{ "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 + support us if you can!\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).\n", "\n", "**[NEW] Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "import torch\n", "major_version, minor_version = torch.cuda.get_device_capability()\n", "# Must install separately since Colab has torch 2.2.1, which breaks packages\n", "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\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 --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install --no-deps xformers trl peft accelerate bitsandbytes\n", "pass\n", "# Llama 3 Video Tutorial https://www.youtube.com/watch?v=aQmoog_s8HE" ] }, { "cell_type": "markdown", "metadata": { "id": "r2v_X2fA0Df5" }, "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." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 566, "referenced_widgets": [ "ce3d42aef9634825b6393290d7e97720", "89bcff3f688246e3a5697dd64f86aaac", "a1df5feb54fb4ad0974e8befe5c19dc7", "b8a456cf4b994f1ca8c416d3da8a661b", "a3bb17d2790341b3902943aae8cfc732", "8b5f20c19ed3476ca6950a37fcbab209", "b92c2e19db2646fd823b1a52b45bdd25", "9038321c7f4c445aab26101fd6e9d066", "c47737a6f71b4c94a9dc9976bcf08c16", "faa75e00a14546cfb71431e394135185", "e5bc9e01e04b4d30af0aaab6ccb8d6dc", "ebb9edaa79c6478da95da1546d625229", "4db6097654b44d2fbd95e42f8c151719", "45a37167201643d1b88a8792676260b1", "07909833d2324cfd84b020f7747aa263", "aa40f54ac2ab4a2f9b85f5dcd825910d", "62388c4c9ef7420e8f4161ade6c88073", "97e79cc54b174cc0b505d2838e1358d0", "cdd76cddb8c54cff97c014eb359bacff", "4241318a3e4646baaf36a31ff70167ba", "6cf9a343c040495a952180a57d2bb72b", 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"1f6637accbc243e887769b2200bc4365", "b2ace04c4e9a4856b0e37d991be66195", "b3395943294f4fa5936d50d0957e5db1", "e7126fc8e94643d4a7f392b061bdb097", "257c1d7ec8a24c559beddee55dad7b68", "b98f128b6e6a46888dc9650dfd87211f", "2f966ce562274b018e7397979f1a0e84", "57b0ea63d79a4b38b6e2d1f74ea03493", "bc8c46759ce2498e8c13ad151b23f817", "ca8f1fe9376a4335b7f3eb3c9f5786b0", "b79971d4338a46299027e0526257f8bd", "03640a692c3848adb82fb960917d48f1", "e32f5775f36641bbba742befe9ea595c", "f453577430d54a6884926c66710a167f", "a8c63065c72f4b30ac0c4e7d59ff234b", "c3ed7d4ac01f4035a55998eca5f9aa8b", "2cb4c344311042929dde166d9e8658b4", "84039daf8a6a4a768a6be3392f1e9e6c", "cc77ae64bbda4d86b7ce6bc04582fa7d", "a057d365da534ac79ddcb07fdd235a1d", "e6de27c0a39e423db4ff80dde721cfad", "d142740e2e7148209ce79d54327bfbdd", "8d9672ce10eb49069036e2269728cda3", "ea10cad6d2a24a0ba8ac56e9ca2dda6f", "48a48cd00ee34eaaab485256387c73cf", "17328686c8fb4f82b125989e4b997a3d", "6c0fe8ead8c04b5db967189e416a04dc", "75babe22dde24cd2b7b07f8b8d24996e", "205eb758b54e44f88e795e326527c3d7", "01b8c0163fbc4fb28df82a8c4b0a6d19" ] }, "id": "QmUBVEnvCDJv", "outputId": "f942db26-fb91-469e-8b37-e978f4821d07" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/654 [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 = 2,\n", " lora_dropout = 0, # 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 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).\n", "\n", "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", "\n", "If you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n", "\n", "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "7b7d65a25396454d9b075166589534ae", "f57c3c1942c84c7398ca9d73264bd067", "f895339008634f40888e37689efca3e0", "a7586da043054481a1ff0668517eee36", "df097a15d4ed40f993cb71de43e98512", "3be889faa83f483a810fb28305bafe66", "87f99538b9754fc392be52c1e56c9b59", "2c1d1443ce894262a33252e3b47947fa", "a3aa19be7726408d8a7bdfd04f896192", "b6190427a7b04c31aad63320a3754a61", "7ca55e2d5b62444fb1eef7471ca91558", "5d7ed6dc8b9f4d6aa9efbe58d62da766", "ca68eb849cd94c0e882d269b69a1b2a3", "f1a91b8a269a4a42b7aeecd082cd3b2c", "f0a7db552abf42a59c4f238f53b84e8c", "bc9eb3c451584c14b711379622e2638e", "a9df5afa51914ff69b0217b67e0c95be", "369d710597b8441285a778e8317204b5", "58ac0988410245489c76fafd2258cb96", "0866735c8722454d91223d63413e5392", "fc900101d472464d9c2693f43f6c9184