ParitKansal
commited on
Upload notebook.ipynb
Browse filesNotebook of how model is trained.
- notebook.ipynb +2064 -0
notebook.ipynb
ADDED
@@ -0,0 +1,2064 @@
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" Found existing installation: fsspec 2024.10.0\n",
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"\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",
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]
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],
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"source": [
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"# Install the requirements in Google Colab\n",
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"!pip install transformers datasets trl huggingface_hub"
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]
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},
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{
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"source": [
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"# Import necessary libraries\n",
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"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
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"from datasets import load_dataset\n",
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+
"from trl import SFTConfig, SFTTrainer, setup_chat_format, DataCollatorForCompletionOnlyLM\n",
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"import torch"
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],
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"metadata": {
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"id": "7iQRJ-YHDCQu"
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"outputs": []
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"source": [
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"device = (\n",
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")"
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"metadata": {
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"id": "VYETUQkNDIPz"
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"outputs": []
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"source": [
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"# Load the model and tokenizer\n",
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"model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
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+
"model = AutoModelForCausalLM.from_pretrained(\n",
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+
" pretrained_model_name_or_path=model_name\n",
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").to(device)\n",
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"tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)"
|
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],
|
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"metadata": {
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"id": "olO-YsF-DMSh"
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},
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"execution_count": 37,
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"outputs": []
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{
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"cell_type": "code",
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"source": [
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+
"# Set up the chat format\n",
|
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+
"model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
|
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+
],
|
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+
"metadata": {
|
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+
"id": "L2H0JHzBDTpm"
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},
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"execution_count": 38,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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+
"# Set our name for the finetune to be saved &/ uploaded to\n",
|
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+
"finetune_name = \"SmolLM2-135M-SFT-smoltalk\"\n",
|
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+
"finetune_tags = [\"smol-course\",\"sft_finetuning\"]"
|
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+
],
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"metadata": {
