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Comet provides tooling to track, explain, manage, and monitor your models in a single place! It works with Jupyter notebooks and scripts and-- most importantly--it's 100% free to get started!\n", + "\n", + "[unsloth](https://github.com/unslothai/unsloth) dramatically improves the speed and efficiency of LLM fine-tuning for models including Llama, Phi-3, Gemma, Mistral, and more. For a full listed of 100+ supported unsloth models, [see here](https://huggingface.co/unsloth).\n", + "\n", + "Instrument your torchtune training runs with Comet to start managing experiments with efficiency, reproducibility, and collaboration in mind.\n", + "\n", + "Find more information about [our integration with torchtune here](https://www.comet.com/docs/v2/integrations/third-party-tools/unsloth?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth) or [learn about our other integrations here](https://www.comet.com/docs/v2/integrations?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth)." + ], + "metadata": { + "id": "KDjO5WbeDtp0" + } + }, + { + "cell_type": "markdown", + "source": [ + "## ⚙ Install and import dependencies" + ], + "metadata": { + "id": "f0fhpJZgsYII" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "5zTSjY9r4cGc" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install comet_ml \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", + "\n", + "# check torch version for Xformers (2.3 -> 0.0.27)\n", + "from torch import __version__; from packaging.version import Version as V\n", + "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n", + "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton" + ] + }, + { + "cell_type": "code", + "source": [ + "import comet_ml\n", + "import os\n", + "from google.colab import userdata\n", + "\n", + "# Log in to Comet\n", + "COMET_API_KEY = userdata.get('COMET_API_KEY')\n", + "comet_ml.login(api_key = COMET_API_KEY)\n", + "# Create experiment object\n", + "exp = comet_ml.Experiment(project_name=\"comet-example-unsloth\", workspace=\"examples\")\n", + "\n", + "# Set Hugging Face token (for dataset retrieval)\n", + "HF_TOKEN = userdata.get('HF_TOKEN')\n", + "os.environ[\"HF_TOKEN\"] = HF_TOKEN" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "p8GTqJOW88fI", + "outputId": "1c7f76ee-a9ff-418d-8067-4cb357f938e5" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Valid Comet API Key saved in /root/.comet.config (set COMET_CONFIG to change where it is saved).\n", + "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n", + "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/examples/comet-example-unsloth/cb280e1a2ac942cbab2343349d80282f\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## ⚙ Download model" + ], + "metadata": { + "id": "5bfxeJqKwpKj" + } + }, + { + "cell_type": "code", + "source": [ + "from unsloth import FastLanguageModel\n", + "import torch\n", + "\n", + "max_seq_length = 2048\n", + "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + "load_in_4bit = True" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bZei5Dvw7_kM", + "outputId": "62f7a016-6c2b-4f76-ebf2-f2051b907932" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Find the full list of [100+ supported unsloth models here](https://huggingface.co/unsloth). For a full list of supported 4-bit models see [here](https://huggingface.co/collections/unsloth/load-4bit-models-4x-faster-659042e3a41c3cbad582e734)" + ], + "metadata": { + "id": "NG--Sbr1n5Cv" + } + }, + { + "cell_type": "code", + "source": [ + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"unsloth/Meta-Llama-3.1-8B\",\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " token = HF_TOKEN\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284, + "referenced_widgets": [ + "77caf8ce2875463f86cb7e5b0e02d10f", + "986a13810ebf4e11ac2d636013efc948", + "0902e41471fb40f7a7fb807397d1e780", + "ec2615ff447e4a72a359143122a58887", + "bb0052241a574fdbb6ca8c942d99e1eb", + "1aff4958133640838a260a881e84bd66", + "b920a8e467d64b629189d1c76541cd17", + "0fe442a6a70948a4a9b703352148d780", + "5bf1ce41bf6c4b389bac20669bd64e3b", + "7d06574680cb41b9bfac0c3e47916d6e", + "b6cd239b54c345feb11143f2364fcfaf", + "e38214ae388e4fdca976e2adeb30d20c", + "85d9ede0e7064521bfe7a92d54df211f", + "740857a56a134b2dba327dc8bdbc974a", + "725534abda3c462188fa27a978bfb59d", + "c61150dc57cb4279b38064e1bd82b3bc", + "6472e21298f44e6e9c5a3eefcedc59fc", + "adb028ffb1c045f9ade7be249a33b375", + "a1a32114bd2d4cb8882afebf56d64d41", + "8c6ee78067c0490292ee7845dd886a1b", + "6d28c2bb843a48908827b362ad094183", + "41d5a34977dd4488ba5b88c308301fe6", + "d0ce911bc4c845e88168f32781cf7923", + "718199222cfc412e9e1ee351b45c9db6", + "dcde9dfd56de417fb2057b3ccc9725f1", + "c749164ce965417db5411d00f9401623", + "61a290abfcb14c4f8b6cd9e453600bc5", + "7cce70a2af1a4fc2a04658264c37b0c7", + "096fb8d33b804a02b12bc085fa3c372f", + "c6345c375f474aaf88b7b73e662b20f2", + "b68053e4411747b0b12ab63d309aa765", + "76664f2e75744dbf875de8fd4fdbe2d4", + "05891b9099734df397dc8bb7396ef3e2", + "01f7a6c4eec04fc9b063add3719c0777", + "e4f2492f92a74794aec11bf7e2e8d554", + "cba30ecbcad14542b3acc82e4396275c", + "249258f9a1764d40a440a5846227059b", + "53c336629bca4e6cacfe69ffa316890a", + "b7c0946c9f1b40d5b469e28149808565", + "1bc726cc52084737a0269d01f9df6a16", + "51b3596a668742acab22e5a6659d2c55", + "30d75e70fb6e4c4db53137804d55295d", + "2c6dd56cf2f740cca814d5c6fa504d93", + "55e22c9351de46c4a192008dde020393", + "c333e3f23b8f4df6a7fd9f7a000a54ab", + "903f318a80d849098b11451a8c855d50", + "6b7e50badd5b4c7b979d20d7ffd1b132", + "d83d854a33ec4adabc9bef9c2b699a0b", + "1be75bb752214bdc8a3ef6e6976f3312", + "d03ff62190934187be5be033a6928545", + "1ec6fa8d08e642059f1d673f6260169f", + "23351e3d7e8741a1adad4c1292dab841", + "33c4f5595d924cd3a6d8c1575815a5c8", + "6c4cf50fd9ee484396e8272591d21ee9", + "124e70720a374644a6d360926059911f" + ] + }, + "id": "L09m09Xs8Y2e", + "outputId": "4c67ce27-09cd-44dd-e987-7168a29ee335" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==((====))== Unsloth 2024.8: Fast Llama patching. Transformers = 4.44.2.\n", + " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform = Linux.\n", + "O^O/ \\_/ \\ Pytorch: 2.4.0+cu121. CUDA = 8.0. CUDA Toolkit = 12.1.\n", + "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.27.post2. 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" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/5.70G [00:00" + ], + "text/html": [ + "\n", + "