", "aa172a862e194b348b01c93855eacdd6", "a7d2e08b1f62493084777dcbc7098214", "75222ac391d344eea795873080489915", "5451a9f5b02f446f9470ddc9c6e3d7cb", "a860cb660c2445f48d7a84767bcbd906", "b17d02496ed842acb722bdec3e9e5605", "2be71c392304435db3487d3c91240491", "017d7db7289c466284febe6936c0cdd8", "612b049dd207462191bc990b73dcdf78", "8c2a380d50a74ece9c76fcabbb75a55d", "5f3e52d1b62f4dd288da35d402f0313e", "823718d22a7e42239b196b6dad2178d1", "ee5205206f09413e9d4066637398962e", "d5e4f08968494172a6a0d516179b2057", "71b9e675bbb4416986292167a9c11fa2", "af0e61d33dda4276944bdbb4a456896b", "2509b58450f245efa97cb8747de1e8a1", "9cb511835ba2494c8e14b352f8751543", "b9cd92d0d7494ff8b7a15c4cbc5b8ec8", "54837fce58b44ee7b5de41a623f2c460", "15a8ad2e952b4f688fad0402a68f0370", "8f45368098c84d92998bff5c8f88cb17", "5d85deb4f495466f8fa979156b77de0a" ] }, "id": "LjY75GoYUCB8", "outputId": "b648afaf-0923-49f0-f96a-7e91d4bf13d6" }, "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`!" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 106, "referenced_widgets": [ "4328f43c391b4eab9d5364d10c97e388", "183de632abdc45c886728bb334327634", "a657f61ee1314b2d82ec9994137350c4", "f36ead3bb80845f49e57eb05a3d0ce3e", "892879b07f6d4ea5bacbd8ed80d8dbed", "d2c30a9a87e64280becbc90e62e777b7", "1fa6b3b7a22d44ce90308e76c2bfdee2", "4842f02106f34a5ab3278ac8e4e8170b", "aee1a412d04043e1aa940577ea5cfb84", "5bddeca848d3480db1ad23b6c5499e03", "b614816f05e04797b546b3b065321af4" ] }, "id": "95_Nn-89DhsL", "outputId": "7e902acb-652b-4c2c-f43c-6eebc4fa8d02" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", " self.pid = os.fork()\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Map (num_proc=2): 0%| | 0/51760 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 01:26, Epoch 0/1]\n", "
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StepTraining Loss
11.753600
22.139800
31.721000
42.052800
51.839700
61.962900
71.549100
81.640200
91.617900
101.714700
111.335100
121.472800
131.166000
141.201500
151.079900
160.990700
171.102000
181.329100
191.095200
200.936700
210.992800
221.038600
230.948300
241.045500
251.108500
261.052700
271.106300
280.930700
290.886100
300.890600
310.874400
320.894200
331.020800
340.913300
350.982900
360.883900
370.931800
380.798800
391.092400
401.185600
410.912600
421.028800
431.011900
440.940900
450.937300
461.009400
470.886800
481.222100
490.958500
501.084800
511.049300
520.956600
531.008200
541.232700
550.826700
561.069100
570.958600
580.858000
590.892600
600.942500

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "pCqnaKmlO1U9", "outputId": "17640299-3913-4f79-bb05-aec68baded0d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "94.6208 seconds used for training.\n", "1.58 minutes used for training.\n", "Peak reserved memory = 17.469 GB.\n", "Peak reserved memory for training = 2.274 GB.\n", "Peak reserved memory % of max memory = 44.154 %.\n", "Peak reserved memory for training % of max memory = 5.748 %.\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": "ekOmTR1hSNcr" }, "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "kR3gIAX-SM2q", "outputId": "8726395b-0e77-47ac-f4f5-da2792d1bd45" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\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:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 985, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" ] }, "metadata": {}, "execution_count": 9 } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\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)" ] }, { "cell_type": "markdown", "metadata": { "id": "CrSvZObor0lY" }, "source": [ " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "e2pEuRb1r2Vg", "outputId": "d37d551f-84db-4748-a7c4-284bdc16e19a" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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:\n", "Continue the fibonnaci sequence.