Take a dataset from the Hugging Face hub and process it for SFT.
\n", - "Difficulty Levels
\n", - "🐢 Convert the `HuggingFaceTB/smoltalk` dataset into chatml format.
\n", - "🐕 Convert the `openai/gsm8k` dataset into chatml format.
\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 381 + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "tnHzBR7vtFlr" + }, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "from trl import setup_chat_format\n", + "import torch" + ] }, - "id": "qbkXV2_ItFlt", - "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f" - }, - "outputs": [ { - "data": { - "text/html": [ - "Take a dataset from the Hugging Face hub and process it for SFT.
\n", + "Difficulty Levels
\n", + "🐢 Convert the `HuggingFaceTB/smoltalk` dataset into chatml format.
\n", + "🐕 Convert the `openai/gsm8k` dataset into chatml format.
\n", + "Take a dataset from the Hugging Face hub and finetune a model on it.
\n", - "Difficulty Levels
\n", - "🐢 Use the `HuggingFaceTB/smoltalk` dataset
\n", - "🐕 Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.
\n", - "🦁 Select a dataset that relates to a real world use case your interested in
\n", - "Take a dataset from the Hugging Face hub and finetune a model on it.
\n", + "Difficulty Levels
\n", + "🐢 Use the `HuggingFaceTB/smoltalk` dataset
\n", + "🐕 Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.
\n", + "🦁 Select a dataset that relates to a real world use case your interested in
\n", + "/content/wandb/run-20241213_154654-n16pyg5n"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "| Step | \n", + "Training Loss | \n", + "Validation Loss | \n", + "
|---|---|---|
| 50 | \n", + "1.057300 | \n", + "1.151843 | \n", + "
| 100 | \n", + "1.109600 | \n", + "1.122580 | \n", + "
| 150 | \n", + "1.061700 | \n", + "1.095493 | \n", + "
| 200 | \n", + "1.046400 | \n", + "1.080045 | \n", + "
| 250 | \n", + "1.041300 | \n", + "1.070940 | \n", + "
| 300 | \n", + "1.030200 | \n", + "1.062110 | \n", + "
| 350 | \n", + "1.003000 | \n", + "1.055429 | \n", + "
| 400 | \n", + "1.007700 | \n", + "1.051079 | \n", + "
| 450 | \n", + "1.019900 | \n", + "1.043225 | \n", + "
| 500 | \n", + "1.076600 | \n", + "1.034016 | \n", + "
| 550 | \n", + "0.990400 | \n", + "1.028658 | \n", + "
| 600 | \n", + "0.803400 | \n", + "1.033651 | \n", + "
| 650 | \n", + "0.808500 | \n", + "1.030679 | \n", + "
| 700 | \n", + "0.755200 | \n", + "1.032045 | \n", + "
| 750 | \n", + "0.851500 | \n", + "1.029035 | \n", + "
| 800 | \n", + "0.797400 | \n", + "1.025460 | \n", + "
| 850 | \n", + "0.777700 | \n", + "1.026119 | \n", + "
| 900 | \n", + "0.826100 | \n", + "1.023559 | \n", + "
| 950 | \n", + "0.863700 | \n", + "1.022506 | \n", + "
| 1000 | \n", + "0.793300 | \n", + "1.022317 | \n", + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "# Train the model\n", + "trainer.train()\n", + "\n", + "# Save the model\n", + "trainer.save_model(f\"./{'chat_conv'}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 246, + "referenced_widgets": [ + "9f587f58d1854dc0b7c15b1d7f3c8a56", + "a2dd12c155c8419e8353653f5365ddc1", + "fa6ff968950d42d8866daa962db5b4b2", + "1fca17a36f0d4f46be7bf898321f37d9", + "d0d6bac398d3438f876e7fd8d2452832", + "df791ace9d1442819d0fadcffaf606d9", + "41dd72742a0d44a69cb3dba613be36fb", + "b18d1a0d32c8440196606929274f45c4", + "78abf442829046949805bdd8d81f3edc", + "f42aec05d8aa42ddb43c9b62d596c038", + "6fede47e17814b51b1ca536b01843d04", + "761dc3f3b2aa4bf68a8de90be6a5fd8b", + "8e06c9d28fc548259a1bf41a33dc917a", + "2ec7704ac56f41e2b426e1e0d146486b", + "74ec59b3ec5e4321a6973c3d99b6bbbe", + "65a0bba505954799898fff8970163314", + "1938d3e251eb482bad1b322f79f3155e", + "dd26f394661a440d8249f469ceb3fd96", + "bafef02cac5e4ca3a3b1deaddd273cae", + "a8ab96fc92a84c3c89c2c1b1691be35b", + "bd9b696f8b5f46bbabfbb0bd121c76c3", + "5eb81fb00ff14cb1bbe619dd5e0bd75e", + "f5b08567353947aa8c5abfb21ec9df4d", + "a8389edf8fd44dc99bba8cfa79cf2d0c", + "44b346489d794438afe5957deaed77d4", + "d222647fb0f247b08f484a8b16d6b716", + "24d0378dc16b4bebbfc8853417368a1c", + "785313e15aad4682bf3928c83f3f92c3", + "5946ff625560426c987e23c22c76f253", + "ba71bc4b55614aa6b12b4bfdbebf683c", + "9f6aefc1ac294dcdabee864d33e9a94f", + "b65a8ea788774a79acf87236dc2aec0f", + "5f797eba735944d68cbdaf45c42f6b1a", + "1120a5b318fd44909d8869102ad2ef91", + "803f1cbd8acf413db64e5452032f9986", + "0edf0b1edfe04432aacd48d4f5d8085d", + "98dbf655847a473ab2c2e618754018e5", + "fd13fce273a447d0a1dbcafaa14d1da0", + "2f0eaf808ebd4351a6d0a2ff69854417", + "acea77cbe8574a27819210fb4c8b9012", + "2a44485f1e7e4d1c94df1e8c9eb8585a", + "08d8056a7ad840e5b87442ec294b836d", + "7e70dbd54e1a436688d5795e0f13964a", + "f7b6b04cadf2453483a6d68208b43b8e", + "640d1ef9b3464046928285b1630f4dc4", + "8e390f59c2ec4187866cc1b8fa3fc9e6", + "84a505cde2a347c9aec9335c3c92d604", + "a7c0e76695d649e6982ac0a8c5c771ef", + "82b8bd9abdde4f3aba51543ba39c6cee", + "c349a5ddeb4f4614abb1cced3c7d4236", + "e0ab92c7bba0436daa34c26a92504004", + "b26d0555309a4c27ba51078c5b7c0090", + "9687a61ad81e40e9882ff9782585d218", + "aaa1ea8f8459444cb80d391e42acaf6e", + "26865e3d78a34ab28994165e083b267a" + ] + }, + "id": "KlwEI5LeP0VT", + "outputId": "64161dd5-02d8-4210-b8f7-afcdf25a1ece" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "events.out.tfevents.1734104607.34852365e3c0.526.0: 0%| | 0.00/11.2k [00:00\n", + "
🐕 Use the fine-tuned to model generate a response, just like with the base example..
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eCwbgjtfP0VT", + "outputId": "92095ad5-6f11-4cde-bfb6-87bffd4e15fc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "After training:\n", + "user\n", + "Write a haiku about programming\n", + "\n", + "What is a haiku?\n", + "\n", + "A haiku is a short, 5-7-5 syllable poem. It's a simple and beautiful way to express thoughts and feelings. It's often used in poetry, but you can also write them yourself.\n", + "\n", + "What is a haiku about?\n", + "\n", + "A haiku about programming is about programming. It's about the process of programming and how it affects our daily lives. We can think of programming as a way to create and organize\n" + ] + } + ], + "source": [ + "# Test the fine-tuned model on the same prompt\n", + "\n", + "# Let's test the base model before training\n", + "prompt = \"Write a haiku about programming\"\n", + "\n", + "# Format with template\n", + "messages = [{\"role\": \"user\", \"content\": prompt}]\n", + "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "# Generate response\n", + "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens=100)\n", + "print(\"After training:\")\n", + "print(tokenizer.decode(outputs[0], skip_special_tokens=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ORmys3UWP0VT" + }, + "source": [ + "## 💐 You're done!\n", + "\n", + "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n", + "\n", + "- Try this notebook on a harder difficulty\n", + "- Review a colleagues PR\n", + "- Improve the course material via an Issue or PR." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + }, + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "accelerator": "GPU", + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "be2a123c716a42f7a862c4a9fdd9fe59": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [], + "layout": "IPY_MODEL_4d629e2756bc475e8dba353cdd2b74c3" + } + }, + "bc3360281761471fb9a5b5cb9250bf92": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fbee0efafec94ef6a6470501c070cdd3", + "placeholder": "", + "style": "IPY_MODEL_c0bbbd98938b41498fa889c4b5736171", + "value": "🐕 Use the fine-tuned to model generate a response, just like with the base example..
