.rotary_emb.inv_freq', 'model.layers.2.self_attn.rotary_emb.inv_freq', 'model.layers.20.self_attn.rotary_emb.inv_freq', 'model.layers.21.self_attn.rotary_emb.inv_freq', 'model.layers.22.self_attn.rotary_emb.inv_freq', 'model.layers.23.self_attn.rotary_emb.inv_freq', 'model.layers.24.self_attn.rotary_emb.inv_freq', 'model.layers.25.self_attn.rotary_emb.inv_freq', 'model.layers.26.self_attn.rotary_emb.inv_freq', 'model.layers.27.self_attn.rotary_emb.inv_freq', 'model.layers.28.self_attn.rotary_emb.inv_freq', 'model.layers.29.self_attn.rotary_emb.inv_freq', 'model.layers.3.self_attn.rotary_emb.inv_freq', 'model.layers.30.self_attn.rotary_emb.inv_freq', 'model.layers.31.self_attn.rotary_emb.inv_freq', 'model.layers.4.self_attn.rotary_emb.inv_freq', 'model.layers.5.self_attn.rotary_emb.inv_freq', 'model.layers.6.self_attn.rotary_emb.inv_freq', 'model.layers.7.self_attn.rotary_emb.inv_freq', 'model.layers.8.self_attn.rotary_emb.inv_freq', 'model.layers.9.self_attn.rotary_emb.inv_freq']\n",
"- This IS expected if you are initializing LlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing LlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
},
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"data": {
"text/plain": [
"generation_config.json: 0%| | 0.00/136 [00:00<?, ?B/s]"
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"tokenizer_config.json: 0%| | 0.00/50.6k [00:00<?, ?B/s]"
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"version_major": 2,
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"tokenizer.json: 0%| | 0.00/9.08M [00:00<?, ?B/s]"
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"model_id": "3482cfc1285b4f44932ec673ff9af0e3"
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"data": {
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/73.0 [00:00<?, ?B/s]"
],
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"version_major": 2,
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{
"output_type": "stream",
"name": "stderr",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
"nvidia/Llama3-ChatQA-1.5-8B does not have a padding or unknown token!\n",
"Will use the EOS token of id 128001 as padding.\n"
]
}
],
"source": [
"from unsloth import FastLanguageModel\n",
"import torch\n",
"max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
"dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
"load_in_4bit = False # Use 4bit quantization to reduce memory usage. Can be False.\n",
"\n",
"# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
"# fourbit_models = [\n",
"# \"unsloth/mistral-7b-bnb-4bit\",\n",
"# \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n",
"# \"unsloth/llama-2-7b-bnb-4bit\",\n",
"# \"unsloth/gemma-7b-bnb-4bit\",\n",
"# \"unsloth/gemma-7b-it-bnb-4bit\", # Instruct version of Gemma 7b\n",
"# \"unsloth/gemma-2b-bnb-4bit\",\n",
"# \"unsloth/gemma-2b-it-bnb-4bit\", # Instruct version of Gemma 2b\n",
"# \"unsloth/llama-3-8b-bnb-4bit\", # [NEW] 15 Trillion token Llama-3\n",
"# ] # More models at https://huggingface.co/unsloth\n",
"\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"nvidia/Llama3-ChatQA-1.5-8B\",\n",
" max_seq_length = max_seq_length,\n",
" dtype = dtype,\n",
" load_in_4bit = load_in_4bit,\n",
" token = \"hf_\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SXd9bTZd1aaL"
},
"source": [
"We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "6bZsfBuZDeCL",
"outputId": "d2edbf2e-51e9-4a0f-ba12-bd4beb9a3cc7"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Unsloth 2024.4 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
]
}
],
"source": [
"model = FastLanguageModel.get_peft_model(\n",
" model,\n",
" r = 1, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
" target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
" \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
" lora_alpha = 5,\n",
" lora_dropout = 0, # Supports any, but = 0 is optimized\n",
" bias = \"none\", # Supports any, but = \"none\" is optimized\n",
" # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
" use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
" random_state = 3407,\n",
" use_rslora = False, # We support rank stabilized LoRA\n",
" loftq_config = None, # And LoftQ\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vITh0KVJ10qX"
},
"source": [
"<a name=\"Data\"></a>\n",
"### Data Prep\n",
"We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n",
"\n",
"**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n",
"\n",
"**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n",
"\n",
"If you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n",
"\n",
"For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)."