\n", "\n", "### Input:\n", "1, 1, 2, 3, 5, 8\n", "\n", "### Response:\n", "13, 21, 34, 55, 89, 144, 233, 377, 610, 985, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040, 1346269, 2178309, 3524578, 5702887, 9227465, 14930352, 24157817, 39088169, 632459\n" ] } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\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", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is a famous bridge in San Francisco bay area?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "Z5tWI7ivVjMg", "outputId": "44825c13-5dfd-481c-a153-3c74c2f1c3ee" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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:\n", "What is a famous bridge in San Francisco bay area?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The Golden Gate Bridge is a famous bridge in the San Francisco Bay Area. It is a suspension bridge that spans the Golden Gate strait, which is the opening of San Francisco Bay into the Pacific Ocean. The bridge connects the city of San Francisco to Marin County, and it is one of the most iconic and recognizable landmarks in the world. It was completed in 1937 and has since become a symbol of the city and the Bay Area. The bridge is 1.7 miles (2.7 km) long and has a height of 746 feet (227 meters) at its tallest point. It is painted a distinctive shade of\n" ] } ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is a famous university in San Francisco bay area?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "9fJZQcwlVlBq", "outputId": "0687b832-6109-4312-9846-fcd5a525aed7" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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:\n", "What is a famous university in San Francisco bay area?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "One of the most famous universities in the San Francisco Bay Area is Stanford University. It is located in Stanford, California, and is known for its academic excellence, research opportunities, and successful alumni. Stanford is a private research university that was founded in 1885 and is considered one of the top universities in the world. It is also known for its proximity to Silicon Valley, which is home to many major technology companies. Stanford is a popular choice for students who are interested in pursuing a career in technology, business, or other fields. It is also known for its beautiful campus, which features a mix of modern and historic buildings, as well\n" ] } ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What are the DNA bases?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "1TPeJxviVls8", "outputId": "f8240540-eb2c-415d-df50-94d81dda4e99" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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:\n", "What are the DNA bases?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The DNA bases are adenine (A), guanine (G), cytosine (C), and thymine (T). These four bases are the building blocks of DNA and are paired together in a specific manner to form the double helix structure of DNA. The pairing of the bases is as follows: A pairs with T, and G pairs with C. This pairing is crucial for the proper functioning of DNA and is essential for the transmission of genetic information from one generation to the next. The sequence of these bases determines the genetic code, which is used to create proteins and other molecules that are necessary for life. The four DNA\n" ] } ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What are all of the types of bonds found in DNA?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "GlPUCSprVnRK", "outputId": "33b8e72d-c3f6-4a0a-cc15-df056ca0063a" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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:\n", "What are all of the types of bonds found in DNA?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "There are four main types of bonds found in DNA: hydrogen bonds, covalent bonds, ionic bonds, and van der Waals bonds. Hydrogen bonds are weak bonds that hold the two strands of DNA together. Covalent bonds are strong bonds that connect the sugar and phosphate molecules in the DNA backbone. Ionic bonds are weak bonds that form between the negatively charged phosphate groups and the positively charged hydrogen atoms. Van der Waals bonds are weak bonds that form between the non-polar regions of the DNA molecule. These bonds work together to give DNA its unique structure and function. They also help to stabilize the molecule and allow it\n" ] } ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What are the reaction names to create DNA?