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360.860900
370.882400
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391.090100
401.174900
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420.981300
430.954800
440.911400
450.918000
460.973800
470.872000
481.198100
490.909500
501.032100
511.017500
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530.976500
541.154300
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570.885200
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" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "exp.end()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8II72I92Bm9s", + "outputId": "6518c199-dff2-4c4a-f821-aefdf595fa2c" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Data:\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m display_summary_level : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m name : apparent_pagoda_1033\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m url : https://www.comet.com/examples/comet-example-unsloth/cb280e1a2ac942cbab2343349d80282f\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Metrics [count] (min, max):\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m epoch [61] : (0.0001545595054095827, 0.00927357032457496)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m grad_norm [60] : (0.2999851107597351, 2.063112258911133)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m learning_rate [60] : (0.0, 0.0002)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m loss [60] : (0.7567, 2.3058)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m total_flos : 5726714157219840.0\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/epoch [61] : (0.0001545595054095827, 0.00927357032457496)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/grad_norm [60] : (0.2999851107597351, 2.063112258911133)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/learning_rate [60] : (0.0, 0.0002)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/loss [60] : (0.7567, 2.3058)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/total_flos : 5726714157219840.0\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_loss : 1.0665242771307628\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_runtime : 109.8838\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_samples_per_second : 4.368\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_steps_per_second : 0.546\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_loss : 1.0665242771307628\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_runtime : 109.8838\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_samples_per_second : 4.368\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_steps_per_second : 0.546\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Others:\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m hasNestedParams : True\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Parameters:\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|dispatch_batches : None\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|even_batches : True\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|gradient_accumulation_kwargs : None\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|non_blocking : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|split_batches : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|use_seedable_sampler : True\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|adafactor : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|adam_beta1 : 0.9\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|adam_beta2 : 0.999\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|adam_epsilon : 1e-08\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|auto_find_batch_size : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|batch_eval_metrics : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|bf16 : True\n", + "\u001b[1;38;5;39mCOMET 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config|suppress_tokens : None\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|task_specific_params : None\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|temperature : 1.0\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|tf_legacy_loss : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|tie_encoder_decoder : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|tie_word_embeddings : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|tokenizer_class : None\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|top_k : 50\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|top_p : 1.0\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|torch_dtype : bfloat16\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|torchscript : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|transformers_version : 4.44.2\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|typical_p : 1.0\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|unsloth_version : 2024.8\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|use_bfloat16 : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|use_cache : True\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|vocab_size : 128256\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m peft_config|default : LoraConfig(peft_type=, auto_mapping=None, base_model_name_or_path='unsloth/meta-llama-3.1-8b-bnb-4bit', revision=None, task_type=, inference_mode=False, r=16, target_modules={'k_proj', 'gate_proj', 'v_proj', 'o_proj', 'q_proj', 'down_proj', 