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chore: import upstream snapshot with attribution
2026-07-13 13:24:42 +08:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "WE5GJ6s7y0Xo"
},
"source": [
"## Fine-tune large models using 🤗 `peft` adapters, `transformers` & `bitsandbytes`\n",
"\n",
"In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in 8-bit.\n",
"The fine-tuning method will rely on a recent method called \"Low Rank Adapters\" (LoRA), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model. \n",
"After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TfBzP8gWzkpv"
},
"source": [
"### Install requirements\n",
"\n",
"First, run the cells below to install the requirements:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "otj46qRbtpnd",
"outputId": "2aa109f6-3f4e-4887-a16e-336f51e7cc9a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m72.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
]
}
],
"source": [
"!pip install -q datasets==3.6.0 accelerate\n",
"!pip install -q git+https://github.com/bitsandbytes-foundation/bitsandbytes.git\n",
"!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FOtwYRI3zzXI"
},
"source": [
"### Model loading\n",
"\n",
"Here let's load the `opt-6.7b` model, its weights in half-precision (float16) are about 13GB on the Hub! If we load them in 8-bit we would require around 7GB of memory instead."
]
},
{
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"height": 408,
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"outputId": "135a7675-6a4d-4786-b5dc-34cb867f40c7"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bee2f575b3e64c30b2f3afa137802406",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"import bitsandbytes as bnb\n",
"from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, BitsAndBytesConfig\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\"facebook/opt-6.7b\", quantization_config=BitsAndBytesConfig(load_in_8bit=True))\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"facebook/opt-6.7b\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "9fTSZntA1iUG"
},
"source": [
"### Prepare model for training\n",
"\n",
"Some pre-processing needs to be done before training such an int8 model using `peft`, therefore let's import an utiliy function `prepare_model_for_kbit_training` that will: \n",
"- Casts all the non `int8` modules to full precision (`fp32`) for stability\n",
"- Add a `forward_hook` to the input embedding layer to enable gradient computation of the input hidden states\n",
"- Enable gradient checkpointing for more memory-efficient training"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "T-gy-LxM0yAi"
},
"outputs": [],
"source": [
"from peft import prepare_model_for_kbit_training\n",
"\n",
"model = prepare_model_for_kbit_training(model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KwOTr7B3NlM3"
},
"source": [
"### Apply LoRA\n",
"\n",
"Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "4W1j6lxaNnxC"
},
"outputs": [],
"source": [
"def print_trainable_parameters(model):\n",
" \"\"\"\n",
" Prints the number of trainable parameters in the model.\n",
" \"\"\"\n",
" trainable_params = 0\n",
" all_param = 0\n",
" for _, param in model.named_parameters():\n",
" all_param += param.numel()\n",
" if param.requires_grad:\n",
" trainable_params += param.numel()\n",
" print(\n",
" f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4iwHGzKBN6wk",
"outputId": "039f7175-14c9-42b4-a078-80d27aab161c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 8388608 || all params: 6666862592 || trainable%: 0.12582542214183376\n"
]
}
],
"source": [
"from peft import LoraConfig, get_peft_model\n",
"\n",
"config = LoraConfig(\n",
" r=16, lora_alpha=32, target_modules=[\"q_proj\", \"v_proj\"], lora_dropout=0.05, bias=\"none\", task_type=\"CAUSAL_LM\"\n",
")\n",
"\n",
"model = get_peft_model(model, config)\n",
"print_trainable_parameters(model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QdjWif4CVXR6"
},
"source": [
"### Training"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b_RPiO7f3ClX"
},
"source": []
},
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},
"id": "AQ_HCYruWIHU",
"outputId": "e9baadaf-e202-413d-87f0-74c730d78408"
},
"outputs": [
{
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"text/plain": [
"Downloading readme: 0%| | 0.00/5.55k [00:00<?, ?B/s]"
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},
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{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:datasets.builder:Using custom data configuration Abirate--english_quotes-6e72855d06356857\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset json/Abirate--english_quotes to /root/.cache/huggingface/datasets/Abirate___json/Abirate--english_quotes-6e72855d06356857/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\n"
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{
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{
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{
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"text/plain": [
"Generating train split: 0 examples [00:00, ? examples/s]"
]
},
"metadata": {},
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},
{
"name": "stdout",
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"text": [
"Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/Abirate___json/Abirate--english_quotes-6e72855d06356857/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n"
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{
"data": {
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{
"name": "stderr",
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"text": [
"The model is loaded in 8-bit precision. To train this model you need to add additional modules inside the model such as adapters using `peft` library and freeze the model weights. Please check the examples in https://github.com/huggingface/peft for more details.\n",
"max_steps is given, it will override any value given in num_train_epochs\n",
"Using cuda_amp half precision backend\n",
"The following columns in the training set don't have a corresponding argument in `PeftModelForCausalLM.forward` and have been ignored: author, tags, quote. If author, tags, quote are not expected by `PeftModelForCausalLM.forward`, you can safely ignore this message.\n",
"/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n",
"***** Running training *****\n",
" Num examples = 2508\n",
" Num Epochs = 2\n",
" Instantaneous batch size per device = 4\n",
" Total train batch size (w. parallel, distributed & accumulation) = 16\n",
" Gradient Accumulation steps = 4\n",
" Total optimization steps = 200\n",
" Number of trainable parameters = 8388608\n",
"/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
" warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
]
},
{
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"\n",
