{ "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": [ "55f62e1f638c43e299528f349e0d1118", "db87ede1e8614add8dbda84bbb00931b", "cc005c5047dc414a9044fa0b6ba7edd8", "d0a6fe53cc4440b1868b26aee2ef681e", "6749a85d046741f785b1b8d60aedbdd8", "d081a3197a134e1eb60bd0c293a68b17", "3981af0134c34f7685ed1e156eb330de", "6e2afe9ecb134804a29d4959adf642e4", "fd3f6ace084342fbbbcf8e3e72ceeba0", "61c193a68ca3492dacda834c206fbc52", "0668ca47f9564bdfba1f933e4e88ffcb", "90faecf049504737963b558ed0499c21", "fcbeb9246f4a4ad4af57eb75b7127d19", "5cc4ec3e78de4be98b03b5e2c01756e4", "5ab3ad3fb8c346efb89660dfd44f9e42", "38cf6f5168ea478a9b4ada357a98d4d4", "2ec3bbae3adf4b40a4b66c8c3423d3d0", "798eb275d26a4186bb6e4101246b34dd", "f9e07da39b064a85b5888c6b5ffd58e9", "cb7af558fcb64ee6a10e8dd29a462aaf", "b74bbfeebfaa4988aa2b71cc77e376c7", 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"fd5b99a53c63442996cf7bc280ccfacb", "66c23abbc7df42a0aabadf5281c61a72", "1566b27c4584406d9b9068b0d86967bd" ] }, "id": "QmUBVEnvCDJv", "outputId": "86f96c57-96eb-491d-c224-02574d871289" }, "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 = 5,\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": [ "c47620573c0d4350ab220f7f8778318d", "159393a4b4664f1bb027614e8239f3be", "438e28181a7d41fa8f1285c880e1b83f", "5733682f5f6b41f8bb362232a05c01c4", "d193bc6e7bc2435fa8ddb1f2dbddd8fa", "8f866b409046447f968df266512a2086", "bb736db233474bfabc80956a27461767", "c1b7c749cbfb4b8684465f9ee87186d7", "668d3aab6143490f9c63ae1f31c8441e", "c453e1c0f743407dad82254c1fc73032", "270e1a19a5754280960cbb7d7df4532d", "914511660a67459584eb562bc4129e2b", "839830531e554e29b21a78c815d3d1d2", "ca0758bcf44e4e1d8edfd19b06930190", "5429172e80fc4fd3bc0979e5f0e5e501", "0610c44eaa0a4398b3cf24db63326277", "c4834ecff42742a6bb28a55b1ffcabc1", "3197bb6ef2734274b050c913b4f3c579", "b8d5c76eb1f6483ca39683625eff2a93", "225e04e5af434185a986c7e34ec6f716", "387190cca540450090dda07d2cf8b2fa", "c0435d34a94f4ddd84e3af03bebdaca8", "1ab82440156c4812bea47096b4e2977d", "351a6feb9eac4103a4c3d69716f52fd8", "91c161ad57bf42b7ae95746d16011c52", "3c26bc2807054989b4ab31e7b3f6ba6e", "bf17b400ac894b61909da1ac9f79edc2", "e04b5f1c48bd4d72ac0c7ce51f36f6c8", "859fea16dc31423ea49f21425f58ee12", "8763e18ba688488588fcfbc11ca7a50e", "36bf7f8506194b07982d6c54233e3575", "d0a51d52b5a74e88a054e2197ffdb41b", "3234d709e9d24b448bac5736c783dac2", "a733300ae06d4d58b4b066ff61d8dbd6", "f54fe9be3b644c92938a5d8fa7931c96", "e4a02323cabf40f2b19eed785b34c838", "0438c8dcfd2742cb96d9cc1b0c907cf5", "6c60807a0abd4142823db9cd12132fa1", "4dc9d95b82d345f6a1b67a2b65bc4ea5", "3b5eaa89254e434cbceed5c4a13a8461", "909b071ac77f4b3f98444372e09ea1fb", "0c13f67b10774f929b3476ba5f726565", "54ffd2c7732e4a2b867c744ec2740275", "f05e6d80d546493e9f913e6c48b108ce" ] }, "id": "LjY75GoYUCB8", "outputId": "4ee59799-20f5-4f2c-9dc5-33b51d7bdce8" }, "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": [ "2d47b8937fa146b5abe5e5cbb1527511", "62fb9bc622ef487b9d88c9732bc9e1e6", "383e10b7eccb4915a3628d7c6937080b", "5a2dee539b7941fa9dfc6b145aefef2c", "81a8c78de00f49f290f3c6c554680030", "26b7a74d1e8f405e8bc51d177cf87c73", "2bbb65669a6a468ea98c79910c5bbe94", "05f92851c0b0422cb1ee370e9a7c6997", "d067bca9d8ad4e3aa77941ce47a377e7", "3e0ef525c9464d44836860c90eb97911", "e08abd2f5e1a48a8888f2d447949fd5d" ] }, "id": "95_Nn-89DhsL", "outputId": "d9103048-b5c0-4e64-999d-0b7e92633380" }, "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 01:28, Epoch 0/1]\n", "
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StepTraining Loss
11.753600
22.139800
31.717900
42.042100
51.797300
61.844900
71.399100
81.483000
91.379700
101.372800
111.060300
121.095700
130.944700
141.065100
150.919100
160.889900
171.029900
181.273100
191.032300
200.891100
210.938900
220.967500
230.908800
241.017300