\n", - " |
-| ORPO Training | Learn how to train models using Odds Ratio Preference Optimization | 🐢 Train a model using instruction and preference data |
+| DPO Training | Learn how to train models using Direct Preference Optimization | 🐢 Train a model using the Anthropic HH-RLHF dataset |
+| ORPO Training | Learn how to train models using Odds Ratio Preference Optimization | 🐢 Train a model using instruction and preference data |
## Resources
@@ -38,4 +38,4 @@ ORPO introduces a combined approach to instruction tuning and preference alignme
- [Blog post on DPO](https://huggingface.co/blog/dpo-trl) - Practical guide on implementing DPO using the TRL library with code examples and best practices.
- [TRL example script on DPO](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py) - Complete example script demonstrating how to implement DPO training using the TRL library.
- [TRL example script on ORPO](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) - Reference implementation of ORPO training using the TRL library with detailed configuration options.
-- [Hugging Face Alignment Handbook](https://github.com/huggingface/alignment-handbook) - Resource guides and codebase for aligning language models using various techniques including SFT, DPO, and RLHF.
\ No newline at end of file
+- [Hugging Face Alignment Handbook](https://github.com/huggingface/alignment-handbook) - Resource guides and codebase for aligning language models using various techniques including SFT, DPO, and RLHF.
diff --git a/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
index 6539fa68..eb79e253 100644
--- a/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ b/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
@@ -1,5498 +1,8937 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Preference Alignment with Direct Preference Optimization (DPO)\n",
- "\n",
- "This notebook will guide you through the process of fine-tuning a language model using Direct Preference Optimization (DPO). We will use the SmolLM2-135M-Instruct model which has already been through a SFT training, so it it compatible with DPO. You can also use the model you trained in [1_supervised_finetuning](../1_supervised_finetuning/notebooks/sft_finetuning_example.ipynb).\n",
- "\n",
- "Take a dataset from the Hugging Face hub and align a model on it.
\n", - "Difficulty Levels
\n", - "🐢 Use the `trl-lib/ultrafeedback_binarized` dataset
\n", - "🐕 Try out the `argilla/ultrafeedback-binarized-preferences` dataset
\n", - "🦁 Select a dataset that relates to a real world use case your interested in, or use the model you trained in [1_supervised_finetuning](../1_supervised_finetuning/notebooks/sft_finetuning_example.ipynb)
\n", - "🦁 change the model to the path or repo id of the model you trained in [1_supervised_finetuning](../1_supervised_finetuning/notebooks/sft_finetuning_example.ipynb)
\n", - "Take a dataset from the Hugging Face hub and align a model on it.