|
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"id": "YBiQ7YZPDhKe"
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},
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"execution_count": 39,
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"outputs": []
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{
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"cell_type": "code",
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"source": [
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+
"# Let's test the base model before training\n",
|
1564 |
+
"prompt = \"Write a haiku about programming\"\n",
|
1565 |
+
"\n",
|
1566 |
+
"# Format with template\n",
|
1567 |
+
"messages = [{\"role\": \"user\", \"content\": prompt}]\n",
|
1568 |
+
"formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
|
1569 |
+
"\n",
|
1570 |
+
"# Generate response\n",
|
1571 |
+
"inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
|
1572 |
+
"outputs = model.generate(**inputs, max_new_tokens=100)\n",
|
1573 |
+
"print(\"Before training:\")\n",
|
1574 |
+
"print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
|
1575 |
+
],
|
1576 |
+
"metadata": {
|
1577 |
+
"colab": {
|
1578 |
+
"base_uri": "https://localhost:8080/"
|
1579 |
+
},
|
1580 |
+
"id": "av7kKBd0DhkZ",
|
1581 |
+
"outputId": "27d49f1f-6f0b-4e77-d276-b5b482b8bfff"
|
1582 |
+
},
|
1583 |
+
"execution_count": 40,
|
1584 |
+
"outputs": [
|
1585 |
+
{
|
1586 |
+
"output_type": "stream",
|
1587 |
+
"name": "stdout",
|
1588 |
+
"text": [
|
1589 |
+
"Before training:\n",
|
1590 |
+
"user\n",
|
1591 |
+
"Write a haiku about programming\n",
|
1592 |
+
"Write a haiku about programming\n",
|
1593 |
+
"Write a haiku about programming\n",
|
1594 |
+
"Write a haiku about programming\n",
|
1595 |
+
"Write a haiku about programming\n",
|
1596 |
+
"Write a haiku about programming\n",
|
1597 |
+
"Write a haiku about programming\n",
|
1598 |
+
"Write a haiku about programming\n",
|
1599 |
+
"Write a haiku about programming\n",
|
1600 |
+
"Write a haiku about programming\n",
|
1601 |
+
"Write a haiku about programming\n",
|
1602 |
+
"Write a haiku about programming\n",
|
1603 |
+
"Write a haiku about programming\n",
|
1604 |
+
"Write a haiku about programming\n",
|
1605 |
+
"Write a haiku about programming\n",
|
1606 |
+
"Write a\n"
|
1607 |
+
]
|
1608 |
+
}
|
1609 |
+
]
|
1610 |
+
},
|
1611 |
+
{
|
1612 |
+
"cell_type": "code",
|
1613 |
+
"source": [
|
1614 |
+
"# Load a sample dataset\n",
|
1615 |
+
"from datasets import load_dataset\n",
|
1616 |
+
"ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
|
1617 |
+
"ds"
|
1618 |
+
],
|
1619 |
+
"metadata": {
|
1620 |
+
"colab": {
|
1621 |
+
"base_uri": "https://localhost:8080/"
|
1622 |
+
},
|
1623 |
+
"id": "qyWuLJDjDmqK",
|
1624 |
+
"outputId": "0162aeae-94bf-4d73-ec2a-e53c35d96bb3"
|
1625 |
+
},
|
1626 |
+
"execution_count": 41,
|
1627 |
+
"outputs": [
|
1628 |
+
{
|
1629 |
+
"output_type": "execute_result",
|
1630 |
+
"data": {
|
1631 |
+
"text/plain": [
|
1632 |
+
"DatasetDict({\n",
|
1633 |
+
" train: Dataset({\n",
|
1634 |
+
" features: ['full_topic', 'messages'],\n",
|
1635 |
+
" num_rows: 2260\n",
|
1636 |
+
" })\n",
|
1637 |
+
" test: Dataset({\n",
|
1638 |
+
" features: ['full_topic', 'messages'],\n",
|
1639 |
+
" num_rows: 119\n",
|
1640 |
+
" })\n",
|
1641 |
+
"})"
|
1642 |
+
]
|
1643 |
+
},
|
1644 |
+
"metadata": {},
|
1645 |
+
"execution_count": 41
|
1646 |
+
}
|
1647 |
+
]
|
1648 |
+
},
|
1649 |
+
{
|
1650 |
+
"cell_type": "code",
|
1651 |
+
"source": [
|
1652 |
+
"ds['train'][0]"
|
1653 |
+
],
|
1654 |
+
"metadata": {
|
1655 |
+
"colab": {
|
1656 |
+
"base_uri": "https://localhost:8080/"
|
1657 |
+
},
|
1658 |
+
"id": "_6D93RBmDxbk",
|
1659 |
+
"outputId": "1540bcb9-8191-44cd-b2ee-51abb6d0807a"
|
1660 |
+
},
|
1661 |
+
"execution_count": 42,
|
1662 |
+
"outputs": [
|
1663 |
+
{
|
1664 |
+
"output_type": "execute_result",
|
1665 |
+
"data": {
|
1666 |
+
"text/plain": [
|
1667 |
+
"{'full_topic': 'Travel/Vacation destinations/Beach resorts',\n",
|
1668 |
+
" 'messages': [{'content': 'Hi there', 'role': 'user'},\n",
|
1669 |
+
" {'content': 'Hello! How can I help you today?', 'role': 'assistant'},\n",
|
1670 |
+
" {'content': \"I'm looking for a beach resort for my next vacation. Can you recommend some popular ones?\",\n",
|
1671 |
+
" 'role': 'user'},\n",
|
1672 |
+
" {'content': \"Some popular beach resorts include Maui in Hawaii, the Maldives, and the Bahamas. They're known for their beautiful beaches and crystal-clear waters.\",\n",