251.075100
261.043300
271.099800
280.915000
290.863600
300.879600
310.869000
320.884300
331.011000
340.897300
350.976500
360.877400
370.923600
380.787000
391.085700
401.171700
410.907100
421.009000
430.987500
440.926800
450.931600
460.996500
470.871700
481.210000
490.946400
501.078200
511.026900
520.947400
530.996400
541.216700
550.818500
561.062300
570.945000
580.849700
590.886800
600.931400

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "pCqnaKmlO1U9", "outputId": "86f346fb-75cb-451e-d799-169044ef4445" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "96.5835 seconds used for training.\n", "1.61 minutes used for training.\n", "Peak reserved memory = 17.396 GB.\n", "Peak reserved memory for training = 2.23 GB.\n", "Peak reserved memory % of max memory = 43.969 %.\n", "Peak reserved memory for training % of max memory = 5.636 %.\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": "533fcf7d-4f9e-409e-fbb5-47ace68aba2d" }, "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": "ceece2d1-22b6-4298-fafc-ce6e6fbbdda9" }, "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": "2ecfc621-c84a-4d5e-b58c-95185b31bbf3" }, "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", "One of the most famous bridges in the San Francisco Bay Area is the Golden Gate Bridge. It is a suspension bridge that spans the Golden Gate Strait, connecting the city of San Francisco to Marin County. The bridge is an iconic symbol of the city and is one of the most photographed bridges in the world. It was completed in 1937 and has since become a popular tourist destination and a symbol of the city's engineering and architectural prowess. The bridge is also a vital transportation route, carrying thousands of vehicles and pedestrians across the strait every day. It is also a popular spot for walkers, joggers, and cyclists, offering stunning views\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": "2b5c1260-a357-4667-a4d2-64696c2226c9" }, "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. Located in Stanford, California, it is one of the top-ranked universities in the world and is known for its academic excellence, innovative research, and successful alumni. Stanford University is also home to the Hoover Institution, a public policy think tank, and the Stanford Research Institute, a research organization. The university is also known for its beautiful campus, which features a mix of modern and historic buildings, as well as its proximity to Silicon Valley, the hub of the technology industry. Many of the world's top tech companies, including Google, Facebook, and Apple, have\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": "c2822155-3e90-4fec-91ae-d6026485454b" }, "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 arranged in a specific sequence to form genes, which contain the instructions for the development and function of all living organisms. The sequence of these bases determines the genetic information encoded in an organism's DNA. The four DNA bases are:\n", "\n", "1. Adenine (A)\n", "2. Guanine (G)\n", "3. Cytosine (C)\n", "4. Thymine (T) - replaced by Uracil (U) in RNA. The\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": "3cbb9253-d868-44d9-bdeb-38fa85094741" }, "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. \n", "\n", "1. Hydrogen bonds: These bonds are formed between the nitrogenous bases of DNA, specifically between the hydrogen atoms of one base and the oxygen or nitrogen atoms of another base. Hydrogen bonds are weak and reversible, but they play a crucial role in the structure and function of DNA.