'up_proj'}, lora_alpha=16, lora_dropout=0, fan_in_fan_out=False, bias='none', use_rslora=False, modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={}, use_dora=False, layer_replication=None, runtime_config=LoraRuntimeConfig(ephemeral_gpu_offload=False))\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploads:\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m environment details : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m filename : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m installed packages : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m model graph : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m notebook : 2\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m os packages : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m source_code : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n", + "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## ⚙ Inference" + ], + "metadata": { + "id": "0IdjUa_0pzPQ" + } + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model)\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"Continue the fibonnaci sequence.\", # instruction\n", + " \"1, 1, 2, 3, 5, 8\", # input\n", + " \"\", # output - leave this blank for generation\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "tokenizer.batch_decode(outputs)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "76su-yv9Aad0", + "outputId": "8b9c2ccd-4597-41fc-d9ef-e665272fae05" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + } + ] +} \ No newline at end of file From 15858bea34f953c783d5eef5490a2e759a0ace49 Mon Sep 17 00:00:00 2001 From: Abby Morgan <86856445+anmorgan24@users.noreply.github.com> Date: Thu, 5 Sep 2024 15:38:19 -0400 Subject: [PATCH 2/3] update authentication methods --- .../unsloth/notebooks/Comet_and_unsloth.ipynb | 8146 +++++++++-------- 1 file changed, 4074 insertions(+), 4072 deletions(-) diff --git a/integrations/model-training/unsloth/notebooks/Comet_and_unsloth.ipynb b/integrations/model-training/unsloth/notebooks/Comet_and_unsloth.ipynb index a563f93..3ef0557 100644 --- a/integrations/model-training/unsloth/notebooks/Comet_and_unsloth.ipynb +++ b/integrations/model-training/unsloth/notebooks/Comet_and_unsloth.ipynb @@ -1,4067 +1,620 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true, - "machine_shape": "hm", - "gpuType": "A100" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "6RF2EQaKDoxr" + }, + "source": [ + "\n", + " \n", + "" + ] }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + { + "cell_type": "markdown", + "metadata": { + "id": "KDjO5WbeDtp0" + }, + "source": [ + "# Comet and unsloth\n", + "\n", + "[Comet](https://www.comet.com/site/?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth) is an MLOps platform designed to help data scientists and teams build better models faster! Comet provides tooling to track, explain, manage, and monitor your models in a single place! It works with Jupyter notebooks and scripts and-- most importantly--it's 100% free to get started!\n", + "\n", + "[unsloth](https://github.com/unslothai/unsloth) dramatically improves the speed and efficiency of LLM fine-tuning for models including Llama, Phi-3, Gemma, Mistral, and more. For a full listed of 100+ supported unsloth models, [see here](https://huggingface.co/unsloth).\n", + "\n", + "Instrument your torchtune training runs with Comet to start managing experiments with efficiency, reproducibility, and collaboration in mind.\n", + "\n", + "Find more information about [our integration with torchtune here](https://www.comet.com/docs/v2/integrations/third-party-tools/unsloth?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth) or [learn about our other integrations here](https://www.comet.com/docs/v2/integrations?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth)." + ] }, - "language_info": { - "name": "python" + { + "cell_type": "markdown", + "metadata": { + "id": "f0fhpJZgsYII" + }, + "source": [ + "## ⚙ Install and import dependencies" + ] }, - "accelerator": "GPU", - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "77caf8ce2875463f86cb7e5b0e02d10f": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "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_986a13810ebf4e11ac2d636013efc948", - "IPY_MODEL_0902e41471fb40f7a7fb807397d1e780", - "IPY_MODEL_ec2615ff447e4a72a359143122a58887" - ], - "layout": "IPY_MODEL_bb0052241a574fdbb6ca8c942d99e1eb" - } - }, - "986a13810ebf4e11ac2d636013efc948": { - "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_1aff4958133640838a260a881e84bd66", - "placeholder": "​", - "style": "IPY_MODEL_b920a8e467d64b629189d1c76541cd17", - "value": "model.safetensors: 100%" - } - }, - "0902e41471fb40f7a7fb807397d1e780": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "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": "danger", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_0fe442a6a70948a4a9b703352148d780", - "max": 5702746390, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_5bf1ce41bf6c4b389bac20669bd64e3b", - "value": 5702745847 - } - }, - "ec2615ff447e4a72a359143122a58887": { - "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_7d06574680cb41b9bfac0c3e47916d6e", - "placeholder": "​", - "style": "IPY_MODEL_b6cd239b54c345feb11143f2364fcfaf", - "value": " 5.70G/5.70G [00:13<00:00, 698MB/s]" - } + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "5zTSjY9r4cGc" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install comet_ml \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", + "\n", + "# check torch version for Xformers (2.3 -> 0.0.27)\n", + "from torch import __version__; from packaging.version import Version as V\n", + "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n", + "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - "bb0052241a574fdbb6ca8c942d99e1eb": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "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, - "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 - } + "id": "p8GTqJOW88fI", + "outputId": "1c7f76ee-a9ff-418d-8067-4cb357f938e5" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Valid Comet API Key saved in /root/.comet.config (set COMET_CONFIG to change where it is saved).