|
1673 |
+
" 'role': 'assistant'},\n",
|
1674 |
+
" {'content': 'That sounds great. Are there any resorts in the Caribbean that are good for families?',\n",
|
1675 |
+
" 'role': 'user'},\n",
|
1676 |
+
" {'content': 'Yes, the Turks and Caicos Islands and Barbados are excellent choices for family-friendly resorts in the Caribbean. They offer a range of activities and amenities suitable for all ages.',\n",
|
1677 |
+
" 'role': 'assistant'},\n",
|
1678 |
+
" {'content': \"Okay, I'll look into those. Thanks for the recommendations!\",\n",
|
1679 |
+
" 'role': 'user'},\n",
|
1680 |
+
" {'content': \"You're welcome. I hope you find the perfect resort for your vacation.\",\n",
|
1681 |
+
" 'role': 'assistant'}]}"
|
1682 |
+
]
|
1683 |
+
},
|
1684 |
+
"metadata": {},
|
1685 |
+
"execution_count": 42
|
1686 |
+
}
|
1687 |
+
]
|
1688 |
+
},
|
1689 |
+
{
|
1690 |
+
"cell_type": "code",
|
1691 |
+
"source": [
|
1692 |
+
"def process_messages(samples):\n",
|
1693 |
+
" # Add 'human' role logic\n",
|
1694 |
+
" result = []\n",
|
1695 |
+
" for x in samples['messages']:\n",
|
1696 |
+
" if x[-1]['role'] == 'user': # Add condition for 'human' role\n",
|
1697 |
+
" result.append(x)\n",
|
1698 |
+
" else:\n",
|
1699 |
+
" result.append(x[:-1]) # Truncate the message if condition is not met\n",
|
1700 |
+
" return {'messages': result}\n",
|
1701 |
+
"\n",
|
1702 |
+
"# Applying the function on a dataset\n",
|
1703 |
+
"dataset = ds.map(process_messages, batched=True)"
|
1704 |
+
],
|
1705 |
+
"metadata": {
|
1706 |
+
"id": "cSYoD4Y3FQdu"
|
1707 |
+
},
|
1708 |
+
"execution_count": 43,
|
1709 |
+
"outputs": []
|
1710 |
+
},
|
1711 |
+
{
|
1712 |
+
"cell_type": "code",
|
1713 |
+
"source": [
|
1714 |
+
"# Configure the SFTTrainer\n",
|
1715 |
+
"sft_config = SFTConfig(\n",
|
1716 |
+
" output_dir=\"./sft_output\",\n",
|
1717 |
+
" max_steps=500, # Adjust based on dataset size and desired training duration\n",
|
1718 |
+
" per_device_train_batch_size=16, # Set according to your GPU memory capacity\n",
|
1719 |
+
" learning_rate=5e-5, # Common starting point for fine-tuning\n",
|
1720 |
+
" logging_steps=50, # Frequency for finding training metrics\n",
|
1721 |
+
" save_steps=50, # Frequency for saving model checkpoints\n",
|
1722 |
+
" eval_strategy=\"steps\", # Evaluate the model at regular intervals\n",
|
1723 |
+
" eval_steps=50, # Frequency of evaluation\n",
|
1724 |
+
" use_mps_device=(\n",
|
1725 |
+
" True if device == \"mps\" else False\n",
|
1726 |
+
" ), # Use MPS for mixed precision training\n",
|
1727 |
+
" hub_model_id=finetune_name, # Set a unique name for your model\n",
|
1728 |
+
" report_to=[]\n",
|
1729 |
+
")\n",
|
1730 |
+
"\n",
|
1731 |
+
"# Initialize the SFTTrainer\n",
|
1732 |
+
"trainer = SFTTrainer(\n",
|
1733 |
+
" model=model,\n",
|
1734 |
+
" args=sft_config,\n",
|
1735 |
+
" train_dataset=ds[\"train\"],\n",
|
1736 |
+
" processing_class=tokenizer,\n",
|
1737 |
+
" eval_dataset=ds[\"test\"],\n",
|
1738 |
+
")"
|
1739 |
+
],
|
1740 |
+
"metadata": {
|
1741 |
+
"colab": {
|
1742 |
+
"base_uri": "https://localhost:8080/",
|
1743 |
+
"height": 49,
|
1744 |
+
"referenced_widgets": [
|
1745 |
+
"5479c909ae014cb4af686f98dfd896cb",
|
1746 |
+
"77cd1a0126ea48f6bc358c52b5b22f3e",
|
1747 |
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"452ca86ad5f44319a93031962f10fedf",
|
1748 |
+
"fa32534d4cf349d2a1efac6496277d2e",
|
1749 |
+
"cb16cc0f8c6847cda083dc0b2f186083",
|
1750 |
+
"3b59793ca26745ea8b3780e07447a56c",
|
1751 |
+
"bf13f411d2db420fb25d13d212ba257a",
|
1752 |
+
"075f0860a2ca452c8b1438aeae8e3d18",
|
1753 |
+
"555d876e65c9419b82633841914086d9",
|
1754 |
+
"e07fb7d992e04473ad7bac9248e7aa75",
|
1755 |
+
"4cf7dbf2cb6d4581a356970de1996ec6"
|
1756 |
+
]
|
1757 |
+
},
|
1758 |
+
"id": "nvtJ2H41JTrU",
|
1759 |
+
"outputId": "0a9eaf66-f20d-4a44-d54f-6f6428cab4f1"
|
1760 |
+
},
|
1761 |
+
"execution_count": 44,
|
1762 |
+
"outputs": [
|
1763 |
+
{
|
1764 |
+
"output_type": "display_data",
|
1765 |
+
"data": {
|
1766 |
+
"text/plain": [
|
1767 |
+
"Map: 0%| | 0/119 [00:00<?, ? examples/s]"
|
1768 |
+
],
|
1769 |
+
"application/vnd.jupyter.widget-view+json": {
|
1770 |
+
"version_major": 2,
|
1771 |
+
"version_minor": 0,
|
1772 |
+
"model_id": "5479c909ae014cb4af686f98dfd896cb"
|
1773 |
+
}
|
1774 |
+
},
|
1775 |
+
"metadata": {}
|
1776 |
+
}
|
1777 |
+
]
|
1778 |
+
},
|
1779 |
+
{
|
1780 |
+
"cell_type": "code",
