Datasets
Models
Downloads: 1
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"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.**"
]
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"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"
]
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"* 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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"text": [
"Unsloth: You passed in `meta-llama/Meta-Llama-3-8B-Instruct` and `load_in_4bit = True`.\n",
"We shall load `unsloth/llama-3-8b-Instruct-bnb-4bit` for 4x faster loading.\n"
]
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"config.json: 0%| | 0.00/1.15k [00:00<?, ?B/s]"
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"==((====))== 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 = 0.0.25.post1. FA = True.\n",
" \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n"
]
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"text": [
"Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n"
]
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"model.safetensors: 0%| | 0.00/5.70G [00:00<?, ?B/s]"
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"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"
]
}
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"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 = True # 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 = \"meta-llama/Meta-Llama-3-8B-Instruct\",\n",
" max_seq_length = max_seq_length,\n",
" dtype = dtype,\n",
" load_in_4bit = load_in_4bit,\n",
" token = \"hf_yourwritabletoken\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
")"
]
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"cell_type": "markdown",
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"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/",
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"outputId": "a659525d-f52c-4f8c-eb35-a6eaa2ab8ce7"
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"output_type": "stream",
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"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",
")"
]
},
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"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)."
]
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"fe9aa7c72edd4998b4558374550792f7",
"8c54ed45bf034a5091d28d5f1812e291",
"93a34deba3614c959661ca29ad19005b",
"68523f3b7ee04a80a90651849b781ec5",
"7ac490e0b6614a92818f7e118540023b",
"90bdf9297ac94697a0eba15a125022c4"
]
},
"id": "LjY75GoYUCB8",
"outputId": "a3651ced-3b47-4f45-a767-8a146d5f204e"
},
"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": "e98d116cb3444f569c238343d1e92940"
}
},
"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": "447273c8ed9c41a69f08764591da9f09"
}
},
"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": "54cdb7b774e244dd9705361bdf9f7ca8"
}
},
"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": "3ca30ad8d3ef4ef9970ebfb3b44b09bc"
}
},
"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": "7c3c5e5608574c52a3a35b5d8f7bc146"
}
},
"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": "1e5f773bdab84ade9d36236d22db23ca"
}
},
"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": "2d433cb87d1d460fb31384078df0f6d4"
}
},
"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": "5d258a46e2554272a39ecc0bd2b4e4b2"
}
},
"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": "1b4ecc8a9eae42d3ac5363a11c42189f"
}
},
"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": "842a1d1d044c405cb7510f092bcc8363"
}
},
"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": [
"6334bb08605c4e5384ce42418b81dd62",
"e9aef030346140e689a71af45ca29e0d",
"a65307a2e7604ec58ebba832f5fc433b",
"0b86c760e0dd4e29acf60dae7af71fcb",
"ca65331d5b26471f8f717500e2ce2c02",
"4f45eed47bfa484e928160327ac88a01",
"9bfe52e790954329a4fd49710906e832",
"f9aee8e971594078bef4f2e05c2e7d5e",
"89555eab6a224e548a21ec71141c661b",
"be3fcacec7dd43a09572770eb2a02e93",
"2ea846eea90f46f381d60326070b58fd"
]
},
"id": "95_Nn-89DhsL",
"outputId": "44c60daa-6916-4e1e-d1ce-914d0ad85a33"
},
"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": "6334bb08605c4e5384ce42418b81dd62"
}
},
"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": "d428de06-5788-4bf2-9d1e-35ae2306bef4"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPU = NVIDIA A100-SXM4-40GB. Max memory = 39.564 GB.\n",
"5.459 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": 12711
},
"id": "yqxqAZ7KJ4oL",
"outputId": "d2a6be92-6e85-4efa-da49-1dd01ea2cde3"
},
"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 11:43, 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>1.968600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2.185600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2.230400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1.842200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1.912200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>2.123900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.675900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.511800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>1.492900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>1.226400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>1.185600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>1.319300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>1.079800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>1.062400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.938500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.876700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>0.963200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>0.846000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>0.896200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.874700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.818700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>0.826700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.824500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>0.843200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>0.664400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>0.837700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>0.669300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.716500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.843400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