\n", + "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n", + "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/examples/comet-example-unsloth/cb280e1a2ac942cbab2343349d80282f\n", + "\n" + ] + } + ], + "source": [ + "import comet_ml\n", + "\n", + "comet_ml.login()\n", + "exp = comet_ml.Experiment(project_name=\"comet-example-unsloth\", workspace=\"examples\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5bfxeJqKwpKj" + }, + "source": [ + "## ⚙ Download model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - "1aff4958133640838a260a881e84bd66": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "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, - "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 - } + "id": "bZei5Dvw7_kM", + "outputId": "62f7a016-6c2b-4f76-ebf2-f2051b907932" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n" + ] + } + ], + "source": [ + "from unsloth import FastLanguageModel\n", + "import torch\n", + "\n", + "max_seq_length = 2048\n", + "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + "load_in_4bit = True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NG--Sbr1n5Cv" + }, + "source": [ + "Find the full list of [100+ supported unsloth models here](https://huggingface.co/unsloth). For a full list of supported 4-bit models see [here](https://huggingface.co/collections/unsloth/load-4bit-models-4x-faster-659042e3a41c3cbad582e734)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284, + "referenced_widgets": [ + "77caf8ce2875463f86cb7e5b0e02d10f", + "986a13810ebf4e11ac2d636013efc948", + "0902e41471fb40f7a7fb807397d1e780", + "ec2615ff447e4a72a359143122a58887", + "bb0052241a574fdbb6ca8c942d99e1eb", + "1aff4958133640838a260a881e84bd66", + "b920a8e467d64b629189d1c76541cd17", + "0fe442a6a70948a4a9b703352148d780", + "5bf1ce41bf6c4b389bac20669bd64e3b", + "7d06574680cb41b9bfac0c3e47916d6e", + "b6cd239b54c345feb11143f2364fcfaf", + "e38214ae388e4fdca976e2adeb30d20c", + "85d9ede0e7064521bfe7a92d54df211f", + "740857a56a134b2dba327dc8bdbc974a", + "725534abda3c462188fa27a978bfb59d", + "c61150dc57cb4279b38064e1bd82b3bc", + "6472e21298f44e6e9c5a3eefcedc59fc", + "adb028ffb1c045f9ade7be249a33b375", + "a1a32114bd2d4cb8882afebf56d64d41", + "8c6ee78067c0490292ee7845dd886a1b", + "6d28c2bb843a48908827b362ad094183", + "41d5a34977dd4488ba5b88c308301fe6", + "d0ce911bc4c845e88168f32781cf7923", + "718199222cfc412e9e1ee351b45c9db6", + "dcde9dfd56de417fb2057b3ccc9725f1", + "c749164ce965417db5411d00f9401623", + "61a290abfcb14c4f8b6cd9e453600bc5", + "7cce70a2af1a4fc2a04658264c37b0c7", + "096fb8d33b804a02b12bc085fa3c372f", + "c6345c375f474aaf88b7b73e662b20f2", + "b68053e4411747b0b12ab63d309aa765", + "76664f2e75744dbf875de8fd4fdbe2d4", + "05891b9099734df397dc8bb7396ef3e2", + "01f7a6c4eec04fc9b063add3719c0777", + "e4f2492f92a74794aec11bf7e2e8d554", + "cba30ecbcad14542b3acc82e4396275c", + "249258f9a1764d40a440a5846227059b", + "53c336629bca4e6cacfe69ffa316890a", + "b7c0946c9f1b40d5b469e28149808565", + "1bc726cc52084737a0269d01f9df6a16", + "51b3596a668742acab22e5a6659d2c55", + "30d75e70fb6e4c4db53137804d55295d", + "2c6dd56cf2f740cca814d5c6fa504d93", + "55e22c9351de46c4a192008dde020393", + "c333e3f23b8f4df6a7fd9f7a000a54ab", + "903f318a80d849098b11451a8c855d50", + "6b7e50badd5b4c7b979d20d7ffd1b132", + "d83d854a33ec4adabc9bef9c2b699a0b", + "1be75bb752214bdc8a3ef6e6976f3312", + "d03ff62190934187be5be033a6928545", + "1ec6fa8d08e642059f1d673f6260169f", + "23351e3d7e8741a1adad4c1292dab841", + "33c4f5595d924cd3a6d8c1575815a5c8", + "6c4cf50fd9ee484396e8272591d21ee9", + "124e70720a374644a6d360926059911f" + ] }, - "b920a8e467d64b629189d1c76541cd17": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } + "id": "L09m09Xs8Y2e", + "outputId": "4c67ce27-09cd-44dd-e987-7168a29ee335" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==((====))== Unsloth 2024.8: Fast Llama patching. Transformers = 4.44.2.\n", + " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform = Linux.\n", + "O^O/ \\_/ \\ Pytorch: 2.4.0+cu121. CUDA = 8.0. CUDA Toolkit = 12.1.\n", + "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.27.post2. 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" + ] }, - "0fe442a6a70948a4a9b703352148d780": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "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, - "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 - } + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "77caf8ce2875463f86cb7e5b0e02d10f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model.safetensors: 0%| | 0.00/5.70G [00:00\n", - " \n", - "" - ], - "metadata": { - "id": "6RF2EQaKDoxr" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Comet and unsloth\n", - "\n", - "[Comet](https://www.comet.com/site/?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth) is an MLOps platform designed to help data scientists and teams build better models faster! Comet provides tooling to track, explain, manage, and monitor your models in a single place! It works with Jupyter notebooks and scripts and-- most importantly--it's 100% free to get started!\n", - "\n", - "[unsloth](https://github.com/unslothai/unsloth) dramatically improves the speed and efficiency of LLM fine-tuning for models including Llama, Phi-3, Gemma, Mistral, and more. For a full listed of 100+ supported unsloth models, [see here](https://huggingface.co/unsloth).\n", - "\n", - "Instrument your torchtune training runs with Comet to start managing experiments with efficiency, reproducibility, and collaboration in mind.\n", - "\n", - "Find more information about [our integration with torchtune here](https://www.comet.com/docs/v2/integrations/third-party-tools/unsloth?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth) or [learn about our other integrations here](https://www.comet.com/docs/v2/integrations?