fine-tune-notebooks/1_finetuning_with_explained_data_using_unsloth.ipynb

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    "%%capture\n",
    "!pip install unsloth\n",
    "# Also get the latest nightly Unsloth!\n",
    "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
   ]
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     "text": [
      "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "==((====))==  Unsloth 2024.11.7: Fast Llama patching. Transformers = 4.46.2.\n",
      "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
      "O^O/ \\_/ \\    Pytorch: 2.5.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
      "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
      " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
     ]
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       "model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"
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       "generation_config.json:   0%|          | 0.00/230 [00:00<?, ?B/s]"
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       "tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"
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       "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
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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",
    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_name = \"unsloth/Meta-Llama-3.1-8B\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    dtype = dtype,\n",
    "    load_in_4bit = load_in_4bit,\n",
    "    # token = \"hf_...\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SXd9bTZd1aaL"
   },
   "source": [
    "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6bZsfBuZDeCL",
    "outputId": "7b7b20ae-c077-4b46-acbc-eb9a8a3e5089"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unsloth 2024.11.7 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",
    "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
    "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
    "    random_state = 3407,\n",
    "    use_rslora = False,  # We support rank stabilized LoRA\n",
    "    loftq_config = None, # And LoftQ\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vITh0KVJ10qX"
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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 `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?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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      },
      "text/plain": [
       "(…)tExplainedByGemini Dataset - Chunk 1.csv:   0%|          | 0.00/21.6M [00:00<?, ?B/s]"
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      "text/plain": [
       "Generating train split:   0%|          | 0/861 [00:00<?, ? examples/s]"
      ]
     },
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    {
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      "text/plain": [
       "Map:   0%|          | 0/861 [00:00<?, ? examples/s]"
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   "source": [
    "alpaca_prompt = \"\"\"You are a test smell detector. You will be given a test code, you have to identify which test smells are present there. Then you need to provide explanation of why you marked the test smells\n",
    "\n",
    "### Input:\n",
    "find test smells in the following java code\n",
    "{}\n",
    "\n",
    "### Response:\n",
    "The present test smells are: {}\n",
    "\n",
    "### Explanation\n",
    "{}\n",
    "\"\"\"\n",
    "\n",
    "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
    "def formatting_prompts_func(examples):\n",
    "    test_codes = examples[\"Test_File_Content\"]\n",
    "    present_test_smells       = examples[\"Present_Test_Smells\"]\n",
    "    explanations      = examples[\"Explanation\"]\n",
    "    texts = []\n",
    "    for test_code, present_test_smell, explanations in zip(test_codes, present_test_smells, explanations):\n",
    "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
    "        text = alpaca_prompt.format(test_code, present_test_smell, explanations) + EOS_TOKEN\n",
    "        texts.append(text)\n",
    "    return { \"text\" : texts, }\n",
    "pass\n",
    "\n",
    "from datasets import load_dataset\n",
    "dataset = load_dataset(\"shawon-majid/ts-detect-test-smell-gemini-explained\", split = \"train\")\n",
    "dataset = dataset.map(formatting_prompts_func, batched = True,)"
   ]
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   "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`!"
   ]
  },
  {
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    "outputId": "d20f180f-9340-4fde-8ef6-4428d49ad8a4"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7ea60e9d54a4dd1927d618e45976cf3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map (num_proc=2):   0%|          | 0/861 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from trl import SFTTrainer\n",
    "from transformers import TrainingArguments\n",
    "from unsloth import is_bfloat16_supported\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",
    "        num_train_epochs = 1, # Set this for 1 full training run.\n",
    "        # max_steps = 60,\n",
    "        learning_rate = 2e-4,\n",
    "        fp16 = not is_bfloat16_supported(),\n",
    "        bf16 = is_bfloat16_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",
    "        report_to = \"none\", # Use this for WandB etc\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ejIt2xSNKKp",
    "outputId": "456e3b7d-6e30-462a-b2ec-320babdcf73a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU = Tesla T4. Max memory = 14.748 GB.\n",
      "5.984 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": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yqxqAZ7KJ4oL",
    "outputId": "f8ac6393-e30c-4620-99dc-ae8eac0625d2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
      "   \\\\   /|    Num examples = 861 | Num Epochs = 1\n",
      "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
      "\\        /    Total batch size = 8 | Total steps = 107\n",
