{
"cells": [
{
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"source": [
"To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
"<div class=\"align-center\">\n",
" <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
" <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
" <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join Discord if you need help + support us if you can!\n",
"</div>\n",
"\n",
"To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n",
"\n",
"You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp)."
],
"metadata": {
"id": "IqM-T1RTzY6C"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2eSvM9zX_2d3"
},
"outputs": [],
"source": [
"%%capture\n",
"# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
"!pip install unsloth\n",
"# Get latest Unsloth\n",
"!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\""
]
},
{
"cell_type": "markdown",
"source": [
"* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n",
"* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n",
"* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
"* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
"* 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.\n",
"* [**NEW**] We make Gemma 6 trillion tokens **2.5x faster**! See our [Gemma notebook](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)"
],
"metadata": {
"id": "r2v_X2fA0Df5"
}
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"output_type": "stream",
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"text": [
"/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:66: UserWarning: Running `ldconfig /usr/lib64-nvidia` to link CUDA.\n",
" warnings.warn(\n"
]
},
{
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"data": {
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"config.json: 0%| | 0.00/1.05k [00:00<?, ?B/s]"
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"text": [
"==((====))== Unsloth: Fast Mistral patching release 2024.2\n",
" \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
"O^O/ \\_/ \\ Pytorch: 2.1.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
"\\ / Bfloat16 = FALSE. Xformers = 0.0.22.post7. FA = False.\n",
" \"-____-\" Apache 2 free license: http://github.com/unslothai/unsloth\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"You passed `quantization_config` to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` attribute will be overwritten with the one you passed to `from_pretrained`.\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/llama-2-13b-bnb-4bit\",\n",
" \"unsloth/codellama-34b-bnb-4bit\",\n",
" \"unsloth/tinyllama-bnb-4bit\",\n",
" \"unsloth/gemma-7b-bnb-4bit\", # New Google 6 trillion tokens model 2.5x faster!\n",
" \"unsloth/gemma-2b-bnb-4bit\",\n",
"] # More models at https://huggingface.co/unsloth\n",
"\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"unsloth/mistral-7b-bnb-4bit\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\n",
" max_seq_length = max_seq_length,\n",
" dtype = dtype,\n",
" load_in_4bit = load_in_4bit,\n",
" # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
")"
]
},
{
"cell_type": "markdown",
"source": [
"We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
],
"metadata": {
"id": "SXd9bTZd1aaL"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6bZsfBuZDeCL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b630cc80-ff95-45a2-cc0d-38666010d73b"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Unsloth 2024.2 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 = 16, # 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 = 16,\n",
" lora_dropout = 0, # Supports any, but = 0 is optimized\n",
" bias = \"none\", # Supports any, but = \"none\" is optimized\n",
" use_gradient_checkpointing = True,\n",
" random_state = 3407,\n",
" use_rslora = False, # We support rank stabilized LoRA\n",
" loftq_config = None, # And LoftQ\n",
")"
]
},
{
"cell_type": "markdown",
"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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{
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"Downloading readme: 0%| | 0.00/11.6k [00:00<?, ?B/s]"
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"Generating train split: 0 examples [00:00, ? examples/s]"
],
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"Map: 0%| | 0/51760 [00:00<?, ? examples/s]"
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"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",
"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`!"