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "NelWwdJjVpqR", "outputId": "04f5085f-fd1c-4657-910f-1064b9237626" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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:\n", "What are the reaction names to create DNA?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The reaction names to create DNA are:\n", "\n", "1. Primase: This enzyme adds a short RNA primer to the template strand.\n", "2. DNA polymerase: This enzyme adds nucleotides to the growing DNA strand, matching the base pairing rules.\n", "3. Ligase: This enzyme seals the gaps between the nucleotides, forming a continuous DNA strand. \n", "\n", "These enzymes work together to create DNA through a process called DNA replication. The process involves the following steps:\n", "\n", "1. Unwinding of the DNA double helix\n", "2. Binding of primase to the template strand\n", "3. Addition of RNA primer\n", "4. Binding of DNA\n" ] } ] }, { "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": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "upcOlWe7A1vc", "outputId": "5de0677a-47b8-4f8f-af5c-e2ac7a66f4ca" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:581: UserWarning: Unable to fetch remote file due to the following error 401 Client Error. (Request ID: Root=1-6625b6e6-27414a32577dabc56204d6c5;4e8ba6b8-9fd3-41d1-8918-9fdeb530c138)\n", "\n", "Cannot access gated repo for url https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/resolve/main/config.json.\n", "Repo model meta-llama/Meta-Llama-3-8B-Instruct is gated. You must be authenticated to access it. - silently ignoring the lookup for the file config.json in meta-llama/Meta-Llama-3-8B-Instruct.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in meta-llama/Meta-Llama-3-8B-Instruct - will assume that the vocabulary was not modified.\n", " warnings.warn(\n" ] } ], "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "# model.push_to_hub(\"Your-Model-Name\", organization=\"kevinkawchak\", token = \"Your HF writable token\", private=True) # ONLY saves the LoRA adapters" ] }, { "cell_type": "code", "source": [ "if False:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", " )\n", " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "# alpaca_prompt = You MUST copy from above!\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is a famous tall tower in Paris?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "RYh575LYOG5J", "outputId": "eada21c9-4bfb-45ce-f313-5d0f43ff3e81" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\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:\\nWhat is a famous tall tower in Paris?\\n\\n### Input:\\n\\n\\n### Response:\\nThe Eiffel Tower is a famous tall tower in Paris. It was built for the 1889 World's Fair and stands at a height of 324 meters (1,063 feet). It is one of the most recognizable landmarks in the world and is a symbol of Paris and France. It was designed by Gustave Eiffel and took two years to build. It was originally intended to be a temporary structure, but it has become a permanent part of the Parisian skyline and a popular tourist destination. Visitors can take an elevator or stairs to the top of the tower for a panoramic view of the city. The Eiffel\"]" ] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "markdown", "metadata": { "id": "AEEcJ4qfC7Lp" }, "source": [ "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ] }, { "cell_type": "markdown", "metadata": { "id": "QQMjaNrjsU5_" }, "source": [ "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "yFfaXG0WsQuE" }, "outputs": [], "source": [ "## If using model.save_pretrained(\"lora_model\") # Local