{ "cells": [ { "cell_type": "markdown", "source": [ "To run this, press \"Runtime\" and press \"Run all\" on a A100 Colab instance!\n", "\n", "**[NOTE]** You might be lucky if an A100 is free!. If not, try our Mistral 7b notebook on a free Tesla T4 [here](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing).\n", "
\n", " \n", " \n", " Join our Discord if you need help!\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\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)." ], "metadata": { "id": "8LKIA_qnVKOz" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "import torch\n", "major_version, minor_version = torch.cuda.get_device_capability()\n", "if major_version >= 8:\n", " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", " !pip install \"unsloth[colab-ampere] @ git+https://github.com/unslothai/unsloth.git\"\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n", "pass" ] }, { "cell_type": "markdown", "source": [ "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n", "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\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**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models." ], "metadata": { "id": "r2v_X2fA0Df5" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 680, "referenced_widgets": [ "38edc6260d3d4df8a3526fce50e2c22d", "cc5e3f3f8ed04ab48ce13ac28dedd058", "8222f693a5294d90abcdee3ed7fa9019", "5ed6638ea6744e7b8474e89584968dcf", "3d93b3012a284f39b5be351911216726", "70a9afb334484d118b12b22ba994d087", "59612d3e90744488aadffe03ccfb31c2", "f8c73e5e3c1b4811875cd7389ab475ff", "3bc8d653c14d4c4ea39b915e7e72779a", "3b8b6836ec4545b99af7809edb6c49bf", "f7a57a9a9c70452db0bfd9882a6d7b58", "19501ac6292840538ce2ce6001925dba", "506e1fc398cf4973af416363d3fa9f6d", "3f808b73b7aa4bf3ae9578016eaaab73", "aa6f9517e6a2420095315f8cc96ca54e", "5bbd0c1595124b67828c755c7a5ca278", "8d9d5702ee394c828c4de21122ebac42", "d41de69be4e542ea90667c4d4bb35937", "b577ffa97afe48ff84dff0712df5e7c0", "2c95b388d8804971bacdfa13e09c55af", 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"228cad28-ddf8-499b-f910-393d0db2abff" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:67: UserWarning: CUDA is not linked properly.\n", "We shall run `ldconfig /usr/lib64-nvidia` to try to fix it.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.10k [00:00 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 = 16,\n", " lora_dropout = 0, # Currently only supports dropout = 0\n", " bias = \"none\", # Currently only supports bias = \"none\"\n", " use_gradient_checkpointing = True,\n", " random_state = 3407,\n", " max_seq_length = max_seq_length,\n", ")" ] }, { "cell_type": "markdown", "source": [ "\n", "### Data Prep\n", "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", "\n", "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only)." ], "metadata": { "id": "vITh0KVJ10qX" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LjY75GoYUCB8", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "60325a0f16b74740b7691e55d6216e7e", "fafe2a3793604108a70248a5021de7ad", "60c8ac5e74e148b7a1a7764bee36a78e", "3e600f9d8693456296795bce2a04d93c", "e568aca00f1c4df29605c5fb4cffb8e2", "b6c9c26a0c01432293e7543713486e8c", "ef6da120a7b6427297f12cb113712d7e", "e963e75f83574245addbc6871ec297e9", "9fa0cf6595d24393af8418868479a9af", "dd0dfe23311d43b9a6a440e378dbdfdc", "364eda2feddf40d7bfa29f6305a6b9a5", "0ed378352a3b4335ad5e16661f819e76", "e9aff227c9304948ac13082b18f4437b", "ee6c543c2f704ac2bec92c1c4eadc014", "79c0518d21c14b1a9111be6ad80297cc", "5071d53d22e8462c91c6d467799fbe9f", "7bbfa22f55ae46e089e60ab70bfc5269", "5827e21b0e9f4d10a0e73642ace02c42", "131adc0c9aff46028d8a7f50a4456007", "2233be32641546e5a564e4721e9d2ccf", "e0169d69ab264d0a95133698096ac326", "e4ae90131aa34014a2bde045b90f32e1", "b0f0442c730f4a378cc6cc1ac73beccc", "05cdd4bba2b54a228b85e1c0c25aa418", "7482f645b5b44734a830c8dd4984f156", "679e258497d74b989f90bf1e2e5dbd3c", "2b070d41281e4dfc95931073939b8d0a", "3c1fba9479cd4c1a923d5493bcfb0804", "27639a82221c46b3ad784ed86d30d7cd", "4ec10e9db42e4c3893be6c42577024b1", "38e1d8c1b5514968bcb7d5316c14334c", "5997add21ec646c8b6a05e3b5c05b430", "76f626e94a53499c86a0fb335ed4536c", "1aa9cc3be5b84424b434704ac2d0e452", "cdec8776bc7448e1bc75240599ab4bef", "10c18fd30c30491996e174f1dc073472", "96454fd2977440b68f7bb17f840cfa46", "4d41410eae934e8b95f3c4aaf6321778", "c33822edadeb42ba81f65371b4fd05ac", "1d6abd1240b246e980b5c9cd26d61bb1", "50813fb5c76948b5a2c8e7dae9279123", "b19a86a7a4434a019dde447aff62a50a", "546b7ff40db94679ba63c8f1b46bc300", "6e6b4968826e4f849ae89eb700ac907c" ] }, "cellView": "form", "outputId": "519d3314-a9c2-49c1-8c50-777fa7eba922" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/11.6k [00:00\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`!" ], "metadata": { "id": "idAEIeSQ3xdS" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/", "height": 87, "referenced_widgets": [ "4d94b7ee742543d091ee2707b6ef2e6b", "fb1d2e26443b4132bbb375b5b40e0b89", "467223714c1144528663cf3147d032d0", "e230f4fd008b4a49a4df7665b79149ba", "ab66d7003a0a406696be5d425b9a169e", "9063770bf8c9474d98971d8f2884e122", "643d3d9738934620bf40ae415555bd95", "26a04e0bf6564d94815524483fa6391b", "75714fe7db204cc48e588ce067e0bdeb", "b6fb5174e8a745d691f3e7ce745f8257", "47d3dbae5f674f7bab64078e455a6b79" ] }, "outputId": "4ff59879-eeda-4890-d4bc-73bb57473cd0" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/51760 [00:00" ], "text/html": [ "\n", "