      " \"-____-\"     Number of trainable parameters = 41,943,040\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='107' max='107' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [107/107 1:11:25, 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>0.796200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.826500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.067100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.813700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.827600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.978700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.910200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.726800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.793600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.702100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.706100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.788800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.675100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.639600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.704900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.717000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.657700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.542300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.633600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.611300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.637200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.757100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.598600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.561000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.518900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.550800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.520300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.482000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.412100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.542300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.571000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.473700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.624000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.649200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.510100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.635200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.630100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.667400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.594000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.445400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.535000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.484800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.587500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.499500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.476100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.492700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.604200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.558300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.514400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.566100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.720500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.388100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.523500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.465700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.434200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.556900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.426500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.506800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.616100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.447400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.459500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.686600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.425200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.429600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.532900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.526800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.489200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.299000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.456100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.342800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.434800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.442200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.631800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.492600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.382400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.493900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.520400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.387100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.381700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.439000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.343100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.513800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.464700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.356900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.472300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.498400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.323200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.452400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.515100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.462500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.288200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.493500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.335700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.572300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.319600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.382800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.421300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.405600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.368500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>0.292300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>0.634100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>0.499700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>0.363200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>0.462800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>0.412900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>0.363300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer_stats = trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pCqnaKmlO1U9"
   },
   "outputs": [],
   "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!\n",
    "\n",
    "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SncIembHG7_Y"
   },
   "outputs": [],
   "source": [
    "test_code = \"\"\"\n",
    "package com.querydsl.core.types.dsl;\n",
    "\n",
    "import static org.junit.Assert.assertEquals;\n",
    "\n",
    "import org.junit.Test;\n",
    "\n",
    "import com.querydsl.core.Tuple;\n",
    "import com.querydsl.core.types.Projections;\n",
    "import com.querydsl.core.types.QTuple;\n",
    "\n",
    "public class QTupleTest {\n",
    "\n",
    "    private StringPath first = new StringPath(\"x\");\n",
    "\n",
    "    private NumberPath<Integer> second = new NumberPath<Integer>(Integer.class,\"y\");\n",
    "\n",
    "    private BooleanPath third = new BooleanPath(\"z\");\n",
    "\n",
    "    private QTuple tupleExpression = Projections.tuple(first, second, third);\n",
    "\n",
    "    @Test\n",
    "    public void newInstanceObjectArray() {\n",