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth)." - ], - "metadata": { - "id": "KDjO5WbeDtp0" - } - }, - { - "cell_type": "markdown", - "source": [ - "## ⚙ Install and import dependencies" - ], - "metadata": { - "id": "f0fhpJZgsYII" - } - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "5zTSjY9r4cGc" - }, - "outputs": [], - "source": [ - "%%capture\n", - "!pip install comet_ml \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", - "\n", - "# check torch version for Xformers (2.3 -> 0.0.27)\n", - "from torch import __version__; from packaging.version import Version as V\n", - "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n", - "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton" - ] - }, - { - "cell_type": "code", - "source": [ - "import comet_ml\n", - "import os\n", - "from google.colab import userdata\n", - "\n", - "# Log in to Comet\n", - "COMET_API_KEY = userdata.get('COMET_API_KEY')\n", - "comet_ml.login(api_key = COMET_API_KEY)\n", - "# Create experiment object\n", - "exp = comet_ml.Experiment(project_name=\"comet-example-unsloth\", workspace=\"examples\")\n", - "\n", - "# Set Hugging Face token (for dataset retrieval)\n", - "HF_TOKEN = userdata.get('HF_TOKEN')\n", - "os.environ[\"HF_TOKEN\"] = HF_TOKEN" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "p8GTqJOW88fI", - "outputId": "1c7f76ee-a9ff-418d-8067-4cb357f938e5" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Valid Comet API Key saved in /root/.comet.config (set COMET_CONFIG to change where it is saved).\n", - "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n", - "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/examples/comet-example-unsloth/cb280e1a2ac942cbab2343349d80282f\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## ⚙ Download model" - ], - "metadata": { - "id": "5bfxeJqKwpKj" - } - }, - { - "cell_type": "code", - "source": [ - "from unsloth import FastLanguageModel\n", - "import torch\n", - "\n", - "max_seq_length = 2048\n", - "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", - "load_in_4bit = True" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bZei5Dvw7_kM", - "outputId": "62f7a016-6c2b-4f76-ebf2-f2051b907932" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Find the full list of [100+ supported unsloth models here](https://huggingface.co/unsloth). For a full list of supported 4-bit models see [here](https://huggingface.co/collections/unsloth/load-4bit-models-4x-faster-659042e3a41c3cbad582e734)" - ], - "metadata": { - "id": "NG--Sbr1n5Cv" - } - }, - { - "cell_type": "code", - "source": [ - "model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name = \"unsloth/Meta-Llama-3.1-8B\",\n", - " max_seq_length = max_seq_length,\n", - " dtype = dtype,\n", - " load_in_4bit = load_in_4bit,\n", - " token = HF_TOKEN\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 284, - "referenced_widgets": [ - "77caf8ce2875463f86cb7e5b0e02d10f", - "986a13810ebf4e11ac2d636013efc948", - "0902e41471fb40f7a7fb807397d1e780", - "ec2615ff447e4a72a359143122a58887", - "bb0052241a574fdbb6ca8c942d99e1eb", - "1aff4958133640838a260a881e84bd66", - "b920a8e467d64b629189d1c76541cd17", - "0fe442a6a70948a4a9b703352148d780", - "5bf1ce41bf6c4b389bac20669bd64e3b", - "7d06574680cb41b9bfac0c3e47916d6e", - "b6cd239b54c345feb11143f2364fcfaf", - "e38214ae388e4fdca976e2adeb30d20c", - "85d9ede0e7064521bfe7a92d54df211f", - "740857a56a134b2dba327dc8bdbc974a", - "725534abda3c462188fa27a978bfb59d", - "c61150dc57cb4279b38064e1bd82b3bc", - "6472e21298f44e6e9c5a3eefcedc59fc", - "adb028ffb1c045f9ade7be249a33b375", - "a1a32114bd2d4cb8882afebf56d64d41", - "8c6ee78067c0490292ee7845dd886a1b", - "6d28c2bb843a48908827b362ad094183", - "41d5a34977dd4488ba5b88c308301fe6", - "d0ce911bc4c845e88168f32781cf7923", - "718199222cfc412e9e1ee351b45c9db6", - "dcde9dfd56de417fb2057b3ccc9725f1", - "c749164ce965417db5411d00f9401623", - "61a290abfcb14c4f8b6cd9e453600bc5", - "7cce70a2af1a4fc2a04658264c37b0c7", - "096fb8d33b804a02b12bc085fa3c372f", - "c6345c375f474aaf88b7b73e662b20f2", - "b68053e4411747b0b12ab63d309aa765", - "76664f2e75744dbf875de8fd4fdbe2d4", - "05891b9099734df397dc8bb7396ef3e2", - "01f7a6c4eec04fc9b063add3719c0777", - "e4f2492f92a74794aec11bf7e2e8d554", - "cba30ecbcad14542b3acc82e4396275c", - "249258f9a1764d40a440a5846227059b", - "53c336629bca4e6cacfe69ffa316890a", - "b7c0946c9f1b40d5b469e28149808565", - "1bc726cc52084737a0269d01f9df6a16", - "51b3596a668742acab22e5a6659d2c55", - "30d75e70fb6e4c4db53137804d55295d", - "2c6dd56cf2f740cca814d5c6fa504d93", - "55e22c9351de46c4a192008dde020393", - "c333e3f23b8f4df6a7fd9f7a000a54ab", - "903f318a80d849098b11451a8c855d50", - "6b7e50badd5b4c7b979d20d7ffd1b132", - "d83d854a33ec4adabc9bef9c2b699a0b", - "1be75bb752214bdc8a3ef6e6976f3312", - "d03ff62190934187be5be033a6928545", - "1ec6fa8d08e642059f1d673f6260169f", - "23351e3d7e8741a1adad4c1292dab841", - "33c4f5595d924cd3a6d8c1575815a5c8", - "6c4cf50fd9ee484396e8272591d21ee9", - "124e70720a374644a6d360926059911f" - ] - }, - "id": "L09m09Xs8Y2e", - "outputId": "4c67ce27-09cd-44dd-e987-7168a29ee335" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "==((====))== Unsloth 2024.8: Fast Llama patching. Transformers = 4.44.2.\n", - " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform = Linux.\n", - "O^O/ \\_/ \\ Pytorch: 2.4.0+cu121. CUDA = 8.0. CUDA Toolkit = 12.1.\n", - "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.27.post2. 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" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "model.safetensors: 0%| | 0.00/5.70G [00:00" - ], "text/html": [ "\n", "

\n", @@ -4319,17 +872,22 @@ " \n", " \n", "