\n", + "Difficulty Levels
\n", + "🐢 Use the `trl-lib/ultrafeedback_binarized` dataset
\n", + "🐕 Try out the `argilla/ultrafeedback-binarized-preferences` dataset
\n", + "🦁 Select a dataset that relates to a real-world use case you’re interested in, or use the model you trained in\n", + " 1_instruction_tuning
\n", + "🦁 change the model to the path or repo id of the model you trained in 1_instruction_tuning
\n", + "\n" + ] }, - "cd2481e7bb2c4dc790ba38e0290c6804": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_3e2d4356bdbc49948139a2bd9cc73756", - "IPY_MODEL_eeaf54e93cb64dc7a926134f574d0e28", - "IPY_MODEL_434a2a07f3304c06a198dff182d7046a" + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 113, + "referenced_widgets": [ + "3049acdab9ce47e38cc4410ca251c0ad", + "20f44f98fd4244d982b038c63106b597", + "bcd5a050c32e418ca7eba04a382dc977", + "7a117fa4a42e4fcf9c6eccbe215f04bb", + "05d47981758a4a678a371c8afbe5bc7c", + "64c2831644284a63a4e2dfcc60e17ceb", + "2720d66401e24bdd94b3be962151ef4e", + "9867ef2ffa454a4b866d962c071946f4", + "79407d5cfe124ba7b551fbd7e5dc3768", + "142fb8e09ae94bb496a41319e02703e4", + "ef6406608e404c379ab0a77558b8f10d", + "2b4028c8f4644713b1bd061c110ad4e9", + "68979814021544e7b0fac53034982724", + "522a78998c5446248da0f19ec87af0a8", + "84a1ce773dd346df930525772877c62d", + "3fc34fbc26c343f895f9d67c6e2e9a93", + "ba66fbdd8f46426b955eec589947e186", + "2e31b64a545e4334b6ceb7b7099233cb", + "f21b4b63fbd04bb589d2a25d6a9327e7", + "cd8bd0fd50d84c928afb4c738f043563", + "74abc5b1279b4bdba9d88852d3fbc236", + "5785ba64a8954af5955c5cd9bbeba341", + "1fe5f377d12d42859d71724eca050e43", + "e774612062d949179a3763801c33a870", + "bc72481494fa4c1588d040fd4ecb78ae", + "47c7ee434bca4d20acba0dd01b48ba3e", + "a1d6a9b8610d49a69959c708a6a7bf29", + "475000e1947444f398e6222687605984", + "eb50daf4b9774c7da234372f708377a6", + "b609471cb40541e897292b63fcf0a465", + "3e96d18eb2354912a306279190958402", + "0c3e83aa4fc14544a89628d0763049bb", + "4cb8aac8e41e4d058b9840e23d788ff8" + ] + }, + "id": "TeQCWYJ93KUe", + "outputId": "07e73ba6-7f15-4de6-e58c-0ce88ba73e26" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/812 [00:00" + ], + "application/javascript": [ + "\n", + " window._wandbApiKey = new Promise((resolve, reject) => {\n", + " function loadScript(url) {\n", + " return new Promise(function(resolve, reject) {\n", + " let newScript = document.createElement(\"script\");\n", + " newScript.onerror = reject;\n", + " newScript.onload = resolve;\n", + " document.body.appendChild(newScript);\n", + " newScript.src = url;\n", + " });\n", + " }\n", + " loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n", + " const iframe = document.createElement('iframe')\n", + " iframe.style.cssText = \"width:0;height:0;border:none\"\n", + " document.body.appendChild(iframe)\n", + " const handshake = new Postmate({\n", + " container: iframe,\n", + " url: 'https://wandb.ai/authorize'\n", + " });\n", + " const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n", + " handshake.then(function(child) {\n", + " child.on('authorize', data => {\n", + " clearTimeout(timeout)\n", + " resolve(data)\n", + " });\n", + " });\n", + " })\n", + " });\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "/content/wandb/run-20241214_184320-a1wx6dl8"
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+ "| Step | \n", + "Training Loss | \n", + "
|---|---|
| 1 | \n", + "0.693100 | \n", + "
| 2 | \n", + "0.693100 | \n", + "