.706800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>0.700300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>0.730400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>0.718100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>0.698700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>0.645700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>0.706900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>0.640600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>0.587100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>0.737500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>0.599000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>0.572600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>0.648200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>0.555400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>0.663600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>0.746300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>0.609500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>0.592900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>0.655800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>0.585200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>0.604800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>0.590100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>0.564600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>0.622700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>0.589200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>0.562700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>0.583600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>0.494400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>0.510000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>0.582800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>0.710000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>61</td>\n",
" <td>0.578300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>62</td>\n",
" <td>0.622300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>63</td>\n",
" <td>0.566800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>64</td>\n",
" <td>0.600500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>65</td>\n",
" <td>0.527600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>66</td>\n",
" <td>0.518200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>67</td>\n",
" <td>0.749600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>68</td>\n",
" <td>0.552700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>69</td>\n",
" <td>0.531300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>70</td>\n",
" <td>0.523900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>71</td>\n",
" <td>0.673200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>72</td>\n",
" <td>0.763300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>73</td>\n",
" <td>0.484900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>74</td>\n",
" <td>0.508300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75</td>\n",
" <td>0.462800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>76</td>\n",
" <td>0.650700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>77</td>\n",
" <td>0.520600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>78</td>\n",
" <td>0.542200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>79</td>\n",
" <td>0.481000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>80</td>\n",
" <td>0.460600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>81</td>\n",
" <td>0.506100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>82</td>\n",
" <td>0.532600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>83</td>\n",
" <td>0.493200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>84</td>\n",
" <td>0.511200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>85</td>\n",
" <td>0.539200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>86</td>\n",
" <td>0.532600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>87</td>\n",
" <td>0.537600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>88</td>\n",
" <td>0.469000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>89</td>\n",
" <td>0.553700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>90</td>\n",
" <td>0.486300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>91</td>\n",
" <td>0.451300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>92</td>\n",
" <td>0.467900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>93</td>\n",
" <td>0.505500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>94</td>\n",
" <td>0.602200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>95</td>\n",
" <td>0.529700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>96</td>\n",
" <td>0.504000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>97</td>\n",
" <td>0.445800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>98</td>\n",
" <td>0.507200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>99</td>\n",
" <td>0.505500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>0.521300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>101</td>\n",
" <td>0.570400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>102</td>\n",
" <td>0.523900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>103</td>\n",
" <td>0.479500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>104</td>\n",
" <td>0.532500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>105</td>\n",
" <td>0.607400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>106</td>\n",
" <td>0.460900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>107</td>\n",
" <td>0.538400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>108</td>\n",
" <td>0.463400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>109</td>\n",
" <td>0.431400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>110</td>\n",
" <td>0.532500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>111</td>\n",
" <td>0.619000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>112</td>\n",
" <td>0.442200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>113</td>\n",
" <td>0.626200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>114</td>\n",
" <td>0.506700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>115</td>\n",
" <td>0.495600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>116</td>\n",
" <td>0.509600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>117</td>\n",
" <td>0.488200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>118</td>\n",
