{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "IqM-T1RTzY6C" }, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", " \n", " Join Discord if you need help + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth?tab=readme-ov-file#-installation-instructions).\n", "\n", "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\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)**\n", "\n", "Features in the notebook:\n", "1. Uses Maxime Labonne's [FineTome 100K](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset.\n", "1. Convert ShareGPT to HuggingFace format via `standardize_sharegpt`\n", "2. Train on Completions / Assistant only via `train_on_responses_only`\n", "3. Unsloth now supports Torch 2.4, all TRL & Xformers versions & Python 3.12!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "executionInfo": { "elapsed": 30097, "status": "ok", "timestamp": 1733775916326, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%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" ] }, { "cell_type": "markdown", "metadata": { "id": "r2v_X2fA0Df5" }, "source": [ "* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n", "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", "* [**NEW**] We make Gemma-2 9b / 27b **2x faster**! See our [Gemma-2 9b notebook](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)\n", "* [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" ] }, { "cell_type": "markdown", "metadata": { "id": "OaX2Jb8nmWNo" }, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 5, "status": "ok", "timestamp": 1733775916326, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "IbyXEE3Elbas" }, "outputs": [], "source": [ "TOKEN = \"xxx\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "executionInfo": { "elapsed": 4, "status": "ok", "timestamp": 1733775916326, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "jZEZc62s5By4" }, "outputs": [], "source": [ "MAX_SEQ_LENGTH = 2048" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 316, "referenced_widgets": [ "137dab521f6248e3a362192f7adf9c7b", "d7e30479b93b402d8ff7cec6b4710c4a", "bb0e6b51ecb9445392c959ea5f3058c1", "783901988bef4d52acb581c01ff650f9", "068928b471e849fcb96a70dbc72b3345", "9023135509f9419aa987cb5f06d1e80c", "b484e3be88b74d408979738fdfee0d99", "c3a81e2cf4784298b2907d05a618c6ea", "4b836fa12ea24736ae778ada9787d47c", "5e4227587197440badc72e9be604f8cf", "f7d158f4d6494688902846bacfe590c8", "b3e88ceb56894cc0938a7c5223eaad0e", "c269585f03ff4bae8d7e967a963e8a9e", "c2889da5ada0495e9737b45397ba5602", "e2a08612c6bc4cf1aa57dd2741b50ed4", "ca0fdcbf1da242fb808664e04918b8c9", "3f117443f3fa4cd5b860518c6de3bcfa", "8096adf132044418905c10849555fd7d", "4631a7c48bf0421bbfce874c5fb0161c", "68ea4e5f4dd24707a4b2c6a58babe188", "9e7474eb0fb14087bac09b941e81e51b", "eb007f2385774348af58b0236abc14c5", "90009e55d1954b57b216376d8ca85884", "032da9b9f658458586fa8244e6fcb879", "68e0d86fdd90415c912bd245fddeb3bc", "b1c54c1e144644f880e835e78fd83ef9", "b4ea4b46272048b1b58694b4c349f181", "2d86506ba89e4fa694d670f65a3bad69", "32c93a80c3b8477f841e7a40b4def1c9", "529ec51b0d9a478bb7ef9aba2304fed1", "de4228bf9e5142f094b85d2ebe993ea3", "59203004a8174a429bf06b034c09d120", "3321c6a807e548e8a2910ef861935101", "99c528059643427a9e19febebe3cd584", "55dfb965723a4798a8bd18a8423f58a1", "fa3f324b350b44208359b0d7d783c3c6", "b74805b7a0b04252a9a4073468247ec8", "31d4a509cc414a459383365a4f379a27", "b1a5310ab97f49189da8ebb48421223e", "430527ebf9da49b6a865071e2c5c5301", "d32a5092e3bb4c299ed3fe6d376262bd", "65db2782e9bc4d8d831a2d15be6629dd", "43d8de1eb50a4574adc102c76cd5b245", "1276f878f86342a5acbd2b5ba73bb6b3", "baa7b1a4bea4402a8508d3ba954a6cdc", "37fc9ded06e3431d877bacc7113b45aa", "971b11550323468da9c5bd9e3a7feb38", "f027940dfa2e44e2aee04b2e86d67b57", "e819d4dbcf0844728f080f0b883aec89", "37bc83da455e4a85abea899d39fc26e7", "a5ce8041514846c79bdefbdeee2fce71", "31c34830ce494439a7e6ab758efa1aa6", "1af4ee34b688479caeacc31b9c81b645", "a1b44817da4f4a4aad7545082af045bd", "be98b168374344e89e7080ad98e553fd" ] }, "executionInfo": { "elapsed": 