Datasets
Models
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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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"%%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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"==((====))== 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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"Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/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",
"meta-llama/Meta-Llama-3-8B-Instruct 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 = \"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",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SXd9bTZd1aaL"
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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/",
"height": 0
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"outputId": "df07949b-b893-4c7d-ebcb-11776dde4b5c"
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"outputs": [
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"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 = 5, # 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 = 3,\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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"cell_type": "markdown",
"metadata": {
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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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"eb530e3e72164a33bc9d37d34e9885f2",
"e964d09cd9dc4f0f86e8a2190c7d0118",
"f2c9d98fc50c4b62bf98dfc20d61b727",
"8ad4d3b23d0649cc81b33f8d45e310dd",
"f5dbbce4886643eb965f70c0461d670e",
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"95116694625c44a1b5eaa3f61147a012",
"72ce6d1296664928993eb975465b425a",
"bb99fb410d2a49e98ceaca045a616284",
"23f73f6fbcad49ffb300b0827d7a0029",
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"c8aff60e0e574a5e9427d23a10c1702b",
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"82a0a82f6781432a8036d1e8da076cee",
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"1a6c7aa08e874e71b7928c23d81be112",
"d5c00845ae71496b815c28c6afa4beda",
"88f582348718441599e29dfac29f131e",
"4ededd715a124691ac90b16c9bdc662a"
]
},
"id": "LjY75GoYUCB8",
"outputId": "6eabbb4a-a83e-4da6-98fd-3e8a0634a460"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading readme: 0%| | 0.00/11.6k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "7a73a0d4ed38459aaa2ee07fb219501a"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading data: 0%| | 0.00/44.3M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9e0f416af7ca4cabab005714c6595e91"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0%| | 0/51760 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f2c9d98fc50c4b62bf98dfc20d61b727"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/51760 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "bb99fb410d2a49e98ceaca045a616284"
}
},
"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(\"yahma/alpaca-cleaned\", split = \"train\")\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": 106,
"referenced_widgets": [
"5e5f3e5ddbb2426a90e8cf48d54d677b",
"9b01f7ec03494a4aa68618d19fa4227d",
"96813b33f4bd4132835188b35ff16323",
"8b8f0353f10d4377a51c05c4596e8001",
"9d1d15343ecd4fa6a1fd0935d28735da",
"82ef8dfdda62485fb418f627a7f5dade",
"5bc0bf05d4754e03bb0c1ffb3c515853",
"4d13106dc80b45a09106c4f1081b5d7b",
"8f7e95dddbe44d05a682e92745385389",
"7537fe8137f548f78e768bd0ec1de240",
"9037a707a1c448ab838762365569c018"
]
},
"id": "95_Nn-89DhsL",
"outputId": "e8f97d5c-788a-46ec-cb3b-204135e6dc76"
},
"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%| | 0/51760 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "5e5f3e5ddbb2426a90e8cf48d54d677b"
}
},
"metadata": {}
}
],
"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 = 60,\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": "53d0eeea-9f73-4c55-a7f7-44b1ed03dd77"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPU = NVIDIA A100-SXM4-40GB. Max memory = 39.564 GB.\n",
"15.195 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": 2049
},
"id": "yqxqAZ7KJ4oL",
"outputId": "441108ff-1395-4717-f1ca-78c6606673ed"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
" \\\\ /| Num examples = 51,760 | Num Epochs = 1\n",
"O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n",
"\\ / Total batch size = 8 | Total steps = 60\n",
" \"-____-\" Number of trainable parameters = 13,107,200\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [60/60 01:25, 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>1.753600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2.139800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.720000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2.050900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1.831800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>1.933500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.499000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.584500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>1.531500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>1.591500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>1.222600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>1.291100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>1.048700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>1.123100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.992800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.948000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>1.068400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>1.299700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>1.059500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.908400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.953100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>0.988600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.919900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>1.028700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>1.090700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>1.044700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>1.099700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.922300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.871500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.887200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>0.872500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>0.889700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>1.015700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>0.907100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>0.977800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>0.883100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>0.929200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>0.794800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>1.090400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>1.177800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>0.907800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>1.019700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>1.002600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>0.934900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>0.934100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>1.004900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>0.880300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>1.216100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>0.954000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>1.081900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>1.038900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>0.951800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>1.004400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>1.227800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>0.825000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>1.066700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>0.954700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>0.855100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>0.888400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>0.937900</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": "7d89a765-46aa-42c4-cd81-0315e6049407"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"94.2551 seconds used for training.\n",
"1.57 minutes used for training.\n",