|
1781 |
+
"source": [
|
1782 |
+
"# Train the model\n",
|
1783 |
+
"trainer.train()"
|
1784 |
+
],
|
1785 |
+
"metadata": {
|
1786 |
+
"colab": {
|
1787 |
+
"base_uri": "https://localhost:8080/",
|
1788 |
+
"height": 441
|
1789 |
+
},
|
1790 |
+
"id": "qF2OxgBoJXKO",
|
1791 |
+
"outputId": "5a505e90-7213-4a80-a0b9-54338b83eadf"
|
1792 |
+
},
|
1793 |
+
"execution_count": 45,
|
1794 |
+
"outputs": [
|
1795 |
+
{
|
1796 |
+
"output_type": "display_data",
|
1797 |
+
"data": {
|
1798 |
+
"text/plain": [
|
1799 |
+
"<IPython.core.display.HTML object>"
|
1800 |
+
],
|
1801 |
+
"text/html": [
|
1802 |
+
"\n",
|
1803 |
+
" <div>\n",
|
1804 |
+
" \n",
|
1805 |
+
" <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
1806 |
+
" [500/500 17:08, Epoch 3/4]\n",
|
1807 |
+
" </div>\n",
|
1808 |
+
" <table border=\"1\" class=\"dataframe\">\n",
|
1809 |
+
" <thead>\n",
|
1810 |
+
" <tr style=\"text-align: left;\">\n",
|
1811 |
+
" <th>Step</th>\n",
|
1812 |
+
" <th>Training Loss</th>\n",
|
1813 |
+
" <th>Validation Loss</th>\n",
|
1814 |
+
" </tr>\n",
|
1815 |
+
" </thead>\n",
|
1816 |
+
" <tbody>\n",
|
1817 |
+
" <tr>\n",
|
1818 |
+
" <td>50</td>\n",
|
1819 |
+
" <td>1.250500</td>\n",
|
1820 |
+
" <td>1.109389</td>\n",
|
1821 |
+
" </tr>\n",
|
1822 |
+
" <tr>\n",
|
1823 |
+
" <td>100</td>\n",
|
1824 |
+
" <td>1.065500</td>\n",
|
1825 |
+
" <td>1.067429</td>\n",
|
1826 |
+
" </tr>\n",
|
1827 |
+
" <tr>\n",
|
1828 |
+
" <td>150</td>\n",
|
1829 |
+
" <td>1.015600</td>\n",
|
1830 |
+
" <td>1.044449</td>\n",
|
1831 |
+
" </tr>\n",
|
1832 |
+
" <tr>\n",
|
1833 |
+
" <td>200</td>\n",
|
1834 |
+
" <td>0.895800</td>\n",
|
1835 |
+
" <td>1.034744</td>\n",
|
1836 |
+
" </tr>\n",
|
1837 |
+
" <tr>\n",
|
1838 |
+
" <td>250</td>\n",
|
1839 |
+
" <td>0.881400</td>\n",
|
1840 |
+
" <td>1.030149</td>\n",
|
1841 |
+
" </tr>\n",
|
1842 |
+
" <tr>\n",
|
1843 |
+
" <td>300</td>\n",
|
1844 |
+
" <td>0.862100</td>\n",
|
1845 |
+
" <td>1.029914</td>\n",
|
1846 |
+
" </tr>\n",
|
1847 |
+
" <tr>\n",
|
1848 |
+
" <td>350</td>\n",
|
1849 |
+
" <td>0.788500</td>\n",
|
1850 |
+
" <td>1.028845</td>\n",
|
1851 |
+
" </tr>\n",
|
1852 |
+
" <tr>\n",
|
1853 |
+
" <td>400</td>\n",
|
1854 |
+
" <td>0.789500</td>\n",
|
1855 |
+
" <td>1.027438</td>\n",
|
1856 |
+
" </tr>\n",
|
1857 |
+
" <tr>\n",
|
1858 |
+
" <td>450</td>\n",
|
1859 |
+
" <td>0.767000</td>\n",
|
1860 |
+
" <td>1.030825</td>\n",
|
1861 |
+
" </tr>\n",
|
1862 |
+
" <tr>\n",
|
1863 |
+
" <td>500</td>\n",
|
1864 |
+
" <td>0.741700</td>\n",
|
1865 |
+
" <td>1.031717</td>\n",
|
1866 |
+
" </tr>\n",
|
1867 |
+
" </tbody>\n",
|
1868 |
+
"</table><p>"
|
1869 |
+
]
|
1870 |
+
},
|
1871 |
+
"metadata": {}
|
1872 |
+
},
|
1873 |
+
{
|
1874 |
+
"output_type": "execute_result",
|
1875 |
+
"data": {
|
1876 |
+
"text/plain": [
|
1877 |
+
"TrainOutput(global_step=500, training_loss=0.9057471542358398, metrics={'train_runtime': 1030.1888, 'train_samples_per_second': 7.766, 'train_steps_per_second': 0.485, 'total_flos': 1302438402256896.0, 'train_loss': 0.9057471542358398, 'epoch': 3.52112676056338})"
|
1878 |
+
]
|
1879 |
+
},
|
1880 |
+
"metadata": {},
|
1881 |
+
"execution_count": 45
|
1882 |
+
}
|
1883 |
+
]
|
1884 |
+
},
|
1885 |
+
{
|
1886 |
+
"cell_type": "code",
|
1887 |
+
"source": [
|
1888 |
+
"# Save the model\n",
|
1889 |
+
"trainer.save_model(f\"./{finetune_name}\")"
|
1890 |
+
],
|
1891 |
+
"metadata": {
|
1892 |
+
"id": "NJAdU1QBJfXK"
|
1893 |
+
},
|
1894 |
+
"execution_count": 46,
|
1895 |
+
"outputs": []
|
1896 |
+
},
|
1897 |
+
{
|
1898 |
+
"cell_type": "code",
|
1899 |
+
"source": [
|
1900 |
+
"trainer.push_to_hub(tags=finetune_tags)"
|
1901 |
+
],
|
1902 |
+
"metadata": {
|
1903 |
+
"colab": {
|
1904 |
+
"base_uri": "https://localhost:8080/",
|
1905 |
+
"height": 200,
|
1906 |
+
"referenced_widgets": [
|
1907 |
+
"f13451f32d11428c97243b2bc5b5268c",
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"02da0b8c8e564c05a91e5dd3b7e1c9cb",
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"43e70aa073504a4496e543fd923b7b0f",
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"46012658060c41e2a2af258e4025499e",
|
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|
1915 |
+
"c4af106fdac144d3b3010ceffc63b4d7",
|
1916 |
+
"b9b5a37d22644cacbc3e81a1682c5184",
|
1917 |
+
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|
1918 |
+
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|
1919 |
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|
1920 |