" <td>0.485500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>119</td>\n",
" <td>0.463000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>120</td>\n",
" <td>0.525300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>121</td>\n",
" <td>0.593600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>122</td>\n",
" <td>0.468400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>123</td>\n",
" <td>0.479400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>124</td>\n",
" <td>0.451400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>125</td>\n",
" <td>0.593200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>126</td>\n",
" <td>0.537500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>127</td>\n",
" <td>0.567400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>128</td>\n",
" <td>0.475300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>129</td>\n",
" <td>0.459400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>130</td>\n",
" <td>0.492400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>131</td>\n",
" <td>0.558800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>132</td>\n",
" <td>0.564000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>133</td>\n",
" <td>0.711400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>134</td>\n",
" <td>0.707200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>135</td>\n",
" <td>0.454500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>136</td>\n",
" <td>0.524000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>137</td>\n",
" <td>0.515500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>138</td>\n",
" <td>0.457600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>139</td>\n",
" <td>0.516500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>140</td>\n",
" <td>0.440200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>141</td>\n",
" <td>0.415000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>142</td>\n",
" <td>0.425900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>143</td>\n",
" <td>0.567800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>144</td>\n",
" <td>0.448100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>145</td>\n",
" <td>0.563400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>146</td>\n",
" <td>0.407900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>147</td>\n",
" <td>0.451900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>148</td>\n",
" <td>0.460500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>149</td>\n",
" <td>0.442300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>0.461100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>151</td>\n",
" <td>0.454800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>152</td>\n",
" <td>0.457000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>153</td>\n",
" <td>0.408800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>154</td>\n",
" <td>0.452300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>155</td>\n",
" <td>0.512600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>156</td>\n",
" <td>0.482100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>157</td>\n",
" <td>0.512500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>158</td>\n",
" <td>0.459300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>159</td>\n",
" <td>0.530700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>160</td>\n",
" <td>0.506100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>161</td>\n",
" <td>0.423300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>162</td>\n",
" <td>0.524300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>163</td>\n",
" <td>0.366400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>164</td>\n",
" <td>0.492300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>165</td>\n",
" <td>0.425500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>166</td>\n",
" <td>0.378100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>167</td>\n",
" <td>0.483300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>168</td>\n",
" <td>0.537500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>169</td>\n",
" <td>0.410300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>170</td>\n",
" <td>0.518800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>171</td>\n",
" <td>0.490700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>172</td>\n",
" <td>0.427400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>173</td>\n",
" <td>0.577000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>174</td>\n",
" <td>0.370200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>175</td>\n",
" <td>0.573400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>176</td>\n",
" <td>0.456400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>177</td>\n",
" <td>0.419800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>178</td>\n",
" <td>0.510000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>179</td>\n",
" <td>0.474100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>180</td>\n",
" <td>0.445900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>181</td>\n",
" <td>0.542800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>182</td>\n",
" <td>0.434800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>183</td>\n",
" <td>0.465800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>184</td>\n",
" <td>0.450000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>185</td>\n",
" <td>0.425200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>186</td>\n",
" <td>0.387100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>187</td>\n",
" <td>0.590400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>188</td>\n",
" <td>0.487600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>189</td>\n",
" <td>0.444500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>190</td>\n",
" <td>0.404000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>191</td>\n",
" <td>0.491900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>192</td>\n",
" <td>0.490600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>193</td>\n",
" <td>0.550200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>194</td>\n",
" <td>0.444000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>195</td>\n",
" <td>0.637300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>196</td>\n",
" <td>0.672200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>197</td>\n",
" <td>0.495500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>198</td>\n",
