From f6586e5d76e0ddf6ffffbde2e6fab0673f0d4a73 Mon Sep 17 00:00:00 2001 From: Ram Kadiyala Date: Mon, 22 Apr 2024 14:03:26 -0400 Subject: [PATCH 1/2] Add files via upload --- 20.Alpaca_+_Llama_3_8b_full_example.ipynb | 5051 +++++++++++++++++++++ 1 file changed, 5051 insertions(+) create mode 100644 20.Alpaca_+_Llama_3_8b_full_example.ipynb diff --git a/20.Alpaca_+_Llama_3_8b_full_example.ipynb b/20.Alpaca_+_Llama_3_8b_full_example.ipynb new file mode 100644 index 0000000..7f4e741 --- /dev/null +++ b/20.Alpaca_+_Llama_3_8b_full_example.ipynb @@ -0,0 +1,5051 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", + "
\n", + " \n", + " \n", + " Join Discord if you need help + ⭐ Star us on Github ⭐\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#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.**" + ], + "metadata": { + "id": "IqM-T1RTzY6C" + } + }, + { + "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", + "# 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" + ] + }, + { + "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": 388, + "referenced_widgets": [ + "c78e69db216844a39fac827349845f81", + "f2c23b7309ec430c9cb984ba8f511302", + "451a5048b6f84360aacbda342448e0da", + "980076d4a6c44020aed6d19ebfaaaa54", + "b869fd94c21146b4917ff3e60654664b", + "526e11b93f064ace9b6a255da796fdd7", + "0df7c15449ed45edb9094e5d58d0731d", + "488bb3579b99411ea03d25c0e916e629", + "ab53150e33004a41ab28c06833ed5aa0", + "1a4b589eed4f4d9694cae1f69330836a", + "a419b86d8ff0428b88843b55cf0be657", + "85ee709c047c49c7b7888caf9eff3a54", + "84569f44a689473889246c2eeab5d8a1", + "999ed6e6f8b24c5bbfd582c478f45c57", + "72072eb832db4889beef6f3fa5d45044", + "b87419aaa7cf4ed48b96ebce680bb29b", + "5e1d1523a0c34285a6ef4aa13e9cc645", + "e4ff98a12bc1424dadd9a8dec3afb2b3", + "92eb4d8be9a64892ae5e92c809f568d0", + "5b40ea812830452c8bdc46dbc0dda54f", + "467bd804fb244a298b8caaf0c3b3a3d4", + "5480935a6ba341bbb62cbfb7d8707fec", + "9a65ad628ffc4e2d87479514be357617", + "a98617118be94b73bbd2043c5dd65621", + "126fe7d2d8cd454eb7f71bec0b9b6e01", + "3cfc4b6de45f4ff5b8f23c3b79e6503f", + "f50c67dc31dc48aa978909d8967c5294", + "5326096097064f4aa0650ca59ead43aa", + "34f78b3e7ea0400b90d48fb2809a654d", + "f753790028d14420b2683c2bc16dba2b", + "b11371e3382440219af0d82b4fae0fe8", + "d091d3f8b01a473ea81f5f62567a6407", + "973e0a9b040f494895f60ba8e1468c6e", + "66f169549d2847e097cb9788e3e4d29c", + "a3709b260001400e85db6464542c66d3", + "7d7726f17a4a462a871f5a97b3d66928", + "3ed36820b9244e878c6610c589d7d821", + "eb07173547f14d428d8549e88b4f9564", + "50bd0e7f67f94c9eab8f42493332041c", + "b41fbbe7b59b4e13b17244edba47af30", + "5c64d6e41ddc46da81e4f92f4c88e6d1", + "b46d2111e0ee41b9b112f7c9fc65028f", + "5557222f4157411d9250ad82827a2f8f", + "b8d26c1ec5c44e1f9c8d20bb6d4c9d1e", + "2d0462c3fd21481fa6adf8fc9df5c28f", + "15248beee1f042f29ba17deb3e29c90b", + "c75a509df90340e5857692400bbb8af8", + "e7611888e9fb47588aee414cdf0ecf27", + "2c95bc4f3f9d42be8f03bef23242af66", + "396e74209ce9460a885b2236d1fb7b97", + "a8ec27381f564d7cb6962ee503d2eae6", + "c7fe113e1b3b46968d97cdc6fb1dc241", + "fcd3a5f67e164712bcf7e8e7ff1f770a", + "1eef300d637145d68a660db46ca9b14a", + "469b8be78b924d3aa90335798775e1c6", + "96f9b00a55f34d4c8a4ed3c496e1ffbf", + "0f36928c39f641618e89a27445c5cbc2", + "d4e450ceb7da45e9b8ae82bec8295343", + "b997bc55dca04d0f8880447ec9f9ba31", + "b1a3d509b7e643fca992dd838bacc203", + "7d03bcf25c774fed864b09770cfe74ea", + "09a4c26458f24fc49aadae2fee60acc4", + "41eaa431bdd94075b0c41899c14c3fc3", + "7734e446cfbd4e9b9f796ba662f2ab71", + "a96216197bda4d73afdfd657567bd059", + "db420165996143ecb8b6624ef6341912" + ] + }, + "id": "QmUBVEnvCDJv", + "outputId": "e14a20ca-5800-4653-9a6f-8a58513f642d" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/1.14k [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, # 