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|
1921 |
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"CommitInfo(commit_url='https://huggingface.co/ParitKansal/SmolLM2-135M-SFT-smoltalk/commit/97e8fed11e0a365f181dc40fc9b8ab4a87a98e99', commit_message='End of training', commit_description='', oid='97e8fed11e0a365f181dc40fc9b8ab4a87a98e99', pr_url=None, repo_url=RepoUrl('https://huggingface.co/ParitKansal/SmolLM2-135M-SFT-smoltalk', endpoint='https://huggingface.co', repo_type='model', repo_id='ParitKansal/SmolLM2-135M-SFT-smoltalk'), pr_revision=None, pr_num=None)"
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"source": [
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"# Test the fine-tuned model on the same prompt\n",
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"\n",
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"# Let's test the base model before training\n",
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"prompt = \"Write about a programming lang\"\n",
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"\n",
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"# Format with template\n",
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"messages = [{\"role\": \"user\", \"content\": prompt}]\n",
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"formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
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"\n",
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2017 |
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"inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
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"\n",
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2019 |
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"# TODO: use the fine-tuned to model generate a response, just like with the base example.\n",
|
2020 |
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"outputs = model.generate(**inputs, max_new_tokens=100)\n",
|
2021 |
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"print(\"After training:\")\n",
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2022 |
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"print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
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],
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"After training:\n",
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"user\n",
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"Write about a programming lang\n",
|
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"\n",
|
2041 |
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"What is a programming language?\n",
|
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"\n",
|
2043 |
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"A programming language is a set of instructions that a computer can understand and execute. It is a set of rules that tells the computer what to do. It is a language that is easy to learn and use.\n",
|
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+
"\n",
|
2045 |
+
"What is a programming language used for?\n",
|
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"\n",
|
2047 |
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"A programming language is used to create software programs. It is a language that is used to create computer programs. It is a language that is easy to learn and use.\n",
|
2048 |
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"\n",
|
2049 |
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"What\n"
|
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]
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}
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]
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},
|
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2058 |
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"id": "DEBtbcL_Vc88"
|
2059 |
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},
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2060 |
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"execution_count": null,
|
2061 |
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"outputs": []
|
2062 |
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}
|
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]
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2064 |
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}
|