]
},
{
"cell_type": "code",
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"base_uri": "https://localhost:8080/",
"height": 430,
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},
"id": "LjY75GoYUCB8",
"outputId": "7d101216-7bbc-4ab5-8972-d0fb01952dfa"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for zjunlp/Mol-Instructions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/zjunlp/Mol-Instructions\n",
"You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
"Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading builder script: 0%| | 0.00/7.34k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b105d9a47d8f4a3eac7fecf1c665d4b5"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading readme: 0%| | 0.00/19.6k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c23c9d7d2bfe4d989fceffb916714958"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading data: 0%| | 0.00/73.2M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "7a52fbf65f644175a023804a785241fb"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating description_guided_molecule_design split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "08f9470fddff4832bdb56e2229838109"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating forward_reaction_prediction split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8ce5582e02a448c291f89af29d8c4a00"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating molecular_description_generation split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8cf08dd07d2b4d568884dd1e08bac7a8"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating property_prediction split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "1999f8cf964e4f66be0d2fc75ca6e9b9"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating reagent_prediction split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "4c59c980819c43ad8ce5126c6bf89861"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating retrosynthesis split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "deffdaa547ba467fa6d8d34b961c8b49"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/298319 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e9a0884be95a4eecab0bbf32194c700a"
}
},
"metadata": {}
}
],
"source": [
"alpaca_prompt = \"\"\"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",
"\n",
"### Instruction:\n",
"{}\n",
"\n",
"### Input:\n",
"{}\n",
"\n",
"### Response:\n",
"{}\"\"\"\n",
"\n",
"EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
"def formatting_prompts_func(examples):\n",
" instructions = examples[\"instruction\"]\n",
" inputs = examples[\"input\"]\n",
" outputs = examples[\"output\"]\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 = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n",
" texts.append(text)\n",
" return { \"text\" : texts, }\n",
"pass\n",
"\n",
"from datasets import load_dataset\n",
"dataset = load_dataset(\"zjunlp/Mol-Instructions\", \"Molecule-oriented Instructions\", split=\"description_guided_molecule_design\")\n",
"dataset = dataset.map(formatting_prompts_func, batched = True,)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "idAEIeSQ3xdS"
},
"source": [
"<a name=\"Train\"></a>\n",
"### Train the model\n",
"Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 124,
"referenced_widgets": [
"029f0dd1adb34818b63e424447df1b38",
"d08aa7985a0f4d46860e62681a214ec0",
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"5fab9560c1c84bd2859b1b522ecaefdf",
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]
},
"id": "95_Nn-89DhsL",
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},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
" self.pid = os.fork()\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map (num_proc=2): 0%| | 0/298319 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "029f0dd1adb34818b63e424447df1b38"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"max_steps is given, it will override any value given in num_train_epochs\n"
]
}
],
"source": [
"from trl import SFTTrainer\n",
"from transformers import TrainingArguments\n",
"\n",
"trainer = SFTTrainer(\n",
" model = model,\n",
" tokenizer = tokenizer,\n",
" train_dataset = dataset,\n",
" dataset_text_field = \"text\",\n",
" max_seq_length = max_seq_length,\n",
" dataset_num_proc = 2,\n",
" packing = False, # Can make training 5x faster for short sequences.\n",
" args = TrainingArguments(\n",
" per_device_train_batch_size = 2,\n",
" gradient_accumulation_steps = 4,\n",
" warmup_steps = 5,\n",
" max_steps = 400,\n",
" learning_rate = 2e-4,\n",
" fp16 = not torch.cuda.is_bf16_supported(),\n",
" bf16 = torch.cuda.is_bf16_supported(),\n",
" logging_steps = 1,\n",
" optim = \"adamw_8bit\",\n",
" weight_decay = 0.01,\n",
" lr_scheduler_type = \"linear\",\n",
" seed = 3407,\n",
" output_dir = \"outputs\",\n",
" ),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "2ejIt2xSNKKp",
"outputId": "761772b4-b4d4-4ff9-eeae-6636c265faf2"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPU = NVIDIA A100-SXM4-40GB. Max memory = 39.564 GB.\n",
"15.094 GB of memory reserved.\n"
]
}
],
"source": [
"#@title Show current memory stats\n",
"gpu_stats = torch.cuda.get_device_properties(0)\n",
"start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
"max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
"print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
"print(f\"{start_gpu_memory} GB of memory reserved.\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 12705
},
"id": "yqxqAZ7KJ4oL",
"outputId": "409adcd1-41ac-4bf8-ba74-4b467393288f"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
" \\\\ /| Num examples = 298,319 | Num Epochs = 1\n",
"O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n",
"\\ / Total batch size = 8 | Total steps = 400\n",
" \"-____-\" Number of trainable parameters = 2,621,440\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='400' max='400' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [400/400 09:51, Epoch 0/1]\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>1.918900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2.120800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2.188200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1.793900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1.848100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>2.075200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.634900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.435100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>1.367400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>1.102000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>1.105100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>1.231900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>1.020600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>1.040000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.928400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.844400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>0.914900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>0.779800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>0.826100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.772200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.736600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>0.751400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.764700