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" <progress value='153' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [153/200 26:04 < 08:06, 0.10 it/s, Epoch 0.97/2]\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>2.364400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2.200400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2.302300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2.184700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1.878700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>2.307200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>2.193800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>2.446200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>2.458900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>2.020000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>1.941200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>1.931000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>2.055900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>1.975100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>2.015100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>2.095600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>1.768300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>2.155700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>2.402300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>2.124600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>2.314900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>1.908500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>2.078800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>1.941900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>1.879800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>1.927500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>1.371400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>1.977600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>2.055000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>1.915800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>1.958100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>2.195900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>2.001000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>2.025000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>1.576900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>1.879800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>1.821600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>1.727800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>1.995700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>1.698600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>2.129300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>2.025800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>1.696500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>1.984700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>2.051100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>2.054400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>1.765600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>2.063100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>1.746900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>1.873000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>2.391300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>2.494100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>2.072300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>1.808000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>1.911900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>2.168100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>2.166100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>1.921500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>1.856000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>1.652800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>61</td>\n",
" <td>1.605000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>62</td>\n",
" <td>2.032500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>63</td>\n",
" <td>1.822100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>64</td>\n",
" <td>1.623600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>65</td>\n",
" <td>1.923200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>66</td>\n",
" <td>2.053200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>67</td>\n",
" <td>2.114300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>68</td>\n",
" <td>1.807700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>69</td>\n",
" <td>1.857800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>70</td>\n",
" <td>1.854600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>71</td>\n",
" <td>2.023000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>72</td>\n",
" <td>1.864900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>73</td>\n",
" <td>1.769300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>74</td>\n",
" <td>1.837700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75</td>\n",
" <td>1.742200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>76</td>\n",
" <td>1.895900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>77</td>\n",
" <td>1.922800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>78</td>\n",
" <td>2.325300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>79</td>\n",
" <td>2.231200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>80</td>\n",
" <td>2.309500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>81</td>\n",
" <td>1.945700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>82</td>\n",
" <td>2.072100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>83</td>\n",
" <td>1.917400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>84</td>\n",
" <td>2.004600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>85</td>\n",
" <td>1.951700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>86</td>\n",
" <td>1.450600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>87</td>\n",
" <td>1.785600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>88</td>\n",
" <td>1.668000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>89</td>\n",
" <td>1.903100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>90</td>\n",
" <td>1.709800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>91</td>\n",
" <td>2.312900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>92</td>\n",
" <td>2.092100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>93</td>\n",
" <td>2.319600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>94</td>\n",
" <td>1.603100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>95</td>\n",
" <td>1.740000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>96</td>\n",
" <td>1.670500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>97</td>\n",
" <td>1.611600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>98</td>\n",
" <td>1.728900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>99</td>\n",
" <td>2.285200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>1.957800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>101</td>\n",
" <td>1.676700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>102</td>\n",
" <td>1.656300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>103</td>\n",
" <td>1.612400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>104</td>\n",
" <td>1.848900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>105</td>\n",
" <td>1.870000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>106</td>\n",
" <td>1.954000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>107</td>\n",
" <td>2.192200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>108</td>\n",
" <td>1.637600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>109</td>\n",
" <td>1.208700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>110</td>\n",
" <td>2.254200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>111</td>\n",
" <td>1.832100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>112</td>\n",
" <td>2.119600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>113</td>\n",
" <td>2.126400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>114</td>\n",
" <td>1.915700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>115</td>\n",
" <td>1.587500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>116</td>\n",
" <td>1.564800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>117</td>\n",
" <td>1.742700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>118</td>\n",
" <td>1.712600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>119</td>\n",
" <td>1.727900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>120</td>\n",
" <td>2.361500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>121</td>\n",