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", + "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)." + ], + "metadata": { + "id": "vITh0KVJ10qX" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LjY75GoYUCB8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "bbea39204f514d65a92dbe79e4d8e697", + "161dc482ea1f4f129f355ee4d20e8cd5", + "208f49c8a05f4f25906fb7df2fd95d7c", + "1bf51ccd8640436482357b6e06a2f850", + "1ea78447b4fc4a38aaff65f7954ae65e", + "15f5446cc9d0433281001eb149c51cdd", + "f27dfff3b0534f558d91747a91629523", + "dd7c9e9d3fca4f0aa6893a48c92e6748", + "606650950feb431e825cb7ea79c5a1d7", + "8ce3a2df43bf48429e29bd73dac5e638", + "db00b79287da4c93b425d4b07b0f7b72", + "10c75c3affac4c839a065c5d07ff7fd3", + "7d9adc9aaa6540c3b80fb2b4e02f7b66", + "2e24b601930a44b58d5d1f53b33315f7", + "bb0f6ed8b0314e119254b574de524f6f", + "355b4205fa0e4612954dc918c9cc6449", + "f9e8a5f5102040f380d3287a93cfa045", + "ab3e06fd917e4cc59f5b8e275caba930", + "4bba5aefb61e4b458381bff09abbe96a", + "6f37c03d14314e59bef4ff1bc985e866", + "0c2631134e6248459fdfe8f48205df25", + "0ec293e756104409b563d3e561ec2937", + "5b7210bdfdd547abb29abbbb26dce37e", + "adc97fcb04834c7b9417597d9de135cd", + "f5b2f797ddae451e8fc1dca9bece278c", + "6bf7fcaf8dc347daaf2f5b04fac24300", + "3018574e40ef4f5284a1832d2efb1121", + "6038bb8ebaf945da8e8c470fa7d436ec", + "c9b628fa9ce34162b6d04d4f191f819d", + "d92aeeaaa8b74016bfcbe49cec44f0d7", + "348691f1aa714d16bfc3148b39518b36", + "8a372927fe8d4a09b4e35bdfc77a6136", + "4bf5cf0af2e441bf8d101795f8a82594", + "45560e601cdb484c8e9293dece7555db", + "9dd2481401cc43acb405a64e9ce9f13f", + "772124f5d8f54e959dff5d56d0335e4c", + "9e3f29ee68524e749a3dbf1ba32653b6", + "fd8ffc5653b94428bb3b82d3979ef54c", + "e288762221be4e0796631bb7b41bea56", + "f94493a870af47c98ded7a71e1614681", + "3b9a0019602a42cd8bbd80a77be5cfcd", + "5fc82503d5984fc0bfe364b2ae5ad530", + "9b3a7554fbf14276b208fce9d24b5b89", + "c8491efcfb72456f8d629acc49dfac90" + ] + }, + "outputId": "51d2433f-c01a-42f8-eb04-498d5406ae70" + }, + "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": 104, + "referenced_widgets": [ + "f07f34e9b5c649469ac667239b0fbaad", + "014479d51b95488da6dbdbfa3e7adce3", + "320ad99d9e91463986ca91073e3729fa", + "acb68ec0ac33408b8d02f22cfbbe4f01", + "30f2bb04d6754a54af59439cef40ff87", + "1b01f784c04e4d11a2afd72284646138", + "5f7db89c6f0b4c968dd8f43e23f54365", + "08a476cadd42469eb2234ac503f8447e", + "8019cfd942a0477e92cc3a34ecc826e6", + "8516663d35be4bf48ce4c54f58188075", + "60d93f53511c4fc1adf11bacf22590cc" + ] + }, + "outputId": "842ad9fb-3ce2-4668-a2bf-60f583d05a2f" + }, + "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", + "
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240.983200
251.070400
261.012900
271.049900
280.869200
290.843500
300.903100
310.866300
320.873700
330.998300
340.863400
350.965900
360.865500
370.891300
380.773200
391.096600
401.172000
410.911400
420.992800
430.960600
440.911000
450.932200
461.000000
470.876400
481.225000
490.923800
501.049000
511.033400
520.939600
531.016800
541.174100
550.801200
561.033900
570.889000
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590.868500
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" + ] + }, + "metadata": {} + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pCqnaKmlO1U9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a176103c-c9eb-4b13-9488-b0ad1bd945b3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "424.2005 seconds used for training.\n", + "7.07 minutes used for training.\n", + "Peak reserved memory = 8.982 GB.\n", + "Peak reserved memory for training = 3.314 GB.\n", + "Peak reserved memory % of max memory = 60.903 %.\n", + "Peak reserved memory for training % of max memory = 22.471 %.\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": [ + "# 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)" + ], + "metadata": { + "id": "kR3gIAX-SM2q", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cd29185d-4433-450f-ea19-f3ad6947ae3d" + }, + "execution_count": null, + "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, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "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!" + ], + "metadata": { + "id": "CrSvZObor0lY" + } + }, + { + "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", + " \"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)" + ], + "metadata": { + "id": "e2pEuRb1r2Vg", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0d250429-e431-4fc8-9edf-97e5fdc81da8" + }, + "execution_count": null, + "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, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040, 