" + ], + "text/plain": [ + "" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "trainer_stats = trainer.train()" ] }, { "cell_type": "code", - "source": [ - "exp.end()" - ], + "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -4337,11 +895,10 @@ "id": "8II72I92Bm9s", "outputId": "6518c199-dff2-4c4a-f821-aefdf595fa2c" }, - "execution_count": 13, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n", "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n", @@ -4622,19 +1179,42 @@ "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n" ] } + ], + "source": [ + "exp.end()" ] }, { "cell_type": "markdown", - "source": [ - "## ⚙ Inference" - ], "metadata": { "id": "0IdjUa_0pzPQ" - } + }, + "source": [ + "## ⚙ Inference" + ] }, { "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "76su-yv9Aad0", + "outputId": "8b9c2ccd-4597-41fc-d9ef-e665272fae05" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model)\n", @@ -4649,27 +1229,3449 @@ "\n", "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "A100", + "machine_shape": "hm", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + 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a/integrations/model-training/unsloth/notebooks/Comet_and_unsloth.ipynb b/integrations/model-training/unsloth/notebooks/Comet_and_unsloth.ipynb index 3ef0557..bc60fea 100644 --- a/integrations/model-training/unsloth/notebooks/Comet_and_unsloth.ipynb +++ b/integrations/model-training/unsloth/notebooks/Comet_and_unsloth.ipynb @@ -1,4677 +1,357 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "6RF2EQaKDoxr" - }, - "source": [ - "\n", - " \n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KDjO5WbeDtp0" - }, - "source": [ - "# Comet and unsloth\n", - "\n", - "[Comet](https://www.comet.com/site/?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth) is an MLOps platform designed to help data scientists and teams build better models faster! Comet provides tooling to track, explain, manage, and monitor your models in a single place! It works with Jupyter notebooks and scripts and-- most importantly--it's 100% free to get started!\n", - "\n", - "[unsloth](https://github.com/unslothai/unsloth) dramatically improves the speed and efficiency of LLM fine-tuning for models including Llama, Phi-3, Gemma, Mistral, and more. For a full listed of 100+ supported unsloth models, [see here](https://huggingface.co/unsloth).\n", - "\n", - "Instrument your torchtune training runs with Comet to start managing experiments with efficiency, reproducibility, and collaboration in mind.\n", - "\n", - "Find more information about [our integration with torchtune here](https://www.comet.com/docs/v2/integrations/third-party-tools/unsloth?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth) or [learn about our other integrations here](https://www.comet.com/docs/v2/integrations?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "f0fhpJZgsYII" - }, - "source": [ - "## ⚙ Install and import dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "5zTSjY9r4cGc" - }, - "outputs": [], - "source": [ - "%%capture\n", - "!pip install comet_ml \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", - "\n", - "# check torch version for Xformers (2.3 -> 0.0.27)\n", - "from torch import __version__; from packaging.version import Version as V\n", - "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n", - "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "p8GTqJOW88fI", - "outputId": "1c7f76ee-a9ff-418d-8067-4cb357f938e5" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Valid Comet API Key saved in /root/.comet.config (set COMET_CONFIG to change where it is saved).\n", - "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n", - "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/examples/comet-example-unsloth/cb280e1a2ac942cbab2343349d80282f\n", - "\n" - ] - } - ], - "source": [ - "import comet_ml\n", - "\n", - "comet_ml.login()\n", - "exp = comet_ml.Experiment(project_name=\"comet-example-unsloth\", workspace=\"examples\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from huggingface_hub import notebook_login\n", - "\n", - "notebook_login()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5bfxeJqKwpKj" - }, - "source": [ - "## ⚙ Download model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bZei5Dvw7_kM", - "outputId": "62f7a016-6c2b-4f76-ebf2-f2051b907932" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n" - ] - } - ], - "source": [ - "from unsloth import FastLanguageModel\n", - "import torch\n", - "\n", - "max_seq_length = 2048\n", - "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", - "load_in_4bit = True" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NG--Sbr1n5Cv" - }, - "source": [ - "Find the full list of [100+ supported unsloth models here](https://huggingface.co/unsloth). For a full list of supported 4-bit models see [here](https://huggingface.co/collections/unsloth/load-4bit-models-4x-faster-659042e3a41c3cbad582e734)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 284, - "referenced_widgets": [ - "77caf8ce2875463f86cb7e5b0e02d10f", - "986a13810ebf4e11ac2d636013efc948", - "0902e41471fb40f7a7fb807397d1e780", - "ec2615ff447e4a72a359143122a58887", - "bb0052241a574fdbb6ca8c942d99e1eb", - "1aff4958133640838a260a881e84bd66", - "b920a8e467d64b629189d1c76541cd17", - "0fe442a6a70948a4a9b703352148d780", - "5bf1ce41bf6c4b389bac20669bd64e3b", - "7d06574680cb41b9bfac0c3e47916d6e", - "b6cd239b54c345feb11143f2364fcfaf", - "e38214ae388e4fdca976e2adeb30d20c", - "85d9ede0e7064521bfe7a92d54df211f", - "740857a56a134b2dba327dc8bdbc974a", - "725534abda3c462188fa27a978bfb59d", - "c61150dc57cb4279b38064e1bd82b3bc", - "6472e21298f44e6e9c5a3eefcedc59fc", - "adb028ffb1c045f9ade7be249a33b375", - "a1a32114bd2d4cb8882afebf56d64d41", - "8c6ee78067c0490292ee7845dd886a1b", - "6d28c2bb843a48908827b362ad094183", - "41d5a34977dd4488ba5b88c308301fe6", - "d0ce911bc4c845e88168f32781cf7923", - "718199222cfc412e9e1ee351b45c9db6", - "dcde9dfd56de417fb2057b3ccc9725f1", - "c749164ce965417db5411d00f9401623", - "61a290abfcb14c4f8b6cd9e453600bc5", - "7cce70a2af1a4fc2a04658264c37b0c7", - "096fb8d33b804a02b12bc085fa3c372f", - "c6345c375f474aaf88b7b73e662b20f2", - "b68053e4411747b0b12ab63d309aa765", - "76664f2e75744dbf875de8fd4fdbe2d4", - "05891b9099734df397dc8bb7396ef3e2", - "01f7a6c4eec04fc9b063add3719c0777", - "e4f2492f92a74794aec11bf7e2e8d554", - "cba30ecbcad14542b3acc82e4396275c", - "249258f9a1764d40a440a5846227059b", - "53c336629bca4e6cacfe69ffa316890a", - "b7c0946c9f1b40d5b469e28149808565", - "1bc726cc52084737a0269d01f9df6a16", - "51b3596a668742acab22e5a6659d2c55", - "30d75e70fb6e4c4db53137804d55295d", - "2c6dd56cf2f740cca814d5c6fa504d93", - "55e22c9351de46c4a192008dde020393", - "c333e3f23b8f4df6a7fd9f7a000a54ab", - "903f318a80d849098b11451a8c855d50", - "6b7e50badd5b4c7b979d20d7ffd1b132", - "d83d854a33ec4adabc9bef9c2b699a0b", - "1be75bb752214bdc8a3ef6e6976f3312", - "d03ff62190934187be5be033a6928545", - "1ec6fa8d08e642059f1d673f6260169f", - "23351e3d7e8741a1adad4c1292dab841", - "33c4f5595d924cd3a6d8c1575815a5c8", - "6c4cf50fd9ee484396e8272591d21ee9", - "124e70720a374644a6d360926059911f" - ] - }, - "id": "L09m09Xs8Y2e", - "outputId": "4c67ce27-09cd-44dd-e987-7168a29ee335" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==((====))== Unsloth 2024.8: Fast Llama patching. Transformers = 4.44.2.