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StepTraining Loss
11.589300
21.634000
31.608500
41.398300
51.652000
61.335000
71.408300
81.282900
91.352700
101.055300
111.100300
120.997700
131.052700
140.966900
150.882100
160.863700
170.846300
180.886200
190.724900
201.072100
210.856300
220.827600
230.875100
240.937700
250.886100
260.885200
270.992800
280.848600
290.774600
300.801600
310.886200
320.886600
330.833800
340.957000
350.901700
360.917500
370.781800
380.814900
390.758600
400.912400
410.712300
420.858600
430.893100
440.927200
450.899200
461.005500
470.866800
480.814000
490.849100
500.867000
510.955600
520.864200
530.840900
540.875000
550.762000
560.730200
570.803000
580.753700
590.841400
600.881800
610.750100
620.832500
630.826000
640.865600
650.825900
660.814600
670.889700
680.842100
690.877200
700.755900
710.877100
720.821600
730.871400
740.982700
750.903600
760.782300
770.867600
780.815300
790.887600
800.859300
810.892100
820.865000
830.795000
840.800200
850.785900
860.913400
870.862100
880.810600
890.901600
900.834600
910.836400
920.772500
930.894400
940.808900
950.792700
960.765500
970.877600
980.808100
990.776400
1000.846200
1010.921600
1020.873600
1030.861900
1040.769600
1050.747000
1060.918800
1070.816800
1080.803500
1090.860400
1100.814100
1110.861400
1120.922300
1130.734800
1140.815300
1150.821500
1160.744900
1170.951700
1180.809700
1190.814000
1200.864500

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pCqnaKmlO1U9", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "0b20db34-7fdf-4c66-8660-e03472c1844a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1020.2541 seconds used for training.\n", "17.0 minutes used for training.\n", "Peak reserved memory = 23.98 GB.\n", "Peak reserved memory for training = 6.189 GB.\n", "Peak reserved memory % of max memory = 60.611 %.\n", "Peak reserved memory for training % of max memory = 15.643 %.\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", "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!" ], "metadata": { "id": "ekOmTR1hSNcr" } }, { "cell_type": "code", "source": [ "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", "]*1, return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { "id": "kR3gIAX-SM2q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7fe95bbd-084d-49ea-be1f-4a4c2e06d853" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1547: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n", " warnings.warn(\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[' 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:\\n1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393']" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "markdown", "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", "\n", "To save to `GGUF` / `llama.cpp`, or for model merging, use `model.merge_and_unload` first, then save the model. Maxime Labonne's [llm-course](https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html) has a nice tutorial on converting HF to GGUF! This [issue](https://github.com/ggerganov/llama.cpp/issues/3097) might be helpful for more info." ], "metadata": { "id": "uMuVrWbjAzhc" } }, { "cell_type": "code", "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "# model.push_to_hub(\"your_name/lora_model\") # Online saving" ], "metadata": { "id": "upcOlWe7A1vc" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now if you want to load the LoRA adapters we just saved, we can!" ], "metadata": { "id": "AEEcJ4qfC7Lp" } }, { "cell_type": "code", "source": [ "from peft import PeftModel\n", "model = PeftModel.from_pretrained(model, \"lora_model\")" ], "metadata": { "id": "MKX_XKs_BNZR" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Finally, we can now do some inference on the loaded model." ], "metadata": { "id": "f8pvYYN9DvbN" } }, { "cell_type": "code", "source": [ "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"Complete the Python program:\", # instruction\n", " \"def fibonnaci(x):\", # input\n", " \"\", # output\n", " )\n", "]*1, return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qWyyUoDq71jt", "outputId": "45d302e6-60e0-4510-9955-82537ad8accd" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[' 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:\\nComplete the Python program:\\n\\n### Input:\\ndef fibonnaci(x):\\n\\n### Response:\\n```\\ndef fibonnaci(x):\\n if x == 0:\\n return 0\\n elif x == 1:\\n return 1\\n else:\\n return fibonnaci(x-1) + fibonnaci(x-2)\\n```\\n\\n### Explanation:\\nThe program is a recursive function that returns the nth fibonnaci number.\\n\\n### Instruction:\\nComplete the Python program:\\n\\n### Input:\\ndef fibonnaci(x):\\n\\n### Response:\\n```\\ndef fibonnaci(x']" ] }, "metadata": {}, "execution_count": 13 } ] }, { "cell_type": "markdown", "source": [ "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", "\n", "We also have other notebooks on:\n", "1. Zephyr DPO [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n", "2. Llama 7b [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n", "3. TinyLlama full Alpaca 52K in under 80 hours [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n", "4. Mistral 7b [free Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)\n", "5. Llama 7b [free Kaggle](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp)\n", "\n", "

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