79785, "status": "ok", "timestamp": 1733775996107, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "QmUBVEnvCDJv", "outputId": "281e68e3-fa12-4d58-92df-defdf684b728" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ¦₯ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "πŸ¦₯ Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2024.12.4: Fast Llama patching. Transformers:4.46.3.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 7.5. CUDA Toolkit: 12.1. Triton: 3.1.0\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" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "137dab521f6248e3a362192f7adf9c7b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors: 0%| | 0.00/2.24G [00:00<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", "\n", "Cutting Knowledge Date: December 2023\n", "Today Date: 26 July 2024\n", "\n", "<|eot_id|><|start_header_id|>user<|end_header_id|>\n", "\n", "What is the ELO of this game: 1. e4 e6 2. d4 d5 3. exd5 exd5 4. Bd3 Bd6 5. Ne2 Nf6 6. c4 dxc4 0-1<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", "\n", "To determine the ELO rating of this game, we need to analyze the moves and the outcome.\n", "\n", "The ELO rating of a game is determined by the difference in rating between the two players. However, since this is a one-move game, we can't directly calculate the ELO rating. But we can calculate the ELO rating of each player based on their moves.\n", "\n", "The moves are:\n", "1. e4 e6\n", "2. d4 d5\n", "3. exd5 exd5\n", "4. Bd3 Bd6\n", "5. Ne2 Nf6\n", "6. c4 dxc4\n", "\n", "This is a Sicilian Defense, a popular opening in chess. The game is not a typical one, as it ends in a draw (0-0-1). To calculate the ELO rating, we need to compare the moves of both players.\n", "\n", "In this case, White (ELO rating of White) has made the moves 1. e4, 2. d4, and 3. exd5, which is a common sequence in the Sicilian Defense. Black (ELO rating of Black) has responded with 2. d5, 3. exd5, and 4. Bd3, which is a typical response in the Sicilian Defense.\n", "\n", "However, Black has also made some unusual moves, such as 5. Ne2 Nf6, which is not a typical move in this position. This move is likely a mistake, as it weakens Black's pawn structure and creates a potential weakness on the kingside.\n", "\n", "Considering the overall moves, I would estimate the ELO rating of White to be around 2200-2400, while the ELO rating of Black is around 1800-2000. However, please note that this is a rough estimate, and actual ELO ratings can vary depending on the player's skill level and experience.\n", "\n", "In general, a 200-point difference between two players is a significant gap, and it's not uncommon for a stronger player to win by a large margin in a game with such a significant rating difference.