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>0.787400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>0.602000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>0.762100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>0.621400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.657300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.766200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.671700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>0.645300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>0.688300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>0.668700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>0.648200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>0.612400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>0.667900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>0.618100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>0.545500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>0.704400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>0.586800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>0.566000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>0.657400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>0.549900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>0.651200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>0.727400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>0.597600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>0.584600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>0.633200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>0.563600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>0.590400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>0.578100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>0.561200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>0.615500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>0.585000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>0.561400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>0.579200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>0.482300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>0.509400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>0.573300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>0.681300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>61</td>\n",
" <td>0.562300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>62</td>\n",
" <td>0.597300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>63</td>\n",
" <td>0.569700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>64</td>\n",
" <td>0.592500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>65</td>\n",
" <td>0.529600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>66</td>\n",
" <td>0.504800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>67</td>\n",
" <td>0.728700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>68</td>\n",
" <td>0.548500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>69</td>\n",
" <td>0.534400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>70</td>\n",
" <td>0.520600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>71</td>\n",
" <td>0.669100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>72</td>\n",
" <td>0.754600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>73</td>\n",
" <td>0.508100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>74</td>\n",
" <td>0.501500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75</td>\n",
" <td>0.457200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>76</td>\n",
" <td>0.651800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>77</td>\n",
" <td>0.517400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>78</td>\n",
" <td>0.535800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>79</td>\n",
" <td>0.478900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>80</td>\n",
" <td>0.456900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>81</td>\n",
" <td>0.502300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>82</td>\n",
" <td>0.530400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>83</td>\n",
" <td>0.499100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>84</td>\n",
" <td>0.519200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>85</td>\n",
" <td>0.538000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>86</td>\n",
" <td>0.523400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>87</td>\n",
" <td>0.533300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>88</td>\n",
" <td>0.462400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>89</td>\n",
" <td>0.562200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>90</td>\n",
" <td>0.489400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>91</td>\n",
" <td>0.449000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>92</td>\n",
" <td>0.476800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>93</td>\n",
" <td>0.518600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>94</td>\n",
" <td>0.579000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>95</td>\n",
" <td>0.531300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>96</td>\n",
" <td>0.496000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>97</td>\n",
" <td>0.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>98</td>\n",
" <td>0.518200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>99</td>\n",
" <td>0.514500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>0.518000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>101</td>\n",
" <td>0.557100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>102</td>\n",
" <td>0.522600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>103</td>\n",
" <td>0.472400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>104</td>\n",
" <td>0.528700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>105</td>\n",
" <td>0.612100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>106</td>\n",
" <td>0.462500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>107</td>\n",
" <td>0.541200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>108</td>\n",
" <td>0.455400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>109</td>\n",
" <td>0.432200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>110</td>\n",
" <td>0.526200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>111</td>\n",
" <td>0.608200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>112</td>\n",
" <td>0.442100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>113</td>\n",
" <td>0.610400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>114</td>\n",
" <td>0.512500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>115</td>\n",
" <td>0.500300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>116</td>\n",
" <td>0.505600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>117</td>\n",
" <td>0.487000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>118</td>\n",
" <td>0.482300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>119</td>\n",
" <td>0.459900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>120</td>\n",
" <td>0.525800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>121</td>\n",
" <td>0.593400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>122</td>\n",
" <td>0.469700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>123</td>\n",
" <td>0.479000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>124</td>\n",