\n", - " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform = Linux.\n", - "O^O/ \\_/ \\ Pytorch: 2.4.0+cu121. CUDA = 8.0. CUDA Toolkit = 12.1.\n", - "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.27.post2. 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": "77caf8ce2875463f86cb7e5b0e02d10f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "model.safetensors: 0%| | 0.00/5.70G [00:00\n", - " \n", - " \n", - " [60/60 01:41, Epoch 0/1]\n", - "

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StepTraining Loss
11.818600
22.305800
31.703400
42.014700
51.735100
61.670600
71.259200
81.308900
91.165100
101.225300
110.952900
120.975100
130.927200
141.053400
150.893100
160.907500
171.010400
181.262300
191.024500
200.884400
210.945600
221.019000
230.899700
240.994800
251.078800
261.021300
271.048400
280.883900
290.849500
300.894900
310.857800
320.866900
330.987500
340.860600
350.959300
360.860900
370.882400
380.756700
391.090100
401.174900
410.893200
420.981300
430.954800
440.911400
450.918000
460.973800
470.872000
481.198100
490.909500
501.032100
511.017500
520.909200
530.976500
541.154300
550.781000
561.012800
570.885200
580.828500
590.853500
600.898100

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "trainer_stats = trainer.train()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8II72I92Bm9s", - "outputId": "6518c199-dff2-4c4a-f821-aefdf595fa2c" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Data:\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m display_summary_level : 1\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m name : apparent_pagoda_1033\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m url : https://www.comet.com/examples/comet-example-unsloth/cb280e1a2ac942cbab2343349d80282f\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Metrics [count] (min, max):\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m epoch [61] : (0.0001545595054095827, 0.00927357032457496)\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m grad_norm [60] : (0.2999851107597351, 2.063112258911133)\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m learning_rate [60] : (0.0, 0.0002)\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m loss [60] : (0.7567, 2.3058)\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m total_flos : 5726714157219840.0\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/epoch [61] : (0.0001545595054095827, 0.00927357032457496)\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/grad_norm [60] : (0.2999851107597351, 2.063112258911133)\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/learning_rate [60] : (0.0, 0.0002)\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/loss [60] : (0.7567, 2.3058)\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/total_flos : 5726714157219840.0\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_loss : 1.0665242771307628\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_runtime : 109.8838\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_samples_per_second : 4.368\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_steps_per_second : 0.546\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_loss : 1.0665242771307628\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_runtime : 109.8838\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_samples_per_second : 4.368\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_steps_per_second : 0.546\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Others:\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m hasNestedParams : True\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Parameters:\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|dispatch_batches : None\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|even_batches : True\n", - "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|gradient_accumulation_kwargs : None\n", - 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Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alpaca_prompt = Copied from above\n", - "FastLanguageModel.for_inference(model)\n", - "inputs = tokenizer(\n", - "[\n", - " alpaca_prompt.format(\n", - " \"Continue the fibonnaci sequence.\", # instruction\n", - " \"1, 1, 2, 3, 5, 8\", # input\n", - " \"\", # output - leave this blank for generation\n", - " )\n", - "], return_tensors = \"pt\").to(\"cuda\")\n", - "\n", - "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", - "tokenizer.batch_decode(outputs)" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "A100", - "machine_shape": "hm", - "provenance": [], - 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Comet provides tooling to track, explain, manage, and monitor your models in a single place! It works with Jupyter notebooks and scripts and-- most importantly--it's 100% free to get started!\n", + "\n", + "[unsloth](https://github.com/unslothai/unsloth) dramatically improves the speed and efficiency of LLM fine-tuning for models including Llama, Phi-3, Gemma, Mistral, and more. For a full listed of 100+ supported unsloth models, [see here](https://huggingface.co/unsloth).\n", + "\n", + "Instrument your torchtune training runs with Comet to start managing experiments with efficiency, reproducibility, and collaboration in mind.\n", + "\n", + "Find more information about [our integration with torchtune here](https://www.comet.com/docs/v2/integrations/third-party-tools/unsloth?