<|eot_id|>" ] } ], "source": [ "from threading import Thread\n", "from transformers import TextIteratorStreamer\n", "\n", "streamer2 = TextIteratorStreamer(tokenizer)\n", "\n", "prompt = tokenizer.apply_chat_template (\n", " history,\n", " tokenize = False,\n", " add_generation_prompt = True, # Must add for generation\n", ")\n", "\n", "inputs = tokenizer([prompt], return_tensors=\"pt\")\n", "\n", "# Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.\n", "generation_kwargs = dict(inputs, streamer=streamer2)\n", "\n", "thread = Thread(target=model.generate, kwargs=generation_kwargs)\n", "thread.start()\n", "\n", "generated_text = \"\"\n", "for new_text in streamer2:\n", " generated_text += new_text\n", " print(new_text, end=\"\", flush=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "ZEo8cUcrt-XZ" }, "source": [ "Before Training:" ] }, { "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": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 6017, "status": "ok", "timestamp": 1733776066434, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "6bZsfBuZDeCL", "outputId": "fafd4dcd-4fbf-4071-d16c-dbc143ba9058" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Unsloth: Dropout = 0 is supported for fast patching. You are using dropout = 0.8.\n", "Unsloth will patch all other layers, except LoRA matrices, causing a performance hit.\n", "Unsloth 2024.12.4 patched 28 layers with 0 QKV layers, 0 O layers and 0 MLP layers.\n" ] } ], "source": [ "model = FastLanguageModel.get_peft_model(\n", " model,\n", " r = 4, # 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 = 8,\n", " lora_dropout = 0.8, # 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" }, "source": [ "\n", "### Data Prep\n", "We now use the `Llama-3.1` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. But we convert it to HuggingFace's normal multiturn format `(\"role\", \"content\")` instead of `(\"from\", \"value\")`/ Llama-3 renders multi turn conversations like below:\n", "\n", "```\n", "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n", "\n", "Hello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", "\n", "Hey there! How are you?<|eot_id|><|start_header_id|>user<|end_header_id|>\n", "\n", "I'm great thanks!<|eot_id|>\n", "```\n", "\n", "We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3` and more." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "3f5e1981361b4ae9b56088950c368d9d", "e2ab0ba89aa54e1cad5a9ca729798063", "1799319f591d4768b85c7393cccd7c52", "4879ff3007fe4a49bb56f084fb5e063f", "56ceeb095aaf4ec1903f77d8aa155a93", "ed712652cdd44d26a845c3e5ddd47c88", "8bfa6035777445cfabdbd34e8dfd752f", "3f6def6294cd4fa0979af1d6191daa0f", "b232a43443bc4a5ca9d5373921430908", "609491424f3b43b6bf33f6fdca36c9b3", "4cbb27ec75114bd6bee5d532ee9f4ac9", "a8a313a8b776496994fba48b1de96ea0", "5eda0b5a2cff43708609c324be81a16f", "367477f4130a42f083661c7dc42e95bd", "5f1b8b90e0a541a68f1d65ca662ea831", "c02f391414c84d0b9ab8fa53f5577fe1", "132d91476ead496cab4bfcee492418bf", "47d0a11be02643da8013323bb3056b2b", "80cf12829616481bb00dbd4d1457a061", "2b7327dd5576423c85caaeb2bfc9bdf3", "5341120c43ac4c59a64aa5b7275ea610", "70b5b2669749474a854fcec6f731e0f6", "86690b93fc8d4bf1850b7ef96db24532", "7cb0a2c20b30414aa40c36ef32cba4b4", "546b8fc25242499abb7f64ae7d3f37ee", "126ffb1875184036886b1a53e6a1788d", "f6cf50a6a9ec4e6f91122108f61fb2f1", "c6d2ce6c09124b008b791fe488b3ea61", "144c726393e14fbcb27814dcfc41e70c", "38a7547c71114ec8be51c648556e4b12", "b49f823a5e6f4e1a87e9380fb7ae1e24", "27b2e10308dd451991a46b8885a74e85", "9aa6caa9d2c84a54b7d4e18341808949" ] }, "executionInfo": { "elapsed": 4109, "status": "ok", "timestamp": 