" <td>0.452100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>125</td>\n",
" <td>0.575000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>126</td>\n",
" <td>0.528000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>127</td>\n",
" <td>0.557400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>128</td>\n",
" <td>0.470000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>129</td>\n",
" <td>0.470200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>130</td>\n",
" <td>0.486000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>131</td>\n",
" <td>0.548000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>132</td>\n",
" <td>0.549300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>133</td>\n",
" <td>0.690200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>134</td>\n",
" <td>0.696200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>135</td>\n",
" <td>0.458100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>136</td>\n",
" <td>0.518900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>137</td>\n",
" <td>0.504000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>138</td>\n",
" <td>0.457900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>139</td>\n",
" <td>0.505700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>140</td>\n",
" <td>0.433700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>141</td>\n",
" <td>0.420200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>142</td>\n",
" <td>0.432100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>143</td>\n",
" <td>0.561700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>144</td>\n",
" <td>0.445600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>145</td>\n",
" <td>0.573900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>146</td>\n",
" <td>0.403500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>147</td>\n",
" <td>0.449000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>148</td>\n",
" <td>0.445500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>149</td>\n",
" <td>0.449600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>0.450900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>151</td>\n",
" <td>0.455100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>152</td>\n",
" <td>0.467200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>153</td>\n",
" <td>0.395900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>154</td>\n",
" <td>0.430000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>155</td>\n",
" <td>0.512500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>156</td>\n",
" <td>0.471900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>157</td>\n",
" <td>0.497100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>158</td>\n",
" <td>0.457900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>159</td>\n",
" <td>0.527100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>160</td>\n",
" <td>0.498100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>161</td>\n",
" <td>0.421300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>162</td>\n",
" <td>0.515900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>163</td>\n",
" <td>0.358900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>164</td>\n",
" <td>0.476900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>165</td>\n",
" <td>0.417100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>166</td>\n",
" <td>0.367700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>167</td>\n",
" <td>0.478800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>168</td>\n",
" <td>0.533100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>169</td>\n",
" <td>0.425000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>170</td>\n",
" <td>0.514400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>171</td>\n",
" <td>0.484500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>172</td>\n",
" <td>0.411500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>173</td>\n",
" <td>0.555200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>174</td>\n",
" <td>0.363900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>175</td>\n",
" <td>0.560700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>176</td>\n",
" <td>0.448600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>177</td>\n",
" <td>0.421900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>178</td>\n",
" <td>0.495500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>179</td>\n",
" <td>0.469300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>180</td>\n",
" <td>0.441900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>181</td>\n",
" <td>0.534800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>182</td>\n",
" <td>0.443400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>183</td>\n",
" <td>0.465000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>184</td>\n",
" <td>0.447900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>185</td>\n",
" <td>0.418700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>186</td>\n",
" <td>0.391100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>187</td>\n",
" <td>0.586800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>188</td>\n",
" <td>0.474900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>189</td>\n",
" <td>0.439900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>190</td>\n",
" <td>0.391200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>191</td>\n",
" <td>0.475000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>192</td>\n",
" <td>0.493700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>193</td>\n",
" <td>0.534900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>194</td>\n",
" <td>0.445600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>195</td>\n",
" <td>0.620100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>196</td>\n",
" <td>0.659700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>197</td>\n",
" <td>0.492500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>198</td>\n",
" <td>0.713400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>199</td>\n",
" <td>0.485600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>0.432100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>201</td>\n",
" <td>0.487500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>202</td>\n",
" <td>0.516200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>203</td>\n",
" <td>0.534600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>204</td>\n",
" <td>0.508600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>205</td>\n",
" <td>0.400800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>206</td>\n",
" <td>0.474300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>207</td>\n",
" <td>0.425600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>208</td>\n",
" <td>0.447400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>209</td>\n",
" <td>0.539600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>210</td>\n",
" <td>0.490900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>211</td>\n",
" <td>0.498000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>212</td>\n",
" <td>0.398200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>213</td>\n",
" <td>0.542300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>214</td>\n",
" <td>0.451300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>215</td>\n",
" <td>0.457300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>216</td>\n",