" <td>0.730700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>199</td>\n",
" <td>0.491500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>0.439800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>201</td>\n",
" <td>0.501800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>202</td>\n",
" <td>0.515000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>203</td>\n",
" <td>0.532200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>204</td>\n",
" <td>0.516600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>205</td>\n",
" <td>0.403500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>206</td>\n",
" <td>0.478000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>207</td>\n",
" <td>0.435000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>208</td>\n",
" <td>0.466300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>209</td>\n",
" <td>0.533600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>210</td>\n",
" <td>0.487100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>211</td>\n",
" <td>0.503600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>212</td>\n",
" <td>0.394500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>213</td>\n",
" <td>0.552900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>214</td>\n",
" <td>0.463500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>215</td>\n",
" <td>0.459000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>216</td>\n",
" <td>0.575100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>217</td>\n",
" <td>0.386300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>218</td>\n",
" <td>0.585200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>219</td>\n",
" <td>0.423800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>220</td>\n",
" <td>0.503100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>221</td>\n",
" <td>0.480000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>222</td>\n",
" <td>0.476000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>223</td>\n",
" <td>0.446500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>224</td>\n",
" <td>0.477000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>225</td>\n",
" <td>0.491800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>226</td>\n",
" <td>0.458400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>227</td>\n",
" <td>0.684100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>228</td>\n",
" <td>0.504800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>229</td>\n",
" <td>0.388200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>230</td>\n",
" <td>0.387600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>231</td>\n",
" <td>0.652200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>232</td>\n",
" <td>0.372800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>233</td>\n",
" <td>0.391700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>234</td>\n",
" <td>0.372100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>235</td>\n",
" <td>0.495400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>236</td>\n",
" <td>0.463100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>237</td>\n",
" <td>0.382900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>238</td>\n",
" <td>0.402600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>239</td>\n",
" <td>0.370500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>240</td>\n",
" <td>0.455200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>241</td>\n",
" <td>0.589800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>242</td>\n",
" <td>0.459600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>243</td>\n",
" <td>0.370900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>244</td>\n",
" <td>0.422000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>245</td>\n",
" <td>0.490200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>246</td>\n",
" <td>0.501900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>247</td>\n",
" <td>0.474500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>248</td>\n",
" <td>0.464900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>249</td>\n",
" <td>0.463100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>0.422600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>251</td>\n",
" <td>0.358600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>252</td>\n",
" <td>0.398100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>253</td>\n",
" <td>0.455000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>254</td>\n",
" <td>0.440400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>255</td>\n",
" <td>0.406500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>256</td>\n",
" <td>0.416900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>257</td>\n",
" <td>0.410600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>258</td>\n",
" <td>0.528900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>259</td>\n",
" <td>0.463200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>260</td>\n",
" <td>0.465700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>261</td>\n",
" <td>0.506200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>262</td>\n",
" <td>0.395000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>263</td>\n",
" <td>0.584700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>264</td>\n",
" <td>0.471600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>265</td>\n",
" <td>0.488600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>266</td>\n",
" <td>0.460000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>267</td>\n",
" <td>0.378700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>268</td>\n",
" <td>0.561300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>269</td>\n",
" <td>0.449400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>270</td>\n",
" <td>0.475200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>271</td>\n",
" <td>0.504100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>272</td>\n",
" <td>0.370200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>273</td>\n",
" <td>0.389200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>274</td>\n",
" <td>0.417500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>275</td>\n",
" <td>0.369600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>276</td>\n",
" <td>0.352100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>277</td>\n",
" <td>0.414400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>278</td>\n",
