{ "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 + 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)." ], "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", "if major_version >= 8:\n", " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", " !pip install \"unsloth[colab_ampere] @ git+https://github.com/unslothai/unsloth.git\"\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n", "pass\n", "\n", "!pip install \"git+https://github.com/huggingface/transformers.git\" # Native 4bit loading works!" ] }, { "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": 527, "referenced_widgets": [ "3a4a21c80b1e4232825d13fbceb4e051", "f16087f7a4f94b18ae92a2615bd3719c", "06eac325390f467d9825c489eb4cb442", "9309cba5e6a64d62af82b8e8403336ba", "c339d8fa8d7d4ad8874bc59f47ef193d", "c73d502c8be34400bd4fd31f1c14f354", "34e7e54d02cf4120acc14cfa9430674d", "52995fc47dcc4b6bbbba3a158370da1b", "5a88fa6168b34baca965387de6f9cbf4", "320fff7da29b49d0980a5e19ecdd7e11", "b0caf6ffd475467b9c26e9258902a6d7", "8978f89cdfd64c41b504debc99fa000b", "fad0db53f8d84346996a49bb8548a28f", "d719af3c7e434c4fbd88ce6442ff94f3", "1dffcda7345949cca7a01373e87bef91", "2da70f09f484477ebbb8f81458556515", "db33c13036a74c5eb2bcab082f9ce07d", "dca1b3a080e74bf3b00a27dd1ed4b99c", "838d7a4c2c3a4fe99a3eb0689fa7ef27", "e0ed107826af43db93b4fbd171bb9f36", "1eef883b99634b8abefd9c008eddbd1f", "a858db7a4c8a40a5bedc4ea817491c4c", "c52fb02a25774735bc6cd1fd4085ffb2", "2c64fd94311247d89b2bbb37b0a72d52", "28ffcbb08f9c443c87df1b6d6d5f38bb", "699373e9a43d4433b1b3eacdc4eb338d", "ed41c69c299e42ccaea8cf4e31162b8e", "f85bc895a6ee4c5b8cfd26a1c827888f", "e5f70528f2834d2b861dc303346eff3a", "e76d203cc320464cadfc7b2649d8fdb7", "0b2301c8190944a3bf8935c4bf075d8c", "ead0df4cb60a4223b6a2d27814971093", "5b1aa79e9567426d893b92bab1f1cc40", "6a46284e269e4044b2e082098fb0988f", "c5311843eaf640fd885b125d32fec217", "aed33eb228e84f77bf141b5141dab343", "27cee145c0774440ba13e7033db70b14", "613978ea26b94edeac575404c09edc52", "d05f155563dd45c9a0cd76d7d0ee38f9", "cf6fe6d02df5448986728647e0442031", "cd12b6b9c1204fe18733c1862d0900e2", "612b4afa75734acba7ab7917a70ece9a", "9ca704fdbe8843239b76380e1735bc25", "07087433b7984095a400cffe9c47b38d", "3e463765bc424c67b88b5b522fd12f36", "9cf66b807829446f9985efbddee49228", "a836c026fe3a4a3d8c981b436b6af487", "fd99cd77c6e2476aaa4f32bbf2fae893", "bb4a77f4f8fc4477b1987a0a69ee767e", "9cb14f593e754af19eaab25521b92907", "2237cb2d037e4945a7858c3550d12588", "c694dd6f0d3b4971b11b662b156fc773", "13578e0496f644a4a454d96645dbf65f", "abdfbaceb7e24276958b71512b35ddc9", "8ebff9bab3d240c4a056c6c88a864773", "ae42b52c748240f2bcbfea01b4573622", "c590336dde92421c8126df142cc1d05c", "c2326fa217954f389b973478abd98275", "b82fba3690c74fc08ec2e8b382245809", "bc231a99984a4c0d9e6081b339d690e9", "f2f113c785114890b3eaed0219e2d0f3", "bffa3542ab7f4b009da51ba63d56e478", "e50f02bb85d44156887f90ad6aca8102", "53ceff04ca854c38838dd982b75c5b05", "2d6d42bf728c42fea2c225e7fb59d582", "b0543ff9bb5d40fba101c988bfbc027d", "b5eae4bf90bc4f10baa3cef668109188", "f15b829dd14a4c95a390721dfd15e44c", "c5ca8a1293aa46c397bc95bbc239f7e1", "a7705bbedfcc45f8b227186a619fdb88", "238205803c234c7fac3106429d86ceee", "f8d4f91fcd8440bbb41efbdcaed2128f", "2223525ba0014e018e4833305c7eb26a", "36082006c1d84d0e8a291e05714d4419", "0332ff4a20b34672bae74bebf98cc6e2", "ac5ff6c3f94840e0a856e748d614f985", "2ab87d2720a243e9ba6942a8a3953c15" ] }, "id": "QmUBVEnvCDJv", "outputId": "a1a670b2-ee62-4bbf-901a-38f79389db28" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:67: UserWarning: Running `ldconfig /usr/lib64-nvidia` to link CUDA.