" <td>0.557700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>217</td>\n",
" <td>0.384500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>218</td>\n",
" <td>0.562100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>219</td>\n",
" <td>0.423900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>220</td>\n",
" <td>0.483100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>221</td>\n",
" <td>0.464600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>222</td>\n",
" <td>0.480800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>223</td>\n",
" <td>0.452700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>224</td>\n",
" <td>0.473100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>225</td>\n",
" <td>0.483700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>226</td>\n",
" <td>0.459100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>227</td>\n",
" <td>0.621900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>228</td>\n",
" <td>0.494500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>229</td>\n",
" <td>0.391300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>230</td>\n",
" <td>0.383500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>231</td>\n",
" <td>0.625800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>232</td>\n",
" <td>0.382200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>233</td>\n",
" <td>0.390100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>234</td>\n",
" <td>0.372700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>235</td>\n",
" <td>0.493000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>236</td>\n",
" <td>0.422200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>237</td>\n",
" <td>0.384500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>238</td>\n",
" <td>0.395900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>239</td>\n",
" <td>0.370200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>240</td>\n",
" <td>0.443400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>241</td>\n",
" <td>0.589900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>242</td>\n",
" <td>0.465900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>243</td>\n",
" <td>0.382200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>244</td>\n",
" <td>0.425400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>245</td>\n",
" <td>0.477300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>246</td>\n",
" <td>0.485700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>247</td>\n",
" <td>0.476300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>248</td>\n",
" <td>0.468200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>249</td>\n",
" <td>0.464900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>0.427900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>251</td>\n",
" <td>0.354300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>252</td>\n",
" <td>0.397400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>253</td>\n",
" <td>0.461500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>254</td>\n",
" <td>0.440000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>255</td>\n",
" <td>0.401200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>256</td>\n",
" <td>0.421100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>257</td>\n",
" <td>0.399900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>258</td>\n",
" <td>0.522100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>259</td>\n",
" <td>0.462400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>260</td>\n",
" <td>0.462300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>261</td>\n",
" <td>0.492300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>262</td>\n",
" <td>0.399600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>263</td>\n",
" <td>0.575300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>264</td>\n",
" <td>0.475000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>265</td>\n",
" <td>0.492500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>266</td>\n",
" <td>0.459800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>267</td>\n",
" <td>0.380500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>268</td>\n",
" <td>0.555400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>269</td>\n",
" <td>0.449000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>270</td>\n",
" <td>0.458000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>271</td>\n",
" <td>0.487900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>272</td>\n",
" <td>0.372700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>273</td>\n",
" <td>0.389800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>274</td>\n",
" <td>0.421400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>275</td>\n",
" <td>0.370700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>276</td>\n",
" <td>0.350700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>277</td>\n",
" <td>0.417900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>278</td>\n",
" <td>0.491700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>279</td>\n",
" <td>0.508900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>280</td>\n",
" <td>0.515500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>281</td>\n",
" <td>0.440800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>282</td>\n",
" <td>0.458300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>283</td>\n",
" <td>0.449300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>284</td>\n",
" <td>0.438900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>285</td>\n",
" <td>0.514800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>286</td>\n",
" <td>0.395800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>287</td>\n",
" <td>0.636900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>288</td>\n",
" <td>0.491500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>289</td>\n",
" <td>0.518600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>290</td>\n",
" <td>0.445200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>291</td>\n",
" <td>0.496000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>292</td>\n",
" <td>0.406700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>293</td>\n",
" <td>0.479000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>294</td>\n",
" <td>0.460200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>295</td>\n",
" <td>0.498700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>296</td>\n",
" <td>0.369000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>297</td>\n",
" <td>0.394700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>298</td>\n",
" <td>0.419100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>299</td>\n",
" <td>0.358800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>0.515700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>301</td>\n",
" <td>0.410000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>302</td>\n",
" <td>0.367000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>303</td>\n",
" <td>0.399300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>304</td>\n",
" <td>0.485600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>305</td>\n",
" <td>0.471900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>306</td>\n",
" <td>0.470500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>307</td>\n",
" <td>0.408100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>308</td>\n",