    "        Tuple tuple = tupleExpression.newInstance(\"1\", 42, true);\n",
    "        assertEquals(3, tuple.size());\n",
    "        assertEquals(\"1\", tuple.get(0, String.class));\n",
    "        assertEquals(Integer.valueOf(42), tuple.get(1, Integer.class));\n",
    "        assertEquals(Boolean.TRUE, tuple.get(2, Boolean.class));\n",
    "        assertEquals(\"1\", tuple.get(first));\n",
    "        assertEquals(Integer.valueOf(42), tuple.get(second));\n",
    "        assertEquals(Boolean.TRUE, tuple.get(third));\n",
    "\n",
    "    }\n",
    "\n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kR3gIAX-SM2q",
    "outputId": "29ddad8f-b4ea-4ecd-c629-de5cc1dca196"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<|begin_of_text|>You are a test smell detector. You will be given a test code, you have to identify which test smells are present there. Then you need to provide explanation of why you marked the test smells\\n\\n### Input:\\nfind test smells in the following java code\\nFind smells in this code \\npackage com.querydsl.core.types.dsl;\\n\\nimport static org.junit.Assert.assertEquals;\\n\\nimport org.junit.Test;\\n\\nimport com.querydsl.core.Tuple;\\nimport com.querydsl.core.types.Projections;\\nimport com.querydsl.core.types.QTuple;\\n\\npublic class QTupleTest {\\n\\n    private StringPath first = new StringPath(\"x\");\\n\\n    private NumberPath<Integer> second = new NumberPath<Integer>(Integer.class,\"y\");\\n\\n    private BooleanPath third = new BooleanPath(\"z\");\\n\\n    private QTuple tupleExpression = Projections.tuple(first, second, third);\\n\\n    @Test\\n    public void newInstanceObjectArray() {\\n        Tuple tuple = tupleExpression.newInstance(\"1\", 42, true);\\n        assertEquals(3, tuple.size());\\n        assertEquals(\"1\", tuple.get(0, String.class));\\n        assertEquals(Integer.valueOf(42), tuple.get(1, Integer.class));\\n        assertEquals(Boolean.TRUE, tuple.get(2, Boolean.class));\\n        assertEquals(\"1\", tuple.get(first));\\n        assertEquals(Integer.valueOf(42), tuple.get(second));\\n        assertEquals(Boolean.TRUE, tuple.get(third));\\n\\n    }\\n\\n}\\n\\n\\n### Response:\\nThe present test smells are: \\n\\n### Explanation\\n\\nThe test smells in the provided code stem from a lack of descriptive test methods and a reliance on the `assertEquals` method.  The test methods `newInstanceObjectArray` use the `assertEquals` method multiple times without explaining *what* is being asserted.  This makes it hard to understand *why* a test fails.  The tests also use the `tuple` object directly, rather than extracting specific values to assert, which makes it harder to understand the expected behavior.  The tests also don\\'t use assertions that would flag issues with null values or other unexpected conditions.  The tests are not focused on testing the *logic* of']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "        f\"Find smells in this code {test_code}\", # test code\n",
    "        \"\", # test smells\n",
    "        \"\", # explanation\n",
    "    )\n",
    "], return_tensors = \"pt\").to(\"cuda\")\n",
    "\n",
    "outputs = model.generate(**inputs, max_new_tokens = 128, 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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e2pEuRb1r2Vg",
    "outputId": "c38db48d-c62e-4ecc-c856-96a81893f283"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<|begin_of_text|>You are a test smell detector. You will be given a test code, you have to identify which test smells are present there. Then you need to provide explanation of why you marked the test smells\n",
      "\n",
      "### Input:\n",
      "find test smells in the following java code\n",
      "Find smells in this code \n",
      "package com.querydsl.core.types.dsl;\n",
      "\n",
      "import static org.junit.Assert.assertEquals;\n",
      "\n",
      "import org.junit.Test;\n",
      "\n",
      "import com.querydsl.core.Tuple;\n",
      "import com.querydsl.core.types.Projections;\n",
      "import com.querydsl.core.types.QTuple;\n",
      "\n",
      "public class QTupleTest {\n",
      "\n",
      "    private StringPath first = new StringPath(\"x\");\n",
      "\n",
      "    private NumberPath<Integer> second = new NumberPath<Integer>(Integer.class,\"y\");\n",
      "\n",
      "    private BooleanPath third = new BooleanPath(\"z\");\n",
      "\n",
      "    private QTuple tupleExpression = Projections.tuple(first, second, third);\n",
      "\n",
      "    @Test\n",
      "    public void newInstanceObjectArray() {\n",
      "        Tuple tuple = tupleExpression.newInstance(\"1\", 42, true);\n",
      "        assertEquals(3, tuple.size());\n",
      "        assertEquals(\"1\", tuple.get(0, String.class));\n",
      "        assertEquals(Integer.valueOf(42), tuple.get(1, Integer.class));\n",
      "        assertEquals(Boolean.TRUE, tuple.get(2, Boolean.class));\n",
      "        assertEquals(\"1\", tuple.get(first));\n",
      "        assertEquals(Integer.valueOf(42), tuple.get(second));\n",
      "        assertEquals(Boolean.TRUE, tuple.get(third));\n",
      "\n",
      "    }\n",
      "\n",
      "}\n",
      "\n",
      "\n",
      "### Response:\n",
      "The present test smells are: \n",
      "\n",
      "### Explanation\n",
      "\n",
      "The provided test code exhibits several code smells related to test design and organization.\n",
      "\n",
      "* **No test smells detected:**  The code is clean and does not contain any test smells.\n",
      "\n",
      "The test suite in the `QTupleTest` class is well-structured and follows best practices.  It uses the `newInstance` method of the `QTuple` class to create a tuple instance and then asserts that the tuple contains the expected values.  The test methods are clearly named and focused on verifying specific aspects of the `QTuple` functionality.  There are no redundant tests or unnecessary assertions.\n",
      "\n",
      "<|end_of_text|>\n"
     ]
    }
   ],
   "source": [
    "# alpaca_prompt = Copied from above\n",
    "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
    "inputs = tokenizer(\n",
    "[\n",
    "    alpaca_prompt.format(\n",
    "        f\"Find smells in this code {test_code}\", # test code\n",
    "        \"\", # test smells\n",
    "        \"\", # explanation\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 = 256)"
   ]
  },
  {
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   "metadata": {
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   "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!"
   ]
  },
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      "Saved model to https://huggingface.co/shawon-majid/llama-3.1-8b-finetuned-test-smell-detector\n"
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    "tokenizer.save_pretrained(\"llama-3.1-8b-finetuned-test-smell-detector\")\n",
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