" <td>0.497900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>279</td>\n",
" <td>0.523200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>280</td>\n",
" <td>0.513200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>281</td>\n",
" <td>0.445600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>282</td>\n",
" <td>0.456400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>283</td>\n",
" <td>0.455300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>284</td>\n",
" <td>0.436700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>285</td>\n",
" <td>0.521000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>286</td>\n",
" <td>0.392400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>287</td>\n",
" <td>0.688200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>288</td>\n",
" <td>0.491800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>289</td>\n",
" <td>0.517700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>290</td>\n",
" <td>0.447800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>291</td>\n",
" <td>0.493800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>292</td>\n",
" <td>0.416500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>293</td>\n",
" <td>0.471900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>294</td>\n",
" <td>0.456700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>295</td>\n",
" <td>0.510000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>296</td>\n",
" <td>0.362200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>297</td>\n",
" <td>0.402800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>298</td>\n",
" <td>0.424600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>299</td>\n",
" <td>0.361800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>0.525800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>301</td>\n",
" <td>0.413200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>302</td>\n",
" <td>0.368400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>303</td>\n",
" <td>0.403900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>304</td>\n",
" <td>0.474800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>305</td>\n",
" <td>0.462300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>306</td>\n",
" <td>0.477700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>307</td>\n",
" <td>0.411400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>308</td>\n",
" <td>0.449100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>309</td>\n",
" <td>0.370800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>310</td>\n",
" <td>0.437800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>311</td>\n",
" <td>0.551300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>312</td>\n",
" <td>0.513900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>313</td>\n",
" <td>0.336300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>314</td>\n",
" <td>0.526700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>315</td>\n",
" <td>0.631800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>316</td>\n",
" <td>0.486800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>317</td>\n",
" <td>0.553500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>318</td>\n",
" <td>0.387700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>319</td>\n",
" <td>0.408300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>320</td>\n",
" <td>0.380700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>321</td>\n",
" <td>0.587000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>322</td>\n",
" <td>0.348900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>323</td>\n",
" <td>0.573300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>324</td>\n",
" <td>0.421800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>325</td>\n",
" <td>0.400200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>326</td>\n",
" <td>0.434600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>327</td>\n",
" <td>0.396900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>328</td>\n",
" <td>0.610200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>329</td>\n",
" <td>0.460500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330</td>\n",
" <td>0.438300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>331</td>\n",
" <td>0.494800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>332</td>\n",
" <td>0.478000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>333</td>\n",
" <td>0.453900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>334</td>\n",
" <td>0.453700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>335</td>\n",
" <td>0.415600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>336</td>\n",
" <td>0.461300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>337</td>\n",
" <td>0.386100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>338</td>\n",
" <td>0.350400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>339</td>\n",
" <td>0.481300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>340</td>\n",
" <td>0.415000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>341</td>\n",
" <td>0.529700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>342</td>\n",
" <td>0.365800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>343</td>\n",
" <td>0.450800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>344</td>\n",
" <td>0.473300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>345</td>\n",
" <td>0.422900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>346</td>\n",
" <td>0.394600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>347</td>\n",
" <td>0.553900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>348</td>\n",
" <td>0.472200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>349</td>\n",
" <td>0.398000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>0.417700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>351</td>\n",
" <td>0.433400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>352</td>\n",
" <td>0.396300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>353</td>\n",
" <td>0.385000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>354</td>\n",
" <td>0.634200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>355</td>\n",
" <td>0.373100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>356</td>\n",
" <td>0.468800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>357</td>\n",
" <td>0.334800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>358</td>\n",