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth) or [learn about our other integrations here](https://www.comet.com/docs/v2/integrations?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f0fhpJZgsYII" + }, + "source": [ + "## ⚙ Install and import dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5zTSjY9r4cGc" + }, + "outputs": [], + "source": [ + "%pip install comet_ml \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\" \"torch>=2.4.0\" xformers trl peft accelerate bitsandbytes triton" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "p8GTqJOW88fI" + }, + "outputs": [], + "source": [ + "import comet_ml\n", + "\n", + "comet_ml.login()\n", + "exp = comet_ml.Experiment(project_name=\"comet-example-unsloth\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vdOozTnqq8pL" + }, + "outputs": [], + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5bfxeJqKwpKj" + }, + "source": [ + "## ⚙ Download model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bZei5Dvw7_kM" + }, + "outputs": [], + "source": [ + "from unsloth import FastLanguageModel\n", + "import torch\n", + "\n", + "max_seq_length = 2048\n", + "dtype = (\n", + " None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + ")\n", + "load_in_4bit = True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NG--Sbr1n5Cv" + }, + "source": [ + "Find the full list of [100+ supported unsloth models here](https://huggingface.co/unsloth). For a full list of supported 4-bit models see [here](https://huggingface.co/collections/unsloth/load-4bit-models-4x-faster-659042e3a41c3cbad582e734)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "L09m09Xs8Y2e" + }, + "outputs": [], + "source": [ + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name=\"unsloth/Meta-Llama-3.1-8B\",\n", + " max_seq_length=max_seq_length,\n", + " dtype=dtype,\n", + " load_in_4bit=load_in_4bit,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tbcH3PNSwRvy" + }, + "source": [ + "## ⚙ Add LoRA adapters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BnXS0Trn8md9" + }, + "outputs": [], + "source": [ + "model = FastLanguageModel.get_peft_model(\n", + " model,\n", + " r=16, # Suggested 8, 16, 32, 64, 128\n", + " target_modules=[\n", + " \"q_proj\",\n", + " \"k_proj\",\n", + " \"v_proj\",\n", + " \"o_proj\",\n", + " \"gate_proj\",\n", + " \"up_proj\",\n", + " \"down_proj\",\n", + " ],\n", + " lora_alpha=16,\n", + " lora_dropout=0, # Supports any, but = 0 is optimized\n", + " bias=\"none\", # Supports any, but = \"none\" is optimized\n", + " use_gradient_checkpointing=\"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state=3407,\n", + " use_rslora=False, # rank stabilized LoRA\n", + " loftq_config=None, # LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AoqQQ5n4wcfx" + }, + "source": [ + "## ⚙ Data preparation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "z9XFfoIz8sTI" + }, + "outputs": [], + "source": [ + "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "{}\n", + "\n", + "### Input:\n", + "{}\n", + "\n", + "### Response:\n", + "{}\"\"\"\n", + "\n", + "EOS_TOKEN = tokenizer.eos_token # add EOS_TOKEN\n", + "\n", + "\n", + "def formatting_prompts_func(examples):\n", + " instructions = examples[\"instruction\"]\n", + " inputs = examples[\"input\"]\n", + " outputs = examples[\"output\"]\n", + " texts = []\n", + " for instruction, input, output in zip(instructions, inputs, outputs):\n", + " # add EOS_TOKEN, otherwise your generation will go on forever\n", + " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", + " texts.append(text)\n", + " return {\n", + " \"text\": texts,\n", + " }\n", + "\n", + "\n", + "pass\n", + "\n", + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"yahma/alpaca-cleaned\", split=\"train\")\n", + "dataset = dataset.map(\n", + " formatting_prompts_func,\n", + " batched=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wljLKG7LwiHE" + }, + "source": [ + "## ⚙ Training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ra62g_5f8vYo" + }, + "outputs": [], + "source": [ + "from trl import SFTTrainer\n", + "from transformers import TrainingArguments\n", + "from unsloth import is_bfloat16_supported\n", + "\n", + "trainer = SFTTrainer(\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " train_dataset=dataset,\n", + " dataset_text_field=\"text\",\n", + " max_seq_length=max_seq_length,\n", + " dataset_num_proc=2,\n", + " packing=False, # Can make training 5x faster for short sequences.\n", + " args=TrainingArguments(\n", + " per_device_train_batch_size=2,\n", + " gradient_accumulation_steps=4,\n", + " warmup_steps=5,\n", + " # num_train_epochs = 1, # Set this for 1 full training run.\n", + " max_steps=60,\n", + " learning_rate=2e-4,\n", + " fp16=not is_bfloat16_supported(),\n", + " bf16=is_bfloat16_supported(),\n", + " logging_steps=1,\n", + " optim=\"adamw_8bit\",\n", + " weight_decay=0.01,\n", + " lr_scheduler_type=\"linear\",\n", + " seed=3407,\n", + " output_dir=\"outputs\",\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "e5EHhd5zANxX" + }, + "outputs": [], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8II72I92Bm9s" + }, + "outputs": [], + "source": [ + "exp.end()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0IdjUa_0pzPQ" + }, + "source": [ + "## ⚙ Inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "76su-yv9Aad0" + }, + "outputs": [], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model)\n", + "inputs = tokenizer(\n", + " [\n", + " alpaca_prompt.format(\n", + " \"Continue the fibonnaci sequence.\", # instruction\n", + " \"1, 1, 2, 3, 5, 8\", # input\n", + " \"\", # output - leave this blank for generation\n", + " )\n", + " ],\n", + " return_tensors=\"pt\",\n", + ").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)\n", + "tokenizer.batch_decode(outputs)" + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "h6alQJYpwlAM" + }, + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "L4", + "machine_shape": "hm", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file