" <td>0.440800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>309</td>\n",
" <td>0.367800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>310</td>\n",
" <td>0.422300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>311</td>\n",
" <td>0.533800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>312</td>\n",
" <td>0.515600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>313</td>\n",
" <td>0.333500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>314</td>\n",
" <td>0.509600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>315</td>\n",
" <td>0.608200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>316</td>\n",
" <td>0.477100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>317</td>\n",
" <td>0.530200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>318</td>\n",
" <td>0.386700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>319</td>\n",
" <td>0.413200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>320</td>\n",
" <td>0.381700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>321</td>\n",
" <td>0.565400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>322</td>\n",
" <td>0.348000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>323</td>\n",
" <td>0.557100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>324</td>\n",
" <td>0.420100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>325</td>\n",
" <td>0.391500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>326</td>\n",
" <td>0.414700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>327</td>\n",
" <td>0.376500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>328</td>\n",
" <td>0.594300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>329</td>\n",
" <td>0.448800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330</td>\n",
" <td>0.430800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>331</td>\n",
" <td>0.471400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>332</td>\n",
" <td>0.469100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>333</td>\n",
" <td>0.454000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>334</td>\n",
" <td>0.452500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>335</td>\n",
" <td>0.399900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>336</td>\n",
" <td>0.449700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>337</td>\n",
" <td>0.379000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>338</td>\n",
" <td>0.346300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>339</td>\n",
" <td>0.475500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>340</td>\n",
" <td>0.417000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>341</td>\n",
" <td>0.529900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>342</td>\n",
" <td>0.376500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>343</td>\n",
" <td>0.441900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>344</td>\n",
" <td>0.464800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>345</td>\n",
" <td>0.421300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>346</td>\n",
" <td>0.381800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>347</td>\n",
" <td>0.545200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>348</td>\n",
" <td>0.458800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>349</td>\n",
" <td>0.396200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>0.404900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>351</td>\n",
" <td>0.426700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>352</td>\n",
" <td>0.388400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>353</td>\n",
" <td>0.375600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>354</td>\n",
" <td>0.614800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>355</td>\n",
" <td>0.364300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>356</td>\n",
" <td>0.448900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>357</td>\n",
" <td>0.331600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>358</td>\n",
" <td>0.486800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>359</td>\n",
" <td>0.392400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>360</td>\n",
" <td>0.437800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>361</td>\n",
" <td>0.417800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>362</td>\n",
" <td>0.432200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>363</td>\n",
" <td>0.389800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>364</td>\n",
" <td>0.472800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>365</td>\n",
" <td>0.492300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>366</td>\n",
" <td>0.527900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>367</td>\n",
" <td>0.358100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>368</td>\n",
" <td>0.364300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>369</td>\n",
" <td>0.336100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>370</td>\n",
" <td>0.464100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>371</td>\n",
" <td>0.453000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>372</td>\n",
" <td>0.364300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>373</td>\n",
" <td>0.408700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>374</td>\n",
" <td>0.445600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>375</td>\n",
" <td>0.412200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>376</td>\n",
" <td>0.384500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>377</td>\n",
" <td>0.441600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>378</td>\n",
" <td>0.423900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>379</td>\n",
" <td>0.414100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>380</td>\n",
" <td>0.350200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>381</td>\n",
" <td>0.502700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>382</td>\n",
" <td>0.450800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>383</td>\n",
" <td>0.416900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>384</td>\n",
" <td>0.363200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>385</td>\n",
" <td>0.374200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>386</td>\n",
" <td>0.391100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>387</td>\n",
" <td>0.407100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>388</td>\n",
" <td>0.346900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>389</td>\n",
" <td>0.463500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>390</td>\n",
" <td>0.385500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>391</td>\n",
" <td>0.390500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>392</td>\n",
" <td>0.370400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>393</td>\n",
" <td>0.399200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>394</td>\n",
" <td>0.370000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>395</td>\n",
" <td>0.373900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>396</td>\n",
" <td>0.346100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>397</td>\n",
" <td>0.498200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>398</td>\n",
" <td>0.448800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>399</td>\n",
" <td>0.489500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>0.418300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