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.05k [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", " use_gradient_checkpointing = True,\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!" ], "metadata": { "id": "vITh0KVJ10qX" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LjY75GoYUCB8", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "e0ea75df3b5c49ce89427e4d245a7646", "02237d111eba4155ab5a48a1b33d82a4", "927e1d33248d4653a785049b50b6d814", "09979ecafec9443ba1f7335ed64de778", "088a000de2234c9f94a8b09e8a8abbb2", "38c7b4ead56b4540bf68fbe3a5496b9c", "9a0787c7c98b49b8a9141e5212faa249", "daf62811f5384d0f9aea58b40f23161a", "6f90172dd5c240c885140d49add6208f", "187078f1978843f2b873cee2ee55ac91", "ca8af702ee764e7fa620476854a4f2cf", "80b9d976e30e40ecaf35a606bca5d647", "e9b423d370314fdfa3c22e95c3a35d92", "307f44c0a4da4d3ca0d44b54f9a5f6c0", "5503539430024c7586d4f8589c92fd74", "b17542f2cd0f45e79e755df622328358", "fe6d1bab4fd042b5af89f6bb8a73cc39", "8cb7748eaa354d4ea0e3222685f1d9b4", "53bfefa3e2c04cce9ab7d2b3fbca59d9", "aa58f2124335451890857ded72414ecf", "b331b446e257441387b331d822f570a0", "49e3298fbe054c7abb6a041ecd63737c", "6b104e7242cf4a608cfd0c4fc6f44db1", "1a6288e052884d5aa486415880acf134", "289c8b4d1686443f8bed7441673a2c15", "1318a594d2df47779b84f22e4b0c0702", "355ecaa94d1a43b09e1d0b3f6e6ac8e3", "156432d2f1b74697bd74604e1bd5bf13", "976fa37d0fa14282bc96b3c948b9da94", "24768bbd587d4b1ba09b6c52dcd6237c", "cdf054995f314976b7124de9564cbd9b", "1eec25504a6f41d199940c4232b0a114", "007a9dbfb5c84efd8ffd801bc04c0c20", "2ffddabe91124b279f9a3ac41d1ead9d", "f97edc77840e49c49aed8e7ab80f45d1", "33761916bce24fde9bcff866736ddad7", "726aae420987454e87a08b9314844895", "071858b3801e4844a4fc5a91205335c4", "6a98441b0ff74361a79c96178834d8bc", "40e8b59e229548c68ccb339234a5e525", "56b011c2482b427483508e259dc231fd", "507c15541fe7489aa1fd1fd81c4aa222", "4bf05ee528bb4bb2979cde4d0a09544b", "d62a5696b9704580b69e6fc3be9ffa8a" ] }, "outputId": "26e1bc8e-c4e8-472e-ca91-670e3381c6b3" }, "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": 177, "referenced_widgets": [ "477d5041a08f4a3a9f7cfa1d98ab48ff", "25eecdf89b8845a989ce2c8c4d9edbeb", "e02470ee64ad4f0fb2160b088cdcba7f", "b7c0858a80684aed9c19ce00dda85815", "d0b82646d6f549d7ad59e9ff5f426e88", "d94a8d1f9d294abbb156e39da065910f", "ebb0f92e750447a39ff23a23ea73b445", "d4d01dc3290b4174ab13454a28faf972", "c6941f8c60ef49a8a79ed265c46652c2", "24aa300026334db1b545bf0fa906112e", "6d0d021108b54147a62e50fea1ba88ea", "deb2982b19764ed5aec0b4d80e776279", "c24904c0a7294f3a93bb64aa38e70316", "26a77ab74a4a4e21b9afd9798a9f9a29", "7189bac8d0474bcea50cf8711259516c", "ce81350896a44331aa6b6960b7