1346269, 2178309, 3524578, 5702887, 9227465, 14930352, 24157817, 39088169, 632459\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "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!" + ], + "metadata": { + "id": "uMuVrWbjAzhc" + } + }, + { + "cell_type": "code", + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # 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 for inference, set `False` to `True`:" + ], + "metadata": { + "id": "AEEcJ4qfC7Lp" + } + }, + { + "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", + "\n", + "# alpaca_prompt = You MUST copy from above!\n", + "\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", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "tokenizer.batch_decode(outputs)" + ], + "metadata": { + "id": "MKX_XKs_BNZR", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1e563fe1-7dd2-4851-99e0-017392b5cac6" + }, + "execution_count": null, + "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:\\nOne of the most famous tall towers in Paris is the Eiffel Tower. It is a wrought iron tower located on the Champ de Mars in Paris, France. It was built in 1889 as the entrance to the 1889 World's Fair, and it was designed by the French engineers Gustave Eiff\"]" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "markdown", + "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**." + ], + "metadata": { + "id": "QQMjaNrjsU5_" + } + }, + { + "cell_type": "code", + "source": [ + "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\")" + ], + "metadata": { + "id": "yFfaXG0WsQuE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "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." + ], + "metadata": { + "id": "f422JgM9sdVT" + } + }, + { + "cell_type": "code", + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", + "\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", + "\n", + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" + ], + "metadata": { + "id": "iHjt_SMYsd3P" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### GGUF / llama.cpp Conversion\n", + "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", + "\n", + "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", + "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", + "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", + "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K." + ], + "metadata": { + "id": "TCv4vXHd61i7" + } + }, + { + "cell_type": "code", + "source": [ + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")" + ], + "metadata": { + "id": "FqfebeAdT073" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)." + ], + "metadata": { + "id": "bDp0zNpwe6U_" + } + }, + { + "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", + "Some other links:\n", + "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n", + "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n", + "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n", + "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n", + "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n", + "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n", + "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n", + "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n", + "\n", + "

\n", + " \n", + " \n", + " Support our work if you can! Thanks!\n", + "
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[PATCH 2/2] Delete 20.Alpaca_+_Llama_3_8b_full_example.ipynb --- 20.Alpaca_+_Llama_3_8b_full_example.ipynb | 5051 --------------------- 1 file changed, 5051 deletions(-) delete mode 100644 20.Alpaca_+_Llama_3_8b_full_example.ipynb diff --git a/20.Alpaca_+_Llama_3_8b_full_example.ipynb b/20.Alpaca_+_Llama_3_8b_full_example.ipynb deleted file mode 100644 index 7f4e741..0000000 --- a/20.Alpaca_+_Llama_3_8b_full_example.ipynb +++ /dev/null @@ -1,5051 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", - "