| 3 | \n", + "0.729400 | \n", + "
| 4 | \n", + "0.660900 | \n", + "
| 5 | \n", + "0.736700 | \n", + "
| 6 | \n", + "0.729100 | \n", + "
| 7 | \n", + "0.673400 | \n", + "
| 8 | \n", + "0.585100 | \n", + "
| 9 | \n", + "0.703600 | \n", + "
| 10 | \n", + "0.735000 | \n", + "
| 11 | \n", + "0.547500 | \n", + "
| 12 | \n", + "0.693000 | \n", + "
| 13 | \n", + "0.575200 | \n", + "
| 14 | \n", + "0.673800 | \n", + "
| 15 | \n", + "0.611000 | \n", + "
| 16 | \n", + "1.156200 | \n", + "
| 17 | \n", + "1.110500 | \n", + "
| 18 | \n", + "0.678100 | \n", + "
| 19 | \n", + "0.588100 | \n", + "
| 20 | \n", + "0.567000 | \n", + "
| 21 | \n", + "0.475400 | \n", + "
| 22 | \n", + "0.714400 | \n", + "
| 23 | \n", + "0.565500 | \n", + "
| 24 | \n", + "1.061800 | \n", + "
| 25 | \n", + "0.824100 | \n", + "
| 26 | \n", + "0.671500 | \n", + "
| 27 | \n", + "0.548800 | \n", + "
| 28 | \n", + "0.489100 | \n", + "
| 29 | \n", + "0.505100 | \n", + "
| 30 | \n", + "0.577000 | \n", + "
| 31 | \n", + "0.693300 | \n", + "
| 32 | \n", + "0.603200 | \n", + "
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"max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GHTtn__tQby8", + "outputId": "8d2c1425-ca3d-4166-a508-3235bc7140d5" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "After DPO:\n", + "user\n", + "Write a haiku about programming\n", + "\n", + "A programming haiku is a short, concise poem that explores the relationship between programming and programming languages. It's a fun and creative way to express your thoughts about programming. You can use any language you like, but I recommend choosing a programming language that's easy to learn and familiar with.\n", + "\n", + "Here's an example of a programming haiku:\n", + "\n", + "I'm a programming language\n", + "I'm a language\n", + "I'm a language\n", + "I'm a language\n", + "I'm a language\n", + "I\n" + ] + } + ] }, - "f29ddadad3bf4f71b0bc5fe74cbb267f": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - 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By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n", + "\n", + "- Try this notebook on a harder difficulty\n", + "- Review a colleagues PR\n", + "- Improve the course material via an Issue or PR." + ] }, - "ff50fde20bf740f991a7157880ccef24": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_dfe6f96b028542ca94ba25486bdaa34e", - "max": 5178, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_e8f534ec3b9849699870c386615475bb", - "value": 5178 - } + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "zgCYHyvnd3fk" + }, + "execution_count": null, + "outputs": [] } - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "4b75a967a56a44f59b2ed5a6603511d9": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [], + "layout": "IPY_MODEL_a2b0d8119e994845b04fa96d9a9c70ef" + } + }, + "4caf46da78124e4796e94822a39938e3": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_903adb7ed37648949c290700b56f1d7f", + "placeholder": "", + "style": "IPY_MODEL_0e0b5be0351345b1b75ff65de31aec0e", + "value": "
Take a dataset from the Hugging Face hub and align a model on it.
\n", - "Difficulty Levels
\n", - "🐢 Use the `trl-lib/ultrafeedback_binarized` dataset
\n", - "🐕 Try out the `argilla/ultrafeedback-binarized-preferences` dataset
\n", - "🦁 Try on a subset of mlabonne's `orpo-dpo-mix-40k` dataset
\n", - "Take a dataset from the Hugging Face hub and align a model on it.