370325", "73d4af57ddc64fb3afd0e2ab068cbcb4", "672d990adee44df6b58e27fe5804986f", "3fe95fc9bc034a2db85ed19aedfd4250", "c1c0053b8e674ed6ac81316d4af82c48", "74a0e53405cd4d64bd597ad5461ed5dd", "278d35f6e08d45e2a49c3509dd442f0c", "b19d83c6f5ad4f04941e6a684678ac07", "88e100a175e64a3dab6f772847deffd6", "457b5df3a4294c8a966e233d34c15a82", "18af2c44a24b4aaabfd192e3fc4bf655", "61944d9473394be5b19cfd48fd504481", "68d18369acc540a594aef61a2de07e63", "69c676782e0b496b820b55c45424177d", "08dcaabc623c4759a00fbee5430e3ba9", "45a3bcaffc3f4184b9ae869f7b0ccef4", "c45f02fbb40e4041ba7f95074f8e1e82", "0fc1037a17e541eba92a2d6f400ac6eb", "7bd2cc9aa724408fa9e70795744cad85", "fb0588bffd7a4238bac3f73052e09335", "68ff2006b3794e23b4ccbc2c83c52b9b", "910f2e6fffd24cd7b6e68c932c2a2524", "6345dc6f40c6444aa05ed2aba7809a3c", "92eab7fadbed4bc2bc2935b4e3d800cf", "2dfef2cd79c8463cb7eadad6a04aba91", "3be5c37493e742aebf0aa29b6723283b", "273be47263384901b6cf9f249ee3409d", "2ff515b72bbb43c889e2172c83933803", "a1cd830a712d490181d176267ce7b6f0", "1a8da471604841ec9f6c8e0073471c00", "f899ae16708544379d63960be07b7c32", "0bdbf9b92f7b4925a5ac28df93fd0fb0", "c1d8820a789f4899a839e6d28a4f333c", "e711d7f85eee4fe195fad9fbddcfece2", "56ed5fd876d94ebbaa8b4e905c348a0d", "5d461f10bdc44c6b95d070fa9d7425d1", "f4e0e7a39ad9484f930e6673c35c2f5d", "baad9118cade4cee9600a7fbf6426e0a", "16fac1aad22444c4ad42ad0e6e1dfdc9", "ac35368de2d746b4bed736459d187dbd" ] }, "outputId": "adb8cb5d-0ec3-4b79-83a7-5691873873e8" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/971 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 07:52, Epoch 0/1]\n", "
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StepTraining Loss
11.408900
21.709700
31.148400
41.177500
50.986600
60.998200
71.032900
80.887600
90.937500
100.932100
110.902500
120.925300
130.814800
140.978600
150.820800
160.878900
171.005700
180.720300
190.589200
200.721600
210.883800
220.929200
230.754100
240.748900
250.896000
260.735100
270.859300
280.861700
290.785500
300.744700
310.793400
320.754200
330.857900
340.849700
350.738900
360.751700
370.762700
380.801600
390.886300
400.925500
410.730600
420.593000
430.695600
440.660200
450.964300
460.575800
470.752600
480.757200
490.714300
500.959800
510.829300
520.711700
530.841900
540.920200
550.717800
560.948100
570.741000
580.893100
590.771400
600.879800

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pCqnaKmlO1U9", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ff1b0842-5966-4dc2-bd98-c20832526b31" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "484.744 seconds used for training.\n", "8.08 minutes used for training.\n", "Peak reserved memory = 6.846 GB.\n", "Peak reserved memory for training = 2.221 GB.\n", "Peak reserved memory % of max memory = 46.42 %.\n", "Peak reserved memory for training % of max memory = 15.06 %.