\n", - " \n", - " \n", - " Join Discord if you need help + ⭐ Star us on Github ⭐\n", - "
\n", - "\n", - "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#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.**" - ], - "metadata": { - "id": "IqM-T1RTzY6C" - } - }, - { - "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", - "# 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" - ] - }, - { - "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": 388, - "referenced_widgets": [ - "c78e69db216844a39fac827349845f81", - "f2c23b7309ec430c9cb984ba8f511302", - "451a5048b6f84360aacbda342448e0da", - "980076d4a6c44020aed6d19ebfaaaa54", - "b869fd94c21146b4917ff3e60654664b", - "526e11b93f064ace9b6a255da796fdd7", - "0df7c15449ed45edb9094e5d58d0731d", - "488bb3579b99411ea03d25c0e916e629", - "ab53150e33004a41ab28c06833ed5aa0", - "1a4b589eed4f4d9694cae1f69330836a", - "a419b86d8ff0428b88843b55cf0be657", - "85ee709c047c49c7b7888caf9eff3a54", - "84569f44a689473889246c2eeab5d8a1", - "999ed6e6f8b24c5bbfd582c478f45c57", - "72072eb832db4889beef6f3fa5d45044", - "b87419aaa7cf4ed48b96ebce680bb29b", - "5e1d1523a0c34285a6ef4aa13e9cc645", - "e4ff98a12bc1424dadd9a8dec3afb2b3", - "92eb4d8be9a64892ae5e92c809f568d0", - "5b40ea812830452c8bdc46dbc0dda54f", - "467bd804fb244a298b8caaf0c3b3a3d4", - "5480935a6ba341bbb62cbfb7d8707fec", - "9a65ad628ffc4e2d87479514be357617", - "a98617118be94b73bbd2043c5dd65621", - "126fe7d2d8cd454eb7f71bec0b9b6e01", - "3cfc4b6de45f4ff5b8f23c3b79e6503f", - "f50c67dc31dc48aa978909d8967c5294", - "5326096097064f4aa0650ca59ead43aa", - "34f78b3e7ea0400b90d48fb2809a654d", - "f753790028d14420b2683c2bc16dba2b", - "b11371e3382440219af0d82b4fae0fe8", - "d091d3f8b01a473ea81f5f62567a6407", - "973e0a9b040f494895f60ba8e1468c6e", - "66f169549d2847e097cb9788e3e4d29c", - "a3709b260001400e85db6464542c66d3", - "7d7726f17a4a462a871f5a97b3d66928", - "3ed36820b9244e878c6610c589d7d821", - "eb07173547f14d428d8549e88b4f9564", - "50bd0e7f67f94c9eab8f42493332041c", - "b41fbbe7b59b4e13b17244edba47af30", - "5c64d6e41ddc46da81e4f92f4c88e6d1", - "b46d2111e0ee41b9b112f7c9fc65028f", - "5557222f4157411d9250ad82827a2f8f", - "b8d26c1ec5c44e1f9c8d20bb6d4c9d1e", - "2d0462c3fd21481fa6adf8fc9df5c28f", - "15248beee1f042f29ba17deb3e29c90b", - "c75a509df90340e5857692400bbb8af8", - "e7611888e9fb47588aee414cdf0ecf27", - "2c95bc4f3f9d42be8f03bef23242af66", - "396e74209ce9460a885b2236d1fb7b97", - "a8ec27381f564d7cb6962ee503d2eae6", - "c7fe113e1b3b46968d97cdc6fb1dc241", - "fcd3a5f67e164712bcf7e8e7ff1f770a", - "1eef300d637145d68a660db46ca9b14a", - "469b8be78b924d3aa90335798775e1c6", - "96f9b00a55f34d4c8a4ed3c496e1ffbf", - "0f36928c39f641618e89a27445c5cbc2", - "d4e450ceb7da45e9b8ae82bec8295343", - "b997bc55dca04d0f8880447ec9f9ba31", - "b1a3d509b7e643fca992dd838bacc203", - "7d03bcf25c774fed864b09770cfe74ea", - "09a4c26458f24fc49aadae2fee60acc4", - "41eaa431bdd94075b0c41899c14c3fc3", - "7734e446cfbd4e9b9f796ba662f2ab71", - "a96216197bda4d73afdfd657567bd059", - "db420165996143ecb8b6624ef6341912" - ] - }, - "id": "QmUBVEnvCDJv", - "outputId": "e14a20ca-5800-4653-9a6f-8a58513f642d" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "config.json: 0%| | 0.00/1.14k [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, # 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", - "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)." - ], - "metadata": { - "id": "vITh0KVJ10qX" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LjY75GoYUCB8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 145, - "referenced_widgets": [ - "bbea39204f514d65a92dbe79e4d8e697", - "161dc482ea1f4f129f355ee4d20e8cd5", - "208f49c8a05f4f25906fb7df2fd95d7c", - "1bf51ccd8640436482357b6e06a2f850", - "1ea78447b4fc4a38aaff65f7954ae65e", - "15f5446cc9d0433281001eb149c51cdd", - "f27dfff3b0534f558d91747a91629523", - "dd7c9e9d3fca4f0aa6893a48c92e6748", - "606650950feb431e825cb7ea79c5a1d7", - "8ce3a2df43bf48429e29bd73dac5e638", - "db00b79287da4c93b425d4b07b0f7b72", - "10c75c3affac4c839a065c5d07ff7fd3", - "7d9adc9aaa6540c3b80fb2b4e02f7b66", - "2e24b601930a44b58d5d1f53b33315f7", - "bb0f6ed8b0314e119254b574de524f6f", - "355b4205fa0e4612954dc918c9cc6449", - "f9e8a5f5102040f380d3287a93cfa045", - "ab3e06fd917e4cc59f5b8e275caba930", - "4bba5aefb61e4b458381bff09abbe96a", - "6f37c03d14314e59bef4ff1bc985e866", - "0c2631134e6248459fdfe8f48205df25", - "0ec293e756104409b563d3e561ec2937", - "5b7210bdfdd547abb29abbbb26dce37e", - "adc97fcb04834c7b9417597d9de135cd", - "f5b2f797ddae451e8fc1dca9bece278c", - "6bf7fcaf8dc347daaf2f5b04fac24300", - "3018574e40ef4f5284a1832d2efb1121", - "6038bb8ebaf945da8e8c470fa7d436ec", - "c9b628fa9ce34162b6d04d4f191f819d", - "d92aeeaaa8b74016bfcbe49cec44f0d7", - "348691f1aa714d16bfc3148b39518b36", - "8a372927fe8d4a09b4e35bdfc77a6136", - "4bf5cf0af2e441bf8d101795f8a82594", - "45560e601cdb484c8e9293dece7555db", - "9dd2481401cc43acb405a64e9ce9f13f", - "772124f5d8f54e959dff5d56d0335e4c", - "9e3f29ee68524e749a3dbf1ba32653b6", - "fd8ffc5653b94428bb3b82d3979ef54c", - "e288762221be4e0796631bb7b41bea56", - "f94493a870af47c98ded7a71e1614681", - "3b9a0019602a42cd8bbd80a77be5cfcd", - "5fc82503d5984fc0bfe364b2ae5ad530", - "9b3a7554fbf14276b208fce9d24b5b89", - "c8491efcfb72456f8d629acc49dfac90" - ] - }, - "outputId": "51d2433f-c01a-42f8-eb04-498d5406ae70" - }, - "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": 104, - "referenced_widgets": [ - "f07f34e9b5c649469ac667239b0fbaad", - "014479d51b95488da6dbdbfa3e7adce3", - "320ad99d9e91463986ca91073e3729fa", - "acb68ec0ac33408b8d02f22cfbbe4f01", - "30f2bb04d6754a54af59439cef40ff87", - "1b01f784c04e4d11a2afd72284646138", - "5f7db89c6f0b4c968dd8f43e23f54365", - "08a476cadd42469eb2234ac503f8447e", - "8019cfd942a0477e92cc3a34ecc826e6", - "8516663d35be4bf48ce4c54f58188075", - "60d93f53511c4fc1adf11bacf22590cc" - ] - }, - "outputId": "842ad9fb-3ce2-4668-a2bf-60f583d05a2f" - }, - "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", - "
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11.814600
22.293200
31.689600
41.952800
51.645600
61.639600
71.217400
81.246600
91.069000
101.174000
110.963300
120.991200
130.929300
141.050700
150.883800
160.877100
171.006000
181.264100
190.988600
200.885600
210.921400
220.992100
230.859300
240.983200
251.070400
261.012900
271.049900
280.869200
290.843500
300.903100
310.866300
320.873700
330.998300
340.863400
350.965900
360.865500
370.891300
380.773200
391.096600
401.172000
410.911400
420.992800
430.960600
440.911000
450.932200
461.000000
470.876400
481.225000
490.923800
501.049000
511.033400
520.939600
531.016800
541.174100
550.801200
561.033900
570.889000
580.835500
590.868500
600.911400

" - ] - }, - "metadata": {} - } - ], - "source": [ - "trainer_stats = trainer.train()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "pCqnaKmlO1U9", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a176103c-c9eb-4b13-9488-b0ad1bd945b3" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "424.2005 seconds used for training.\n", - "7.07 minutes used for training.\n", - "Peak reserved memory = 8.982 GB.\n", - "Peak reserved memory for training = 3.314 GB.\n", - "Peak reserved memory % of max memory = 60.903 %.\n", - "Peak reserved memory for training % of max memory = 22.471 %.\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": [ - "# 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)" - ], - "metadata": { - "id": "kR3gIAX-SM2q", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "cd29185d-4433-450f-ea19-f3ad6947ae3d" - }, - "execution_count": null, - "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, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" - ] - }, - "metadata": {}, - "execution_count": 9 - } - ] - }, - { - "cell_type": "markdown", - "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!" - ], - "metadata": { - "id": "CrSvZObor0lY" - } - }, - { - "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", - " \"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)" - ], - "metadata": { - "id": "e2pEuRb1r2Vg", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "0d250429-e431-4fc8-9edf-97e5fdc81da8" - }, - "execution_count": null, - "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, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040, 1346269, 2178309, 3524578, 5702887, 9227465, 14930352, 24157817, 39088169, 632459\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "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!" - ], - "metadata": { - "id": "uMuVrWbjAzhc" - } - }, - { - "cell_type": "code", - "source": [ - "model.save_pretrained(\"lora_model\") # Local saving\n", - "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # 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 for inference, set `False` to `True`:" - ], - "metadata": { - "id": "AEEcJ4qfC7Lp" - } - }, - { - "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", - "\n", - "# alpaca_prompt = You MUST copy from above!