"Peak reserved memory = 17.469 GB.\n",
"Peak reserved memory for training = 2.274 GB.\n",
"Peak reserved memory % of max memory = 44.154 %.\n",
"Peak reserved memory for training % of max memory = 5.748 %.\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": "2f1e9868-63d4-402e-b05a-8e2021b691c0"
},
"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, 21, 34, 55, 89, 144, 233, 377, 610, 985, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']"
]
},
"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": "896de721-3ce4-4372-92d3-4cf070cf39a9"
},
"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, 21, 34, 55, 89, 144, 233, 377, 610, 985, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040, 1346269, 2178309, 3524578, 5702887, 9227465, 14930352, 24157817, 39088169, 632459\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": "0a506382-a19b-457f-e6d5-2eed1842c443"
},
"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 famous bridge in the San Francisco Bay Area. It is a suspension bridge that spans the Golden Gate strait, connecting the city of San Francisco to Marin County, California. It is one of the most iconic and recognizable landmarks in the world, and it is a popular tourist destination. The bridge was completed in 1937 and was originally painted a deep orange color, which was later changed to the more familiar international orange color. It is 1.7 miles long and 746 feet tall, and it carries over 110,000 vehicles per day. The Golden Gate Bridge is also a popular spot for pedestrians\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": "575af210-3f06-483b-fd7d-6f8b006e4d18"
},
"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",
"One of the most famous universities in the San Francisco Bay Area is Stanford University. It is located in Stanford, California, and is known for its academic excellence, research opportunities, and successful alumni. Stanford is a private research university that was founded in 1885 and is considered one of the top universities in the world. It is also known for its proximity to Silicon Valley, which is home to many major technology companies. Stanford is a popular choice for students who are interested in pursuing a career in technology, business, or other fields. It is also known for its beautiful campus, which features a mix of modern and historic buildings, as well\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": "c8865690-90c2-4874-c1aa-a5d5e42cdc4a"
},
"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 adenine (A), guanine (G), cytosine (C), and thymine (T). These four bases are the building blocks of DNA and are paired together in a specific manner to form the double helix structure of DNA. Adenine pairs with thymine, and guanine pairs with cytosine. This pairing is crucial for the proper functioning of DNA and is essential for the transmission of genetic information from one generation to the next. The sequence of these bases determines the genetic code and ultimately the characteristics of an organism. The four DNA bases are also known as nucleotides, and\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": "82ae0ca6-66cb-42a6-a599-5502e7a56849"
},
"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",
"There are four main types of bonds found in DNA: hydrogen bonds, covalent bonds, ionic bonds, and van der Waals bonds. \n",
"\n",
"1. Hydrogen bonds: These bonds are formed between the nitrogenous bases of DNA, specifically between the hydrogen atoms of one base and the oxygen or nitrogen atoms of another base. Hydrogen bonds are weak and reversible, but they play a crucial role in the structure and function of DNA.\n",
"\n",
"2. Covalent bonds: These bonds are formed between the carbon and oxygen atoms of the sugar molecule and the nitrogenous bases. Covalent bonds are strong and unbreakable, and\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": "829c8385-7ad0-4156-9142-045841a32e2f"
},
"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:\n",
"\n",
"1. Primase: This enzyme adds nucleotides to the growing DNA chain.\n",
"2. Polymerase: This enzyme links nucleotides together to form a DNA strand.\n",
"3. Ligase: This enzyme seals the gaps between DNA strands.\n",
"4. Helicase: This enzyme unwinds the double helix structure of DNA.\n",
"5. Topoisomerase: This enzyme relaxes the tension in the DNA strands.\n",
"6. DNA polymerase: This enzyme adds nucleotides to the growing DNA chain.\n",
"7. DNA ligase: This enzyme seals the gaps between DNA strands.\n",
"8.\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": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "upcOlWe7A1vc",
"outputId": "de2f89b8-bda1-4011-e695-8dd6514daafe"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:581: UserWarning: Unable to fetch remote file due to the following error 401 Client Error. (Request ID: Root=1-6625ba8d-158b813131b37be33056561d;d0e6e351-bbac-40c4-bd9a-4622b1ba76de)\n",
"\n",
"Cannot access gated repo for url https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/resolve/main/config.json.\n",
"Repo model meta-llama/Meta-Llama-3-8B-Instruct is gated. You must be authenticated to access it. - silently ignoring the lookup for the file config.json in meta-llama/Meta-Llama-3-8B-Instruct.\n",
" warnings.warn(\n",
"/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in meta-llama/Meta-Llama-3-8B-Instruct - will assume that the vocabulary was not modified.\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": "4e41efb3-f354-4467-decf-1b740245b73e"
},
"execution_count": 17,
"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. It was built for the 1889 World's Fair and stands at a height of 324 meters (1,063 feet). It is one of the most recognizable landmarks in the world and is a symbol of Paris and France. It was designed by Gustave Eiffel and took two years to build. It was originally intended to be a temporary structure, but it has become a permanent part of the Parisian skyline and a popular tourist destination. Visitors can take an elevator or stairs to the top of the tower for a panoramic view of the city. The Eiffel\"]"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"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": 18,
"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": 19,
"metadata": {
"id": "iHjt_SMYsd3P",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 416,
"referenced_widgets": [
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"64350e5a5d3e4dd1b7ea1f063c006f79",
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},
"outputId": "531dd16f-03b0-4c4f-86d7-15a9d05ef3fb"
},
"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 16.1G\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
"Unsloth: Will use up to 62.88 out of 83.48 RAM for saving.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 32/32 [00:00<00:00, 72.40it/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: Saving to organization with address kevinkawchak/Meta-Llama-3-8B-Instruct-LoRA-A3\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-LoRA-A3\n",
"Unsloth: Uploading all files... Please wait...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"model-00003-of-00004.safetensors: 0%| | 0.00/4.92G [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "69e89c55c10b4c6dbcc66263522b0e7a"
}
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"model-00002-of-00004.safetensors: 0%| | 0.00/5.00G [00:00<?, ?B/s]"
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{
"output_type": "display_data",
"data": {
"text/plain": [
"model-00001-of-00004.safetensors: 0%| | 0.00/4.98G [00:00<?, ?B/s]"
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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Upload 4 LFS files: 0%| | 0/4 [00:00<?, ?it/s]"
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"text": [
"Done.\n",
"Saved merged model to https://huggingface.co/None/Meta-Llama-3-8B-Instruct-LoRA-A3\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-LoRA-A3\", 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 False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\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": 20,
"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": 21,
"metadata": {
"id": "zmdJjv6-GarM"
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"outputs": [],
"source": [
"# from google.colab import runtime\n",
"# runtime.unassign()"
]
}
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