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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+medquad.csv filter=lfs diff=lfs merge=lfs -text

Bio-Medical-Llama-3-8B-finetuned/model/README.md

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+---
+base_model: ContactDoctor/Bio-Medical-Llama-3-8B
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.13.2

Bio-Medical-Llama-3-8B-finetuned/model/adapter_config.json

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+  "task_type": "CAUSAL_LM",
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+  "use_rslora": false
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+  "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ message['content'] }}{% elif message['role'] == 'user' %}{{ '\n\nHuman: ' + message['content'] +  eos_token }}{% elif message['role'] == 'assistant' %}{{ '\n\nAssistant: '  + message['content'] +  eos_token  }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '\n\nAssistant: ' }}{% endif %}",
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+}

Model_Training.ipynb

@@ -0,0 +1,1829 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "collapsed": true,
+    "id": "pMAzkFCvb8Sn",
+    "outputId": "7fe8df0f-a511-4c67-f62c-afac548031d4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting datasets\n",
+      "  Downloading datasets-3.1.0-py3-none-any.whl.metadata (20 kB)\n",
+      "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
+      "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.34.2)\n",
+      "Collecting bitsandbytes\n",
+      "  Downloading bitsandbytes-0.44.1-py3-none-manylinux_2_24_x86_64.whl.metadata (3.5 kB)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
+      "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n",
+      "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
+      "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
+      "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n",
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+      "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, bitsandbytes, datasets\n",
+      "  Attempting uninstall: fsspec\n",
+      "    Found existing installation: fsspec 2024.10.0\n",
+      "    Uninstalling fsspec-2024.10.0:\n",
+      "      Successfully uninstalled fsspec-2024.10.0\n",
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0mSuccessfully installed bitsandbytes-0.44.1 datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install datasets transformers datasets accelerate bitsandbytes peft\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 84,
+     "referenced_widgets": [
+      "8b349cff427040fc8d00bf53f08e1f9f",
+      "fccfe3a8357a4122bbe8ee85c20af176",
+      "7aa4769c30034c73b98c50bb249ea6e9",
+      "ad43b705a7f04886947691385afa02c8",
+      "a422c9520dd841988a36736a510d62c3",
+      "57648fb4258743948c3007c6603d23e8",
+      "689b0aa3e69d4f75af7a62fae057158d",
+      "ebb2b14e2f8748d495012420720c85db",
+      "04a82101b6704de193d126f740f8ba0a",
+      "859a4a66b43f48a9934e836f1a95d9e1",
+      "53de1a03e8bf4cf7992a878193fa9f22"
+     ]
+    },
+    "id": "Z2reH2Q51KGv",
+    "outputId": "6ea5de08-09cb-4744-f116-38ad924b4acc"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b349cff427040fc8d00bf53f08e1f9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trainable params: 4,718,592 || all params: 8,034,979,840 || trainable%: 0.0587\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "from datasets import Dataset\n",
+    "from transformers import TrainingArguments, Trainer\n",
+    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
+    "from peft import LoraConfig, get_peft_model\n",
+    "\n",
+    "# Load tokenizer and model with quantization for reduced memory usage\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"ContactDoctor/Bio-Medical-Llama-3-8B\")\n",
+    "quantization_config = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,  # Enable 4-bit quantization\n",
+    "    bnb_4bit_compute_dtype=torch.bfloat16, # Use bfloat16 for computation\n",
+    "    bnb_4bit_quant_type=\"nf4\", # Quantization type\n",
+    "    bnb_4bit_use_double_quant=True, # Double quantization\n",
+    ")\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    \"ContactDoctor/Bio-Medical-Llama-3-8B\",\n",
+    "    quantization_config=quantization_config\n",
+    ")\n",
+    "\n",
+    "# **PEFT Configuration:**\n",
+    "# Define the LoRA configuration for the adapter\n",
+    "# Changed target_modules to 'q_proj', 'k_proj', 'v_proj' based on the model architecture\n",
+    "peft_config = LoraConfig(\n",
+    "    r=8,  # Rank of the LoRA update matrices\n",
+    "    lora_alpha=32,  # Scaling factor for the LoRA update matrices\n",
+    "    lora_dropout=0.1,  # Dropout probability for the LoRA layers\n",
+    "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\"], # Specify the target modules for applying LoRA\n",
+    "    bias=\"none\",  # Bias type for the LoRA layers\n",
+    "    task_type=\"CAUSAL_LM\", # Task type for fine-tuning\n",
+    ")\n",
+    "\n",
+    "# Apply LoRA to the model\n",
+    "model = get_peft_model(model, peft_config)\n",
+    "model.print_trainable_parameters() # Print the number of trainable parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 740,
+     "referenced_widgets": [
+      "17af2a5ad09b487dae0a27614cb8c9d4",
+      "4d80a7fbfb1c4ff699732b501ae4fa11",
+      "07883ddd58e74c028db1f071f6b79523",