saving\n", "if False:\n", " # I highly do NOT suggest - use Unsloth if possible\n", " from peft import AutoPeftModelForCausalLM\n", " from transformers import AutoTokenizer\n", " model = AutoPeftModelForCausalLM.from_pretrained(\n", " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = load_in_4bit,\n", " )\n", " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" ] }, { "cell_type": "markdown", "metadata": { "id": "f422JgM9sdVT" }, "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "iHjt_SMYsd3P", "colab": { "base_uri": "https://localhost:8080/", "height": 416, "referenced_widgets": [ "c8da839ba6cc4ef7b280636813cb7ce6", "8cac20985d3948d2a5e7bd550ba6c448", "3bf5717044784d33ba6cdadec4436022", "896a6d344a9e4363b4ac772cb34a814e", "096d280463da4f32870085139f67f6be", "8f63c2e27b32490892779f6f1d6928a0", "036fcd589bab4ab994c428ecc9e19c58", "2599fc19954c40aca12d17e56a3b7493", "91db9390b8be494aa3c9ebf1f13ad046", "8adde43131e14b9e9bcbf2bf79caf980", "07c7d157a4fd492684a24509506cb592", "6b345d7535804f1e83c3c8648364f128", "20c9e7a9fac44a848417c58fa0edf9af", "218422eab9a341a4bdcb5d663ff3b3ce", "51238cd4ff8e4ba78a83914ff28ceb81", "1f6b8ca79a7e4f6aba622445d97636c5", "3d4e3346f9fc48a3bfe107119b188621", "324537cf98ae4e34a931a7a147b3b40b", "acaaaf056d4f49a5b0571ba6e1819702", "efb46dc2d54e4959a7cacc73a3384949", "6b1d1acb86cf4683992f1e648f660f6d", "f37dfd9d994e4929b52e4a3640975aea", "52398951f10c4213849707d563dd1e1f", "2fcd8fb95d79447ebaf112b6558d91c7", "a2eacf7fe0134d4e8526f0915ca68dda", "986be4d52227462aa7788c6ce0479b13", "4afcc3b2bce8415590900cb3dcd471fc", "b9d7f8e5f06e461dba320f21f2493125", "2759a399747349bd86b163c8d435e2fc", "e230fd9abc064e479c023db52a6075e3", "d5957d6eefcf489e8032c456bf321043", "cbeaf2fc93064595b0e8d79e3ac6cf7b", "8897206c09c14fc38b9c3e422c05e8aa", "2a497742dee54ae2b20c11667dfa8993", "9b93600d9c7f49c99fce068636912783", "c3a8ca62de4d49d3a94a0c3e6f686f55", "71b81c9c6d094732999553d363ff8efa", "61963a43c74c4da6a9b1b72dcc5e5784", "11b8f613a0f0485b863007eb9ba42c9f", "61c118854482496d95a1c197e19ca209", "6cc31b6537284e928d1ca637444574ca", "0189147cd080424e8a3021e06f0ce038", "68caadbe19ac4d7390899f3709beb8d9", "7cdbe6769e9f45f3be19dc7aae4ddbf4", "302f461aa9c3407898b58bd8d02f465f", "97f88146523f4cb39cae5f08ed627dfa", "367612dcfcbb40d186b80e12f6c1649a", "6947ac7d48564b39932ce0c854c11c13", "0d50009fc2904993a79b27eada9b2680", "b771532d7eda450d92cb50c5152c5be6", "072c9c2a24ed42b49eb9381afd540969", "058f2dc80ae3484f9a90a99797e023f4", "a9783681a48147e9bf95a8e381fc5a72", "45197bc52e324e11be78bb8949d59487", "eab85d9d2b8d443fbd7c6dc4f4922c63" ] }, "outputId": "3c96ec10-e7ce-481d-9c5c-f1ffe075b139" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n", "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n", "Unsloth: Will remove a cached repo with size 16.1G\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Merging 4bit and LoRA weights to 16bit...\n", "Unsloth: Will use up to 62.91 out of 83.48 RAM for saving.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 32/32 [00:00<00:00, 74.25it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Saving to organization with address kevinkawchak/Meta-Llama-3-8B-Instruct-LoRA-A2\n", "Unsloth: Saving tokenizer... Done.\n", "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n", "Unsloth: Saving to organization with address kevinkawchak/Meta-Llama-3-8B-Instruct-LoRA-A2\n", "Unsloth: Uploading all files... Please wait...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model-00003-of-00004.safetensors: 0%| | 0.00/4.92G [00:00\n", " \n", " \n", " Support our work if you can! Thanks!\n", "" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "zmdJjv6-GarM" }, "outputs": [], "source": [ "# from google.colab import runtime\n", "# runtime.unassign()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "A100", "machine_shape": "hm", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "ce3d42aef9634825b6393290d7e97720": { "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_89bcff3f688246e3a5697dd64f86aaac", 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