" <td>2.070300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>122</td>\n",
" <td>1.878500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>123</td>\n",
" <td>1.846600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>124</td>\n",
" <td>2.061700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>125</td>\n",
" <td>2.149700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>126</td>\n",
" <td>1.940600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>127</td>\n",
" <td>2.098300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>128</td>\n",
" <td>1.734100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>129</td>\n",
" <td>2.111700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>130</td>\n",
" <td>1.887600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>131</td>\n",
" <td>1.716300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>132</td>\n",
" <td>2.070000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>133</td>\n",
" <td>1.782200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>134</td>\n",
" <td>1.955200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>135</td>\n",
" <td>1.762900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>136</td>\n",
" <td>1.954700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>137</td>\n",
" <td>1.687100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>138</td>\n",
" <td>1.979100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>139</td>\n",
" <td>1.634600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>140</td>\n",
" <td>1.801200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>141</td>\n",
" <td>1.954100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>142</td>\n",
" <td>1.833900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>143</td>\n",
" <td>2.051400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>144</td>\n",
" <td>1.921200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>145</td>\n",
" <td>1.787500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>146</td>\n",
" <td>1.825400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>147</td>\n",
" <td>1.363400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>148</td>\n",
" <td>1.977400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>149</td>\n",
" <td>1.768300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>2.226700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>151</td>\n",
" <td>1.945500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import transformers\n",
"from datasets import load_dataset\n",
"\n",
"data = load_dataset(\"Abirate/english_quotes\")\n",
"data = data.map(lambda samples: tokenizer(samples[\"quote\"]), batched=True)\n",
"\n",
"trainer = transformers.Trainer(\n",
" model=model,\n",
" train_dataset=data[\"train\"],\n",
" args=transformers.TrainingArguments(\n",
" per_device_train_batch_size=4,\n",
" gradient_accumulation_steps=4,\n",
" warmup_steps=100,\n",
" max_steps=200,\n",
" learning_rate=2e-4,\n",
" fp16=True,\n",
" logging_steps=1,\n",
" output_dir=\"outputs\",\n",
" ),\n",
" data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
")\n",
"model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Duak7T_B3VpJ"
},
"source": [
"## Share adapters on the 🤗 Hub"
]
},
{
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"execution_count": null,
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"colab": {
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},
"id": "DpYr24pR8T_0",
"outputId": "20186456-1bd4-4655-b2f2-8f24f9f37fcc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Token is valid.\n",
"Your token has been saved to /root/.cache/huggingface/token\n",
"Login successful\n"
]
}
],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 133,
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},
"id": "VxB6UV5XAvvP",
"outputId": "c3b0133b-f5b1-4283-8367-f06524bea46c"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Uploading the following files to ybelkada/opt-6.7b-lora: adapter_config.json,adapter_model.bin\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3dbe077ed0c34e4eb1628418138ccbc6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2693135b6954d35afb3120f3caf4000",
"version_major": 2,
"version_minor": 0
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"text/plain": [
"adapter_model.bin: 0%| | 0.00/33.6M [00:00<?, ?B/s]"
]
},
"metadata": {},
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},
{
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/ybelkada/opt-6.7b-lora/commit/6f240b184e666b54a51b3fe482e4711448e6c751', commit_message='Upload model', commit_description='', oid='6f240b184e666b54a51b3fe482e4711448e6c751', pr_url=None, pr_revision=None, pr_num=None)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.push_to_hub(\"ybelkada/opt-6.7b-lora\", use_auth_token=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S65GcxNGA9kz"
},
"source": [
"## Load adapters from the Hub\n",
"\n",
"You can also directly load adapters from the Hub using the commands below:"
]
},
{
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"execution_count": 10,
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"outputs": [
{
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"application/vnd.jupyter.widget-view+json": {
"model_id": "621d427f78fb458e8ae25262f2ab7ca8",
"version_major": 2,
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"Downloading (…)/adapter_config.json: 0%| | 0.00/332 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4a2107423a164efd89002e031126c8b5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"text/plain": [
"Downloading adapter_model.bin: 0%| | 0.00/33.6M [00:00<?, ?B/s]"
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},
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"source": [
"import torch\n",
"from peft import PeftModel, PeftConfig\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
"\n",
"peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" config.base_model_name_or_path, return_dict=True, quantization_config=BitsAndBytesConfig(load_in_8bit=True), device_map=\"auto\"\n",
")\n",
"tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
"\n",
"# Load the Lora model\n",
"model = PeftModel.from_pretrained(model, peft_model_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MHYljmTjj5wX"
},
"source": [
"## Inference\n",
"\n",
"You can then directly use the trained model or the model that you have loaded from the 🤗 Hub for inference as you would do it usually in `transformers`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"base_uri": "https://localhost:8080/"
},
"id": "MDqJWba-tpnv",
"outputId": "dd65644f-6921-46e1-920c-400bd7d888cf"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/marc/anaconda3/envs/accelerate/lib/python3.10/site-packages/transformers/generation/utils.py:1448: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
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"text": [
"\n",
"\n",
" Two things are infinite: the universe and human stupidity; and I'm not sure about the universe. -Albert Einstein\n",
"I'm not sure about the universe either.\n"
]
}
],
"source": [
"batch = tokenizer(\"Two things are infinite: \", return_tensors=\"pt\").to(model.device)\n",
"\n",
"device_type = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"with torch.amp.autocast(device_type=device_type):\n",
" output_tokens = model.generate(**batch, max_new_tokens=50)\n",
"\n",
"print(\"\\n\\n\", tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HZL8ZbcJCHoy"
},
"source": [
"As you can see by fine-tuning for few steps we have almost recovered the quote from Albert Einstein that is present in the [training data](https://huggingface.co/datasets/Abirate/english_quotes)."
]
}
],
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