\n", + "Difficulty Levels
\n", + "🐢 Use the `trl-lib/ultrafeedback_binarized` dataset
\n", + "🐕 Try out the `argilla/ultrafeedback-binarized-preferences` dataset
\n", + "🦁 Try on a subset of mlabonne's `orpo-dpo-mix-40k` dataset
\n", + "| Step | \n", + "Training Loss | \n", + "Validation Loss | \n", + "Runtime | \n", + "Samples Per Second | \n", + "Steps Per Second | \n", + "Rewards/chosen | \n", + "Rewards/rejected | \n", + "Rewards/accuracies | \n", + "Rewards/margins | \n", + "Logps/rejected | \n", + "Logps/chosen | \n", + "Logits/rejected | \n", + "Logits/chosen | \n", + "Nll Loss | \n", + "Log Odds Ratio | \n", + "Log Odds Chosen | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | \n", + "2.292800 | \n", + "2.288896 | \n", + "149.367500 | \n", + "6.695000 | \n", + "3.347000 | \n", + "-0.228490 | \n", + "-0.244308 | \n", + "0.534000 | \n", + "0.015818 | \n", + "-2.443077 | \n", + "-2.284902 | \n", + "8.426007 | \n", + "7.502706 | \n", + "2.200510 | \n", + "-0.883863 | \n", + "0.175297 | \n", + "
| 14 | \n", + "2.228700 | \n", + "2.266388 | \n", + "152.804300 | \n", + "6.544000 | \n", + "3.272000 | \n", + "-0.225122 | \n", + "-0.241009 | \n", + "0.532000 | \n", + "0.015887 | \n", + "-2.410092 | \n", + "-2.251219 | \n", + "8.549180 | \n", + "7.645444 | \n", + "2.179086 | \n", + "-0.873021 | \n", + "0.176024 | \n", + "
| 21 | \n", + "2.174400 | \n", + "2.252183 | \n", + "152.898000 | \n", + "6.540000 | \n", + "3.270000 | \n", + "-0.222980 | \n", + "-0.238710 | \n", + "0.528000 | \n", + "0.015731 | \n", + "-2.387105 | \n", + "-2.229796 | \n", + "8.626538 | \n", + "7.736025 | \n", + "2.165429 | \n", + "-0.867542 | \n", + "0.174469 | \n", + "
| 28 | \n", + "2.108900 | \n", + "2.245282 | \n", + "153.176400 | \n", + "6.528000 | \n", + "3.264000 | \n", + "-0.221741 | \n", + "-0.237331 | \n", + "0.528000 | \n", + "0.015589 | \n", + "-2.373309 | \n", + "-2.217415 | \n", + "8.682404 | \n", + "7.796231 | \n", + "2.158806 | \n", + "-0.864759 | \n", + "0.173063 | \n", + "
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"align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + "source": [ + "trainer.train() # Train the model\n", + "\n", + "# Save the model\n", + "trainer.save_model(f\"./'smolLM_orpo'\")\n", + "\n", + "# Save to the huggingface hub if login (HF_TOKEN is set)\n", + "if os.getenv(\"HF_TOKEN\"):\n", + " trainer.push_to_hub(tags=finetune_tags)" + ] }, - 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"model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_e3053b2ce5da437686c6f1ebd291b9c0", - "IPY_MODEL_68fec6c768d6491993a6f424fd9418d6", - "IPY_MODEL_29a246ffb7a44dbabd3df938f5ec0387" + { + "cell_type": "code", + "source": [ + "# Test the fine-tuned model on the same prompt\n", + "\n", + "# Let's test the base model before training\n", + "prompt = \"Write a haiku about programming\"\n", + "\n", + "# Format with template\n", + "messages = [{\"role\": \"user\", \"content\": prompt}]\n", + "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "# Generate response\n", + "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens=100)\n", + "print(\"After DPO:\")\n", + "print(tokenizer.decode(outputs[0], skip_special_tokens=True))" ], - 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How can I help you today? I'm going to write a haiku about programming. What is programming? Programming is a way of writing instructions or rules for computers to follow. It's like giving a set of instructions to a computer to do something. Have you ever programmed a computer before? If not, I can help you learn more about it.\n", + "user\n", + "I've been programming for a while now. What's the difference between programming and coding? Programming\n" + ] + } + ] }, - "fccf17dc78654a0498d9cca4ef04f848": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - 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By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n", + "\n", + "- Try this notebook on a harder difficulty\n", + "- Review a colleagues PR\n", + "- Improve the course material via an Issue or PR." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "be9037f0befc40b8b1f6d2e159f2e0ad": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [], + "layout": "IPY_MODEL_1733a0a6375a4c1ca0c86c128cb611df" + } + }, + "35b5489d3b44406a9de13ebfe9d10771": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ffc07c5a6da744faa22316c220fe674a", + "placeholder": "", + "style": "IPY_MODEL_7ff5a7e0a66042ea823b4f13dca551bc", + "value": "