\n", - "\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", - "\n", - "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", - "tokenizer.batch_decode(outputs)" - ], - "metadata": { - "id": "MKX_XKs_BNZR", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "1e563fe1-7dd2-4851-99e0-017392b5cac6" - }, - "execution_count": null, - "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:\\nOne of the most famous tall towers in Paris is the Eiffel Tower. It is a wrought iron tower located on the Champ de Mars in Paris, France. It was built in 1889 as the entrance to the 1889 World's Fair, and it was designed by the French engineers Gustave Eiff\"]" - ] - }, - "metadata": {}, - "execution_count": 12 - } - ] - }, - { - "cell_type": "markdown", - "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**." - ], - "metadata": { - "id": "QQMjaNrjsU5_" - } - }, - { - "cell_type": "code", - "source": [ - "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\")" - ], - "metadata": { - "id": "yFfaXG0WsQuE" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "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." - ], - "metadata": { - "id": "f422JgM9sdVT" - } - }, - { - "cell_type": "code", - "source": [ - "# Merge to 16bit\n", - "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", - "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", - "\n", - "# Merge to 4bit\n", - "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", - "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", - "\n", - "# Just LoRA adapters\n", - "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", - "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" - ], - "metadata": { - "id": "iHjt_SMYsd3P" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### GGUF / llama.cpp Conversion\n", - "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", - "\n", - "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", - "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", - "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", - "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K." - ], - "metadata": { - "id": "TCv4vXHd61i7" - } - }, - { - "cell_type": "code", - "source": [ - "# Save to 8bit Q8_0\n", - "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", - "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", - "\n", - "# Save to 16bit GGUF\n", - "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", - "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", - "\n", - "# Save to q4_k_m GGUF\n", - "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", - "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")" - ], - "metadata": { - "id": "FqfebeAdT073" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)." - ], - "metadata": { - "id": "bDp0zNpwe6U_" - } - }, - { - "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", - "Some other links:\n", - "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n", - "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n", - "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n", - "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n", - "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n", - "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n", - "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n", - "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n", - "\n", - "

\n", - " \n", - " \n", - " Support our work if you can! Thanks!\n", - "
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