}
],
"source": [
"trainer_stats = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "pCqnaKmlO1U9",
"outputId": "8658ee6b-c390-4ac7-b384-ad0a710e1e62"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"599.4502 seconds used for training.\n",
"9.99 minutes used for training.\n",
"Peak reserved memory = 20.223 GB.\n",
"Peak reserved memory for training = 5.129 GB.\n",
"Peak reserved memory % of max memory = 51.115 %.\n",
"Peak reserved memory for training % of max memory = 12.964 %.\n"
]
}
],
"source": [
"#@title Show final memory and time stats\n",
"used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
"used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
"used_percentage = round(used_memory /max_memory*100, 3)\n",
"lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
"print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
"print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
"print(f\"Peak reserved memory = {used_memory} GB.\")\n",
"print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
"print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
"print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ekOmTR1hSNcr"
},
"source": [
"<a name=\"Inference\"></a>\n",
"### Inference\n",
"Let's run the model! You can change the instruction and input - leave the output blank!"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "kR3gIAX-SM2q",
"outputId": "e2d4ceb3-6026-4061-e8a5-1cc14bb72bc3"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['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\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13<|end_of_text|>']"
]
},
"metadata": {},
"execution_count": 9
}
],
"source": [
"# alpaca_prompt = Copied from above\n",
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"inputs = tokenizer(\n",
"[\n",
" alpaca_prompt.format(\n",
" \"Continue the fibonnaci sequence.\", # instruction\n",
" \"1, 1, 2, 3, 5, 8\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
"], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
"tokenizer.batch_decode(outputs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CrSvZObor0lY"
},
"source": [
" You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "e2pEuRb1r2Vg",
"outputId": "18080456-6d74-44f3-9e45-2cb20fa4bab0"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"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",
"\n",
"### Instruction:\n",
"Continue the fibonnaci sequence.\n",
"\n",
"### Input:\n",
"1, 1, 2, 3, 5, 8\n",
"\n",
"### Response:\n",
"13<|end_of_text|>\n"
]
}
],
"source": [
"# alpaca_prompt = Copied from above\n",
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"inputs = tokenizer(\n",
"[\n",
" alpaca_prompt.format(\n",
" \"Continue the fibonnaci sequence.\", # instruction\n",
" \"1, 1, 2, 3, 5, 8\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
"], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
]
},
{
"cell_type": "code",
"source": [
"# alpaca_prompt = Copied from above\n",
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"inputs = tokenizer(\n",
"[\n",
" alpaca_prompt.format(\n",
" \"What is a famous bridge in San Francisco bay area?\", # instruction\n",
" \"\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
"], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "Z5tWI7ivVjMg",
"outputId": "6f6baa35-1b13-4671-d910-d824e66f1640"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"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",
"\n",
"### Instruction:\n",
"What is a famous bridge in San Francisco bay area?\n",
"\n",
"### Input:\n",
"\n",
"\n",
"### Response:\n",
"The Golden Gate Bridge is a suspension bridge that spans the Golden Gate Strait, the mile-wide, three-mile-long channel between San Francisco Bay and the Pacific Ocean.<|end_of_text|>\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# alpaca_prompt = Copied from above\n",
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"inputs = tokenizer(\n",
"[\n",
" alpaca_prompt.format(\n",
" \"What is a famous university in San Francisco bay area?\", # instruction\n",
" \"\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
"], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "9fJZQcwlVlBq",
"outputId": "079a5704-678a-48a6-e41b-eb9526af113e"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"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",
"\n",
"### Instruction:\n",
"What is a famous university in San Francisco bay area?\n",
"\n",
"### Input:\n",
"\n",
"\n",
"### Response:\n",
"Stanford University is a famous university in San Francisco bay area.<|end_of_text|>\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# alpaca_prompt = Copied from above\n",
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"inputs = tokenizer(\n",
"[\n",
" alpaca_prompt.format(\n",
" \"What are the DNA bases?\", # instruction\n",
" \"\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
"], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "1TPeJxviVls8",
"outputId": "2db6af74-dc11-45eb-84b3-b0a716e3d519"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"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",
"\n",
"### Instruction:\n",
"What are the DNA bases?\n",
"\n",
"### Input:\n",
"\n",
"\n",
"### Response:\n",
"The DNA bases are the four nucleobases that are found in DNA. They are adenine (A), cytosine (C), guanine (G), and thymine (T).<|end_of_text|>\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# alpaca_prompt = Copied from above\n",
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"inputs = tokenizer(\n",
"[\n",
" alpaca_prompt.format(\n",
" \"What are all of the types of bonds found in DNA?\", # instruction\n",
" \"\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
"], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "GlPUCSprVnRK",
"outputId": "a2a2bd5e-e175-4163-b8c9-59dfde786e6c"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"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",
"\n",
"### Instruction:\n",
"What are all of the types of bonds found in DNA?\n",
"\n",
"### Input:\n",
"\n",
"\n",
"### Response:\n",
"The four types of bonds found in DNA are hydrogen bonds, phosphodiester bonds, covalent bonds, and ionic bonds.<|end_of_text|>\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# alpaca_prompt = Copied from above\n",
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"inputs = tokenizer(\n",
"[\n",
" alpaca_prompt.format(\n",
" \"What are the reaction names to create DNA?\", # instruction\n",
" \"\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
"], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "NelWwdJjVpqR",
"outputId": "4d21c371-0fb5-41c2-971a-a2a3802e0b3a"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"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",
"\n",
"### Instruction:\n",
"What are the reaction names to create DNA?\n",
"\n",
"### Input:\n",
"\n",
"\n",
"### Response:\n",
"The reaction names to create DNA are DNA polymerization and DNA replication.<|end_of_text|>\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# alpaca_prompt = Copied from above\n",
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"inputs = tokenizer(\n",
"[\n",
" alpaca_prompt.format(\n",
" \"What is the structure for adenine?\", # instruction\n",
" \"\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
"], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "FQw11BVmrV-f",
"outputId": "13faeee6-0226-4d1b-c3a6-29c2662d4f0d"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"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",
"\n",
"### Instruction:\n",
"What is the structure for adenine?\n",
"\n",
"### Input:\n",
"\n",
"\n",
"### Response:\n",
"[C][C][Branch1][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uMuVrWbjAzhc"
},
"source": [
"<a name=\"Save\"></a>\n",
"### Saving, loading finetuned models\n",
"To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
"\n",
"**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"id": "upcOlWe7A1vc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"outputId": "8b077467-8558-405d-c397-d498ed565c81"
},
"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"
]
}
],
"source": [
"model.save_pretrained(\"lora_model\") # Local saving\n",
"# model.push_to_hub(\"Your-Model-Name\", organization=\"kevinkawchak\", token = \"Your HF writable token\", private=True) # ONLY saves the LoRA adapters"
]
},
{
"cell_type": "code",
"source": [
"if False:\n",
" from unsloth import FastLanguageModel\n",
" model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
" max_seq_length = max_seq_length,\n",
" dtype = dtype,\n",
" load_in_4bit = load_in_4bit,\n",
" )\n",
" FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"# alpaca_prompt = You MUST copy from above!\n",
"inputs = tokenizer(\n",
"[\n",
" alpaca_prompt.format(\n",
" \"What is a famous tall tower in Paris?\", # instruction\n",
" \"\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
"], return_tensors = \"pt\").to(\"cuda\")\n",
"outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n",
"tokenizer.batch_decode(outputs)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "RYh575LYOG5J",
"outputId": "4686a43c-839c-47cf-ef8d-c2723184b5a1"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['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\\n### Instruction:\\nWhat is a famous tall tower in Paris?\\n\\n### Input:\\n\\n\\n### Response:\\nThe Eiffel Tower is a famous tall tower in Paris.<|end_of_text|>']"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AEEcJ4qfC7Lp"
},
"source": [
"Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QQMjaNrjsU5_"
},
"source": [
"You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "yFfaXG0WsQuE"
},
"outputs": [],
"source": [
"## If using model.save_pretrained(\"lora_model\") # Local saving\n",
"if False:\n",
" # I highly do NOT suggest - use Unsloth if possible\n",
" from peft import AutoPeftModelForCausalLM\n",
" from transformers import AutoTokenizer\n",
" model = AutoPeftModelForCausalLM.from_pretrained(\n",
" \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
" load_in_4bit = load_in_4bit,\n",
" )\n",
" tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f422JgM9sdVT"
},
"source": [
"### Saving to float16 for VLLM\n",
"\n",
"We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "iHjt_SMYsd3P",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206,
"referenced_widgets": [
"21efc5c935a74e399d4bb7402ec21bd5",
"d6c2ad7d6f634cebacf96b2ab411fe69",
"ef0016a3d73c4925ab602ecbcc2a9ecd",
"b63d3cfd22a443509b05faf2f0139564",
"03a37e97ad844a5e835a759fba6f85aa",
"a07aebf884d242c89c3fe38d4dc1d987",
"36dd174ab9ce4d0da88a05906a8c2bbb",
"c20c18e05ae24a9a8a7820f74077c6a0",
"d5e72923fc87490192f3c9bdc9376cf1",
"7f75d497905f42c8940a94e9d446c936",
"993c768a6ade405e80b54704caa4b010",
"bf926de571624957b17b677173c95497",
"5e4521a9b479429d9e30b1508d5e08d0",
"38aef7e27aea46838aa15de407b4fa53",
"5c7197a9495e4e9194cc900846ea18ee",
"de70c5e7ee7a46fdb7bb1d120f37d0f5",
"de388ca2a20849cd88209af458746b3f",
"c09ffc8bcfc64229b3857832b821a747",
"8191ee4bda644f9ca57dbdcc1ed1eda1",
"cceea9cc9f0d49e7bfc7c1a3d19bf1df",
"f3f51801dd0b42e7883f2c068380d2ec",
"f8c2cd649e18456086e061eedcbc4970",
"883a4bcfacea4df6a23b27aba8a5cac7",
"f227c3c7c08d4ee6b5005a9635b9b99f",
"23c7d3f944c34d52ba4501b6d1d6beea",
"0d85ead432c44e06b212532801752d26",
"4425f01c7aff4416b92e08c2a49c0283",
"ae48783d6e154b358788c9949aec12ab",
"04cd6402aef24bd6b98150e3066889ee",
"0b0f71eda22149e7aeb761cecac09d90",
"a70fb36a75d1476295795417f1d0a513",
"6c6f346f55a5489687c1c579b043c070",
"bbd88bff2d8d44dbbce4c891cbfb226e"
]
},
"outputId": "3867448e-82d8-4f16-acac-07c4580c143c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: Saving LoRA adapters. Please wait...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"README.md: 0%| | 0.00/579 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "21efc5c935a74e399d4bb7402ec21bd5"
}
},
"metadata": {}
},
{
"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"
]
},
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{
"output_type": "stream",
"name": "stdout",
"text": [
"Saved lora model to https://huggingface.co/nvidia-Llama3-ChatQA-1.5-8B-MoleculeLo\n"
]
}
],
"source": [
"# Merge to 16bit\n",
"if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
"if True: model.push_to_hub_merged(\"nvidia-Llama3-ChatQA-1.5-8B-Molecule16\", tokenizer, save_method = \"merged_16bit\", token = \"hf_\", private=True)\n",
"\n",
"# Merge to 4bit\n",
"if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
"if True: model.push_to_hub_merged(\"nvidia-Llama3-ChatQA-1.5-8B-Molecule04\", tokenizer, save_method = \"merged_4bit_forced\", token = \"hf_\", private=True)\n",
"\n",
"# Just LoRA adapters\n",
"if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
"if True: model.push_to_hub_merged(\"nvidia-Llama3-ChatQA-1.5-8B-MoleculeLo\", tokenizer, save_method = \"lora\", token = \"hf_\", private=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TCv4vXHd61i7"
},
"source": [
"### GGUF / llama.cpp Conversion\n",
"To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
"\n",
"Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
"* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
"* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
"* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "FqfebeAdT073"
},
"outputs": [],
"source": [
"# Save to 8bit Q8_0\n",
"if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
"\n",
"# Save to 16bit GGUF\n",
"if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
"\n",
"# Save to q4_k_m GGUF\n",
"if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bDp0zNpwe6U_"
},
"source": [
"Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zt9CHJqO6p30"
},
"source": [
"And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
"\n",
"Some other links:\n",
"1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n",
"2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n",
"3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n",
"4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n",
"5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n",
"6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n",
"7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n",
"8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n",
"\n",
"<div class=\"align-center\">\n",
" <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
" <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
" <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Support our work if you can! Thanks!\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "zmdJjv6-GarM"
},
"outputs": [],
"source": [
"# from google.colab import runtime\n",
"# runtime.unassign()"
]
}
],
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