1733776070540, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "LjY75GoYUCB8", "outputId": "476fa0dd-29d8-4d2b-863f-53fb9bd08171" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3f5e1981361b4ae9b56088950c368d9d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/87.0 [00:00<|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nGuess the elo of this game: 1. d4 d5 2. c4 e6 3. cxd5 exd5 4. Nc3 c6 5. Nf3 f5 6. Bg5 Nf6 7. e3 Bd6 8. Bd3 O-O 9. O-O Qe8 10. Bxf6 Rxf6 11. a3 Be6 12. Ne5 Bxe5 13. dxe5 Rh6 14. f4 Nd7 15. Ne2 Nc5 16. Nd4 Ne4 17. Bxe4 fxe4 18. b4 b6 19. Rc1 Rc8 20. Qe2 c5 21. Nb5 Qg6 22. Nd6 Rc7 23. f5 Bxf5 24. Nxf5 Rh5 25. g4 Rg5 26. h3 h5 27. Rf4 Rf7 28. Kf2 Rfxf5 29. Rxf5 Rxf5+ 30. gxf5 Qxf5+ 31. Ke1 Qxe5 32. bxc5 bxc5 33. Rxc5 Qg3+ 34. Kd2 Qe5 35. Qb5 Qh2+ 36. Kc3 Qf2 37. Qe8+ Kh7 38. Qxh5+ Kg8 39. Rc8+ Qf8 40. Rxf8+ Kxf8 41. Qf5+ Ke7 42. Qxd5 Kf6 43. Qxe4 Kg5 44. Qd4 Kh6 45. Qxa7 g5 46. Qa6+ Kh5 47. Qa5 Kh4 48. Qxg5+ 1-0<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nThe average elo of the game is: 2300.0<|eot_id|>'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[5][\"text\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "idAEIeSQ3xdS" }, "source": [ "\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`!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 84, "referenced_widgets": [ "b4fc58859e284cd4a5d16b9946b671d5", "8479a524c9794bb08e16be8df0802339", "5d00575a870a45ae8cd224f90af746e2", "23a0f246a560479696075f18f6e5b0ee", "ffd666b593014a40b7e5e4ce6eade553", "fe04940516044a4ebfcf9f27b82f62ba", "4923a95cf71d40c28990f763e65e279e", "2b5fe39288f74c0eb073109ce0a74959", "50810dede1f64977ae93c293108515bf", "022a73258360430793abfa7d507d25e2", "3bb08a85f23d49ba975c4a17eee0a67d" ] }, "executionInfo": { "elapsed": 342321, "status": "ok", "timestamp": 1733776433544, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "95_Nn-89DhsL", "outputId": "4085b24f-3213-4caa-cfe6-836aa5346aa7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mounted at /content/drive\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b4fc58859e284cd4a5d16b9946b671d5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map (num_proc=2): 0%| | 0/128927 [00:00user<|end_header_id|>\\n\\n\",\n", " response_part = \"<|start_header_id|>assistant<|end_header_id|>\\n\\n\",\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "Dv1NBUozV78l" }, "source": [ "We verify masking is actually done:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 157 }, "executionInfo": { "elapsed": 16, "status": "ok", "timestamp": 1733776551774, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "LtsMVtlkUhja", "outputId": "70659e67-ced6-45a6-ae14-71000286efff" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nGuess the elo of this game: 1. d4 d5 2. c4 e6 3. cxd5 exd5 4. Nc3 c6 5. Nf3 f5 6. Bg5 Nf6 7. e3 Bd6 8. Bd3 O-O 9. O-O Qe8 10. Bxf6 Rxf6 11. a3 Be6 12. Ne5 Bxe5 13. dxe5 Rh6 14. f4 Nd7 15. Ne2 Nc5 16. Nd4 Ne4 17. Bxe4 fxe4 18. b4 b6 19. Rc1 Rc8 20. Qe2 c5 21. Nb5 Qg6 22. Nd6 Rc7 23. f5 Bxf5 24. Nxf5 Rh5 25. g4 Rg5 26. h3 h5 27. Rf4 Rf7 28. Kf2 Rfxf5 29. Rxf5 Rxf5+ 30. gxf5 Qxf5+ 31. Ke1 Qxe5 32. bxc5 bxc5 33. Rxc5 Qg3+ 34. Kd2 Qe5 35. Qb5 Qh2+ 36. Kc3 Qf2 37. Qe8+ Kh7 38. Qxh5+ Kg8 39. Rc8+ Qf8 40. Rxf8+ Kxf8 41. Qf5+ Ke7 42. Qxd5 Kf6 43. Qxe4 Kg5 44. Qd4 Kh6 45. Qxa7 g5 46. Qa6+ Kh5 47. Qa5 Kh4 48. Qxg5+ 1-0<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nThe average elo of the game is: 2300.0<|eot_id|>'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.decode(trainer.train_dataset[5][\"input_ids\"])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "executionInfo": { "elapsed": 