\n", "\n", "2. Covalent bonds: These bonds are formed between the carbon and oxygen atoms of the sugar molecule and the nitrogenous bases. Covalent bonds are strong and unbreakable, and\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": "7361e3d6-609b-4292-9b83-b678a46b0e59" }, "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 starts with the unwinding of the double helix, followed by the addition of nucleotides to the growing DNA strand, and finally the sealing of the gaps to form a continuous DNA strand. This\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": "acd50bcc-3f16-4db8-cc06-6789450d4a1e" }, "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=1-6625a68e-5d36e3657b7e2b303a6129cf;166b4f5c-1fb0-44cd-a791-44c1845e576f)\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": "4f49f897-0823-483b-8b08-7dc54ab55538" }, "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 became an instant icon of the city and was left standing after the fair. Today, it is one of the most visited attractions in the world, with over 7 million visitors per year. It is also\"]" ] }, "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": [ "4819742b02294d79a16125bbc6c3b6c3", "15f11d25be66458bbb1560c8d8f5b211", "aa3b38bca6154344b06a6e7bbbf0793d", "f974ba151bc74fd8862bf0117824480f", "1d8a13d160e94586b7578b27974079d8", "b54fe64ba16a43a5922c5e65940c7f6e", "e0c901ada1514fadab60beec19c84096", "8f4063566735493196442b63a0946663", "85a3d38bebc24b359b3ec49d8ded3bd7", "f64c0dbc71bf4241b4ae996b05f9fe48", "c4ba20471707462481afc4d9d901e9ee", "2d5e75a52b3e4dcbacff313042c357df", "478db5e47a0842f8a3c86c5ce11b5b2c", "2ac0fafec275467caea89d43ede6d248", "91ea778537c64671a1e046fec056a411", "7d709afd8a744b88819da5bf5a6f5a70", "11a935c5bd7b4c86850229b08fca5cfe", "6dc754197d914be4b1fe6fcb5826a7f4", "3eb461fcd8b242d39bead56a8570960f", "76c17a967f0246b5bdc429c00bb3a5ee", "138c1d22050f4f438b2c352fdd261333", "cdf2bf9ac14b4da6bd715c04d45db63f", "2bc0b3a51eb449edadb281891868aeb6", "7b6be35ef7b9420b88611b085828085a", "4b523cef562140ae81f55ad0aa79c6a4", "d8c49565995144a4b9ac30918eac30f0", "030109bc8a834bfca7d40a6b4e256c6b", "e14b5336fa404aa39ee767401f6c58fc", "940bb6664e5a4705a72f2c929fe3dc20", "c73f3660f845489282505bf035e43001", "6f75255cfd734767868468fbc9a66722", "30958a4ea2cc42b797f863f8e4c5c5e1", "ba7a18f5886e443094ae300c71618aba", "4ab96135f30f48879bb4ff39a421b287", "a7d697bd3fa946f19559f5bf5a936030", "5c9d9c377e65497a83307cad59eb7cd6", "ec08da7aeb98408e9a8af7844e8e6ed3", "c4b2cab1a55f4f3f9af34063490720df", "e62c8698fd3a4fa1a52fb0c9245a8688", "2260d2bf263b444ca01ae277351e79c0", "de9c4602d01b409f9d72ac8be2873ad0", "be81ac1e5d154f1999692c4bdba297f8", "df0dc96a159e43219a3fe38680e44ede", "e6e390c1db944d09bfd433fc1d92e51a", "24b0c9b1c6bc4fafb84b249522055f06", "1ca8799ee87e425fa3d06d14d7b3f881", "e8f6bb1a9c3c42cca05ed619a6b0d785", "672631ffa9714fa08244afd56ae0637e", "67280fddb4ea4976a29f9a8f537deeb0", "b947d47ebef440338b687e02d6d0cbaf", "c517e72a4df946ffa04b968927ecccf8", "d61d4058fe0c403c8cb9578fae2bddd6", "02c08b03b28246f8b5aec093e8a5a831", "6638732e5f3445a9985b7d2268c98ec9", "6e9a1f88075e4fc2a82c7693233fdd2d" ] }, "outputId": "4f57df6b-9ac1-4585-ab1b-5befb3fd69ea" }, "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 62.94 out of 83.48 RAM for saving.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 32/32 [00:00<00:00, 74.08it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Saving to organization with address kevinkawchak/Meta-Llama-3-8B-Instruct-LoRA-R2\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-R2\n", "Unsloth: Uploading all files... Please wait...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model-00004-of-00004.safetensors: 0%| | 0.00/1.17G [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": { "55f62e1f638c43e299528f349e0d1118": { "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_db87ede1e8614add8dbda84bbb00931b", 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