" <td>0.490100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>359</td>\n",
" <td>0.399000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>360</td>\n",
" <td>0.437000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>361</td>\n",
" <td>0.463800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>362</td>\n",
" <td>0.435400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>363</td>\n",
" <td>0.394400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>364</td>\n",
" <td>0.471900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>365</td>\n",
" <td>0.503400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>366</td>\n",
" <td>0.535100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>367</td>\n",
" <td>0.365300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>368</td>\n",
" <td>0.367600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>369</td>\n",
" <td>0.348900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>370</td>\n",
" <td>0.475400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>371</td>\n",
" <td>0.454400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>372</td>\n",
" <td>0.365800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>373</td>\n",
" <td>0.416500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>374</td>\n",
" <td>0.459600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>375</td>\n",
" <td>0.421300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>376</td>\n",
" <td>0.394100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>377</td>\n",
" <td>0.453100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>378</td>\n",
" <td>0.429100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>379</td>\n",
" <td>0.424100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>380</td>\n",
" <td>0.353700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>381</td>\n",
" <td>0.518800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>382</td>\n",
" <td>0.457200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>383</td>\n",
" <td>0.427300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>384</td>\n",
" <td>0.355200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>385</td>\n",
" <td>0.389200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>386</td>\n",
" <td>0.392500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>387</td>\n",
" <td>0.400600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>388</td>\n",
" <td>0.353800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>389</td>\n",
" <td>0.462100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>390</td>\n",
" <td>0.395900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>391</td>\n",
" <td>0.388900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>392</td>\n",
" <td>0.362900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>393</td>\n",
" <td>0.400200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>394</td>\n",
" <td>0.379600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>395</td>\n",
" <td>0.372800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>396</td>\n",
" <td>0.343100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>397</td>\n",
" <td>0.517400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>398</td>\n",
" <td>0.457700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>399</td>\n",
" <td>0.510400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>0.417000</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": "609dc165-c33f-48ea-ba27-93a2c2a309c8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"712.032 seconds used for training.\n",
"11.87 minutes used for training.\n",
"Peak reserved memory = 10.775 GB.\n",
"Peak reserved memory for training = 5.316 GB.\n",
"Peak reserved memory % of max memory = 27.234 %.\n",
"Peak reserved memory for training % of max memory = 13.436 %.\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": "3ea21ee3-9230-43f1-bdd2-03e7403db886"
},
"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": [
"['<|begin_of_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:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13\\n\\n### Reason:\\nThe sequence is a Fibonacci sequence, where each number is the sum of the two preceding ones, usually starting with 0 and 1. The sequence is: 0, 1, 1, 2, 3, 5, 8, 13, 21,']"
]
},
"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": "b27fca9b-b617-4643-8dec-fe8a4128778c"
},
"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": [
"<|begin_of_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\n",
"\n",
"### Reason:\n",
"The sequence is a Fibonacci sequence, where each number is the sum of the two preceding ones, usually starting with 0 and 1. The sequence is: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144,... The sequence is named after the Italian mathematician Leonardo Fibonacci, who introduced it in the 13th century as a solution to a problem involving the growth of a population of rabbits. The sequence is often used to describe the growth of populations, the branching of\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": "352a6415-c44f-4a7e-d885-558136283641"
},
"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": [
"<|begin_of_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, which is the opening of San Francisco Bay into the Pacific Ocean. It connects the city of San Francisco, California, to Marin County, California. The bridge is painted a distinctive shade of orange color, known as \"International Orange,\" which was chosen to make the bridge more visible in the foggy conditions that are common in the area. The Golden Gate Bridge is one of the most iconic and photographed bridges in the world, and it is considered an engineering marvel. It was completed in 1937 and has been declared one of the Wonders of the Modern World\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": "5b136e7c-ab0e-424e-f6f4-a5786b1535de"
},
"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": [
"<|begin_of_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",
"The University of California, Berkeley is a public research university located in Berkeley, California. It is the oldest and one of the most prestigious institutions in the University of California system. The university is known for its academic excellence, research opportunities, and strong programs in a wide range of fields. It is also known for its social and political activism, and has been a hub for the counterculture and social movements of the 1960s and 1970s. The university is located in the San Francisco Bay Area, and is easily accessible by public transportation. It is a popular choice for students from all over the world, and is known\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": "8e6bb881-95ce-45d2-ba4f-0b9197673dff"