],
"metadata": {
"id": "idAEIeSQ3xdS"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "95_Nn-89DhsL",
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"base_uri": "https://localhost:8080/",
"height": 49,
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"0c266c0637064fbe8d548d0876c6b315",
"aa315e0b196d4b80a114690a689d5fbf",
"5b6f098adcb84717b162152ddaadfa0b",
"caaf2d5a555f41fab3b74f3096375ba9",
"1789d926e02549b19e4aca59f44f320a",
"3816c6f816784e5ca11a458bbf790031",
"954d6540772d40a99f642c2524db3a0c",
"fdc5073e7cc24edc8c5b920a363b4a40",
"f69d18a0b745434d8d078939e3a0c7fc"
]
},
"outputId": "4b809e6d-271f-446f-dec8-abe0d13259f8"
},
"outputs": [
{
"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": "28408ab4afb7494f8cb2f834458f411c"
}
},
"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": null,
"metadata": {
"id": "2ejIt2xSNKKp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"outputId": "4815a050-0c0f-4a6a-9d93-b01c44eaea35"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPU = Tesla T4. Max memory = 14.748 GB.\n",
"4.625 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": null,
"metadata": {
"id": "yqxqAZ7KJ4oL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "3cf26aac-6042-4458-c4a6-d8849efb6a95"
},
"outputs": [
{
"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 07:56, 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>1.408900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.709700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.148400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1.177400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>0.986600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>0.998200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.032900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>0.887500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>0.937400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>0.931900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>0.902100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>0.923200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>0.813900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>0.978600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.820900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.879200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>1.005600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>0.720100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>0.588800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.721400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.884100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>0.929400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.754300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>0.748600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>0.895600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>0.735300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>0.859500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.861600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.785600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.746100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>0.793800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>0.757500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>0.858700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>0.851300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>0.739500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>0.754100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>0.767000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>0.801800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>0.885900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>0.927400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>0.734400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>0.596500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>0.699700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>0.661200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>0.965600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>0.576600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>0.752800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>0.757400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>0.715200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>0.961300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>0.829200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>0.713100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>0.843700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>0.921400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>0.715100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>0.948600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>0.740700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>0.893900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>0.772700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>0.880000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
}
],
"source": [
"trainer_stats = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pCqnaKmlO1U9",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "cf63d152-e152-468c-ba0d-938e0d2f71a0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"488.38 seconds used for training.\n",
"8.14 minutes used for training.\n",
"Peak reserved memory = 6.846 GB.\n",
"Peak reserved memory for training = 2.221 GB.\n",
"Peak reserved memory % of max memory = 46.42 %.\n",
"Peak reserved memory for training % of max memory = 15.06 %.\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",
"source": [
"<a name=\"Inference\"></a>\n",
"### Inference\n",
"Let's run the model! You can change the instruction and input - leave the output blank!"
],
"metadata": {
"id": "ekOmTR1hSNcr"
}
},
{
"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",
" \"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)"
],
"metadata": {
"id": "kR3gIAX-SM2q",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5b71f982-38c0-44c8-a4e5-58cd20b5a585"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['<s> 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:\\nThe next number in the Fibonacci sequence is 13.</s>']"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"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!"
],
"metadata": {
"id": "CrSvZObor0lY"
}
},
{
"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",
" \"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)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e2pEuRb1r2Vg",
"outputId": "084aab62-2122-436a-c0cb-8871986640eb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"<s> 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",
"The next number in the Fibonacci sequence is 13.</s>\n"
]
}
]
},
{
"cell_type": "markdown",
"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!"
],
"metadata": {
"id": "uMuVrWbjAzhc"
}
},
{
"cell_type": "code",
"source": [
"model.save_pretrained(\"lora_model\") # Local saving\n",
"# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
],
"metadata": {
"id": "upcOlWe7A1vc"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
],
"metadata": {
"id": "AEEcJ4qfC7Lp"
}
},
{
"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",
"\n",
"# alpaca_prompt = You MUST copy from above!\n",
"\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",
"\n",
"outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
"tokenizer.batch_decode(outputs)"
],
"metadata": {
"id": "MKX_XKs_BNZR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "05e5a193-dab0-41db-e07c-4b3afbdd7932"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['<s> 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, France. It is located on the Champ de Mars and is one of the most recognizable structures in the world.</s>']"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "markdown",
"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**."
],
"metadata": {
"id": "QQMjaNrjsU5_"
}
},
{
"cell_type": "code",
"source": [
"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\")"
],
"metadata": {
"id": "yFfaXG0WsQuE"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"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."
],
"metadata": {
"id": "f422JgM9sdVT"
}
},
{
"cell_type": "code",
"source": [
"# Merge to 16bit\n",
"if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
"if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\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 = \"\")"
],
"metadata": {
"id": "iHjt_SMYsd3P"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"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."
],
"metadata": {
"id": "TCv4vXHd61i7"
}
},
{
"cell_type": "code",
"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 = \"\")"
],
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"id": "FqfebeAdT073"
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"cell_type": "markdown",
"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)."
],
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"cell_type": "markdown",
"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",
"9. Gemma 6 trillion tokens is 2.5x faster! [free Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?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>"
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