13, "status": "ok", "timestamp": 1733776551774, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "_rD6fl8EUxnG", "outputId": "71231cca-4743-4ade-9997-8d04bea14008" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "' \\n\\nThe average elo of the game is: 2300.0<|eot_id|>'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "space = tokenizer(\" \", add_special_tokens = False).input_ids[0]\n", "tokenizer.decode([space if x == -100 else x for x in trainer.train_dataset[5][\"labels\"]])" ] }, { "cell_type": "markdown", "metadata": { "id": "3enWUM0jV-jV" }, "source": [ "We can see the System and Instruction prompts are successfully masked!" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 12, "status": "ok", "timestamp": 1733776551774, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "2ejIt2xSNKKp", "outputId": "94141611-d4c8-4960-cc8d-b8a7f6685a0d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GPU = Tesla T4. Max memory = 14.748 GB.\n", "2.836 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": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 283 }, "executionInfo": { "elapsed": 37715, "status": "ok", "timestamp": 1733776589479, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "yqxqAZ7KJ4oL", "outputId": "bb83d562-a3b2-41b7-fa85-03ee64a24aff" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py:3354: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location=map_location)\n", "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", " \\\\ /| Num examples = 128,927 | Num Epochs = 1\n", "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n", "\\ / Total batch size = 8 | Total steps = 2,500\n", " \"-____-\" Number of trainable parameters = 6,078,464\n", "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py:3033: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " checkpoint_rng_state = torch.load(rng_file)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " \n", " [2500/2500 00:01, Epoch 0/1]\n", "
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StepTraining Loss
25010.446200

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train(\n", " # Uncomment if running from a checkpoint\n", " resume_from_checkpoint = True\n", ")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 10, "status": "ok", "timestamp": 1733776589479, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "pCqnaKmlO1U9", "outputId": "d4fa0682-525e-410f-d30f-47f61ec15861" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "27.8136 seconds used for training.\n", "0.46 minutes used for training.\n", "Peak reserved memory = 4.119 GB.\n", "Peak reserved memory for training = 1.283 GB.\n", "Peak reserved memory % of max memory = 27.929 %.\n", "Peak reserved memory for training % of max memory = 8.699 %.\n" ] } ], "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": "uMuVrWbjAzhc" }, "source": [ "\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!" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 165, "referenced_widgets": [ "71be86350b544e68b7473775800e43e3", "2acd116dba1840dd82cb2879fd28e53e", "771c8d122fd84eb3adfaee96ca3891db", "ef96549d43d84753bb6f0be9a76bc0aa", "bebb18d34499496ea118bc26615c3ca9", "859bfad739704afab1013290e6ee335c", "8327e06f374649eea8b64373bf33be67", "e0dde78cb39d4e51ae2d258e2ec645df", "e1c120e27d784567a4c1aa7649ecbe1a", "543adfb75a5a4f58b89e7bb074ff910c", "a706c65ec4e14d569ca25319680a2568", "101960d821bf4e4d8e74355b372be1ea", "8a64afa504bc43a0a25ba3867b233c29", "bac88f5fbbb04fc39ee234d2a9107e60", "5dcacbf5b5684988a18f48bd5be5bbc5", "7ee7d47cfbef42d3b69a09f9c04b1a74", "a635d4f27fd04ae0bbbe72d60230c445", "9d56e6cb0e2246249021d99d385d1700", "3cfe5c50f29a4028a57fae647ccf267c", "ca6a9ca5ef4d48dea0ed355fe066293f", "515eee026bb44068b7a7b6bcfa673a49", "9b35a5fc5e8348389eaf91b6449ed64c", "681e2238872842c8975abdfb8f3a19ff", "8ecc3958251c4ef58202439966b65f5b", "e37d036cf934416fadec7a60541469f3", "9a29355024e64112987c3961fb758d10", "ff58c5a17a534cc9882ed28f90899ace", "ed855df0dc324842a75e57367808d0e2", "5f0f452e6fad4a00aeb6a21c6e4ff32b", "e08d0386e1b54783b8680f74052ed955", "d46889fb1dc64383b11d867840101b3e", "504bfb6de9d9483aa12be69424527cb4", "36d1fd97afca4ed8abae62bbbe4bee5a" ] }, "executionInfo": { "elapsed": 4726, "status": "ok", "timestamp": 1733776594198, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "upcOlWe7A1vc", "outputId": "f1f5fe3d-9ca9-4049-acb5-1d165eb3e028" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "71be86350b544e68b7473775800e43e3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/5.18k [00:00\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)**\n", "\n", "We use `min_p = 0.1` and `temperature = 1.5`. Read this [Tweet](https://x.com/menhguin/status/1826132708508213629) for more information on why." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 153, "referenced_widgets": [ "c03c5dbd54374bafbf0d9ee27ee4981b", "554b600def0d45bfa8d0f0d7533910d0", "53e9c8fc304b4f2689f155dd0b32cdeb", "0d595d321dcc4cfba8aac4449752c2ea", "ae193fe833034e4abbba0ffe0109843d", "3e4b08174f71435e9e8296a4285bb00d", "a780b06d10f94023b4a3eb31cd02d48e", "a4c4a49755ac49938167e5b0c6008393", "2141802f32f9417285c5ff4b4ac37967", "d37314370ed3405fa6acf6865122198c", "851deff8b37b4d459896ef366baf0b23" ] }, "executionInfo": { "elapsed": 24686, "status": "ok", "timestamp": 1733776618880, "user": { "displayName": "Eugene Park", "userId": "00968345404729434424" }, "user_tz": -60 }, "id": "E3dTAlojfTuJ", "outputId": "64b284ca-ad3f-4ccf-8db1-5966b8967c7b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==((====))== Unsloth 2024.12.4: Fast Llama patching. Transformers:4.46.3.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 7.5. CUDA Toolkit: 12.1. Triton: 3.1.0\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" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c03c5dbd54374bafbf0d9ee27ee4981b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "adapter_model.safetensors: 0%| | 0.00/24.4M [00:00\n", " \n", " \n", " Support our work if you can! Thanks!\n", "" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [ { "file_id": "1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9", "timestamp": 1732531554962 }, { "file_id": "15OyFkGoCImV9dSsewU1wa2JuKB4-mDE_", "timestamp": 1727333957154 }, { "file_id": "1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp", "timestamp": 1724446900756 }, { "file_id": "135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp", "timestamp": 1721714808667 }, { "file_id": "10NbwlsRChbma1v55m8LAPYG15uQv6HLo", "timestamp": 1713459337061 }, { "file_id": "1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_", "timestamp": 1708958229810 }, { "file_id": "1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5", "timestamp": 1703608159823 }, { "file_id": "1oW55fBmwzCOrBVX66RcpptL3a99qWBxb", "timestamp": 1702886138876 } ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "022a73258360430793abfa7d507d25e2": { "model_module": "@jupyter-widgets/base", 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