},
"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": [
"<|begin_of_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 adenine (A), guanine (G), cytosine (C), and thymine (T). The DNA bases are the building blocks of DNA, and they are arranged in a specific sequence to form the genetic code. The DNA bases are paired in a complementary manner to form the double helix structure of DNA. The pairing of the DNA bases is as follows: adenine (A) pairs with thymine (T), and guanine (G) pairs with cytosine (C). This pairing is crucial for the stability and function of DNA. The DNA bases are also involved in the transmission\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": "90dc22cd-9f53-40d1-8ab8-7c5a72aea36b"
},
"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": [
"<|begin_of_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 DNA molecule is composed of two strands of nucleotides that are covalently bonded together. The nucleotides are arranged in a specific sequence that contains the genetic information necessary for the development and function of an organism. The nucleotides are composed of a sugar molecule called deoxyribose, a phosphate group, and a nitrogenous base. The nitrogenous bases are adenine (A), guanine (G), cytosine (C), and thymine (T). The sugar and phosphate molecules are linked together by phosphodiester bonds, which form the backbone of the DNA molecule. The nitrogenous bases project inward\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": "ca86e05b-16e4-4651-d7b1-5f4bcf7c3640"
},
"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": [
"<|begin_of_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 molecule is a DNA.\n",
"\n",
"### Description:\n",
"The molecule is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a\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": "a8126950-a136-493a-f75f-bbdd10c8b5a7"
},
"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": [
"<|begin_of_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][N][C][=C][C][=C][Ring1][=Branch1]C\n",
"\n",
"### Description:\n",
"Adenine is a purine base that is the parent compound of the purine nucleosides and nucleotides. It is a member of purines and a member of pyrimidines. It is a conjugate base of a adeninium. It is a conjugate acid of an adenine(1-).\n",
"\n",
"### References:\n",
"1. PubChem. <https://pubchem.ncbi.nlm.nih.gov/compound/Adenine> 2. ChemIDplus. <https://\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"
},
"outputs": [],
"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": "03297f11-18be-4a2f-be04-5700a9440e4f"
},
"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": [
"[\"<|begin_of_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:\\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. It was built for the 1889 World's Fair and is one of the most recognizable landmarks in the world. It is 324 meters tall and is made of iron. It was designed by Gustave Eiffel and his company. It is a popular tourist destination and is often visited by millions of people each year. It is also a symbol of French culture and engineering. It is located in the Champ de Mars park in the 7th arrondissement of Paris. It is a very popular tourist destination and is often visited by millions of people each year. It\"]"
]
},
"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": 20,
"metadata": {
"id": "iHjt_SMYsd3P",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 704,
"referenced_widgets": [
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},
"outputId": "beff48d6-275b-4d81-9521-00ad79eba950"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n",
"model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n",
"Unsloth: Will remove a cached repo with size 5.7G\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
"Unsloth: Will use up to 61.34 out of 83.48 RAM for saving.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 32/32 [00:00<00:00, 65.84it/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: Saving to organization with address kevinkawchak/Meta-Llama-3-8B-Instruct-Molecule16\n",
"Unsloth: Saving tokenizer... Done.\n",
"Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
"Unsloth: Saving to organization with address kevinkawchak/Meta-Llama-3-8B-Instruct-Molecule16\n",
"Unsloth: Uploading all files... Please wait...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"model-00001-of-00004.safetensors: 0%| | 0.00/4.98G [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "6ff92caa71e9479e9d93fbf8c87bb18d"
}
},
"metadata": {}
},
{
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"text": [
"Done.\n",
"Saved merged model to https://huggingface.co/None/Meta-Llama-3-8B-Instruct-Molecule16\n",
"Unsloth: Merging 4bit and LoRA weights to 4bit...\n",
"This might take 5 minutes...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py:325: UserWarning: Merge lora module to 4-bit linear may get different generations due to rounding errors.\n",
" warnings.warn(\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Done.\n",
"Unsloth: Saving 4bit Bitsandbytes model. Please wait...\n"
]
},
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"text": [
"Saved merged_4bit model to https://huggingface.co/Meta-Llama-3-8B-Instruct-Molecule04\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(\"Meta-Llama-3-8B-Instruct-Molecule16\", tokenizer, save_method = \"merged_16bit\", token = \"hf_yourwritabletoken\", 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(\"Meta-Llama-3-8B-Instruct-Molecule04\", tokenizer, save_method = \"merged_4bit_forced\", token = \"hf_yourwritabletoken\", private=True)\n",
"\n",
"# Just LoRA adapters\n",
"if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
"if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")"
]
},
{
"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": 21,
"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": 22,
"metadata": {
"id": "zmdJjv6-GarM"
},
"outputs": [],
"source": [
"# from google.colab import runtime\n",
"# runtime.unassign()"
]
}
],
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