+      "c4795ad740504fc5bc1c88771fe1f5c1",
+      "c2a37c7e05f54be2bff2fc96e6fd9ab7",
+      "7fc70f038a474b79af71c55beff060bb",
+      "a618ba7eedec463da536b5f738200f56",
+      "c48725720df943ff8c36567ba6e81fab",
+      "59ec7c53d1254f94a1a5b9a14259abdb",
+      "9ba1376a86fc41b3a4093f547abd0a79",
+      "2ae0862b14214f2b8c72cc08e277a440",
+      "31dc3fceda5c44e688d5695b8f4d4d20",
+      "be62a2e692ce41f4a5e28e5203ee8c0f",
+      "90428fe3f2ec477aaade1bce4dc22ff2",
+      "4c8fb76782c847278e0699a2c22b40ea",
+      "7f080d2f8e134e42919e7ebf4e9fb1be",
+      "4f4acea059184f808a75d1a26d23f044",
+      "6aa71ee2be094c85982a13819ef36e58",
+      "2a5bd0453dc6482ba7371dcf91fae280",
+      "7fa36aba3a2b4979b8baedaae7fa9981",
+      "5280b192d5a049d2805ae23f94405c45",
+      "9afc09fd6e7a496eb1b6d2e47a097205",
+      "a3921ef31e69468a8dceda7339df2b54",
+      "be6f516e9eb84af684d95bc7dd21f49c",
+      "25ab1d56afbe40f9b94e85bbff75e542",
+      "1ae32c3063bd495eb1ec65876ba0899e",
+      "94a3128d5b73472481881c18f1d255ad",
+      "728f083f74a4430e8120958ffc004a34",
+      "7410cb000d8c466cb8433b59aa52237b",
+      "660401973ec5496e9fbbfcb76fae6ee4",
+      "dfd12e73e36242f8a42e61fe2d9fa1d8",
+      "de0d60d8d3c84ceda7be69570cf405ce",
+      "6cb9d841eac44ac2a2f7a44a105bf8c7"
+     ]
+    },
+    "id": "qKzPvZYD1yNS",
+    "outputId": "5b5866c7-32d8-4494-b0d2-acd78a4db12d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17af2a5ad09b487dae0a27614cb8c9d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/16412 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31dc3fceda5c44e688d5695b8f4d4d20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/16412 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3921ef31e69468a8dceda7339df2b54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/16412 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:494: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
+      "  self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n",
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='1025' max='1025' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [1025/1025 43:06, 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>500</td>\n",
+       "      <td>0.631100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1000</td>\n",
+       "      <td>0.539600</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
+      "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n",
+      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=1025, training_loss=0.583683032524295, metrics={'train_runtime': 2589.4824, 'train_samples_per_second': 6.338, 'train_steps_per_second': 0.396, 'total_flos': 3.783418353549312e+17, 'train_loss': 0.583683032524295, 'epoch': 0.9992688276870583})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"/content/medquad.csv\")\n",
+    "dataset = Dataset.from_pandas(df)\n",
+    "\n",
+    "def preprocess_function(examples):\n",
+    "    question = examples[\"question\"] if examples[\"question\"] is not None else \"\"\n",
+    "    answer = examples[\"answer\"] if examples[\"answer\"] is not None else \"\"\n",
+    "\n",
+    "    examples[\"text\"] = \"<s> \" + question + \" </s> \" + answer + \" </s>\"\n",
+    "    return examples\n",
+    "\n",
+    "dataset = dataset.map(preprocess_function)\n",
+    "def tokenize_function(examples):\n",
+    "    return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n",
+    "\n",
+    "tokenized_datasets = dataset.map(tokenize_function, batched=True)\n",
+    "\n",
+    "tokenized_datasets = tokenized_datasets.map(lambda examples: {'labels': examples['input_ids']}, batched=True)\n",
+    "\n",
+    "# Training arguments\n",
+    "training_args = TrainingArguments(\n",
+    "    output_dir=\"./results\",\n",
+    "    per_device_train_batch_size=4,  # Reduced batch size from 16 to 4\n",
+    "    gradient_accumulation_steps=4,  # Increased gradient accumulation steps from 2 to 4\n",
+    "    num_train_epochs=1,\n",
+    "    fp16=True,\n",
+    "    logging_dir=\"./logs\",\n",
+    "    learning_rate=2e-5,\n",
+    "    save_steps=100,\n",
+    "    dataloader_num_workers=8, # Utilize more CPU cores for data loading\n",
+    "    remove_unused_columns=True, # Remove unused columns from the dataset\n",
+    ")\n",
+    "\n",
+    "# Trainer initialization\n",
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=tokenized_datasets,\n",
+    ")\n",
+    "\n",
+    "# Start training\n",
+    "trainer.train()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "2N_8NspTsmxA",
+    "outputId": "82b87812-5bbc-4653-949a-f4fb2d65793d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('/content/drive/MyDrive/Bio-Medical-Llama-3-8B-finetuned/tokenizer/tokenizer_config.json',\n",
+       " '/content/drive/MyDrive/Bio-Medical-Llama-3-8B-finetuned/tokenizer/special_tokens_map.json',\n",
+       " '/content/drive/MyDrive/Bio-Medical-Llama-3-8B-finetuned/tokenizer/tokenizer.json')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "\n",
+    "model_folder = \"/content/drive/MyDrive/Bio-Medical-Llama-3-8B-finetuned\"\n",
+    "\n",
+    "\n",
+    "os.makedirs(model_folder, exist_ok=True)\n",
+    "\n",
+    "\n",
+    "trainer.save_model(os.path.join(model_folder, \"model\"))\n",
+    "tokenizer.save_pretrained(os.path.join(model_folder, \"tokenizer\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
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