\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", "\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", "]*1, 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": "9f3bd036-9ed6-40af-b4e8-6193b2088d3b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 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:\\nThe next number in the Fibonacci sequence is 13.']" ] }, "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", "\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", "]*1, 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/" }, "id": "e2pEuRb1r2Vg", "outputId": "2188f68b-6b72-46e6-ea85-f8134df0aa46" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 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", "The next number in the Fibonacci sequence is 13.\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", "\n", "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "{}\n", "\n", "### Input:\n", "{}\n", "\n", "### Response:\n", "{}\"\"\"\n", "\n", "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", "]*1, 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": "68f54910-2634-4cce-f3a2-4cbb90e2e990" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 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, France. It is located on the Champ de Mars and is one of the most recognizable symbols of the city. The tower was built in 1889 as the entrance arch to the 1889 World's Fair and was\"]" ] }, "metadata": {}, "execution_count": 13 } ] }, { "cell_type": "markdown", "source": [ "You can also use Hugging Face's `AutoModelForPeftCausalLM`" ], "metadata": { "id": "QQMjaNrjsU5_" } }, { "cell_type": "code", "source": [ "if False:\n", " from peft import AutoModelForPeftCausalLM\n", " from transformers import AutoTokenizer\n", " model = AutoModelForPeftCausalLM.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!" ], "metadata": { "id": "f422JgM9sdVT" } }, { "cell_type": "code", "source": [ "# Merge to 16bit\n", "if False: model.save_pretrained_merged(\"x\", tokenizer, save_method = \"merged_16bit\",)\n", "if False: model.push_to_hub_merged(\"hf_user/x\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", "\n", "# Merge to 4bit\n", "if False: model.save_pretrained_merged(\"x\", tokenizer, save_method = \"merged_4bit\",)\n", "if False: model.push_to_hub_merged(\"hf_user/x\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", "\n", "# Just LoRA adapters\n", "if False: model.save_pretrained_merged(\"x\", tokenizer, save_method = \"lora\",)\n", "if False: model.push_to_hub_merged(\"hf_user/x\", 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." ], "metadata": { "id": "TCv4vXHd61i7" } }, { "cell_type": "code", "source": [ "# Save to 8bit Q8_0\n", "if False: model.save_pretrained_gguf(\"x\", tokenizer,)\n", "if False: model.push_to_hub_gguf(\"hf_user/x\", tokenizer, token = \"\")\n", "\n", "# Save to 16bit GGUF\n", "if False: model.save_pretrained_gguf(\"x\", tokenizer, quantization_method = \"f16\")\n", "if False: model.push_to_hub_gguf(\"hf_user/x\", tokenizer, quantization_method = \"f16\", token = \"\")\n", "\n", "# Save to q4_k_m GGUF\n", "if False: model.save_pretrained_gguf(\"x\", tokenizer, quantization_method = \"q4_k_m\")\n", "if False: model.push_to_hub_gguf(\"hf_user/x\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")" ], "metadata": { "id": "FqfebeAdT073" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now, use the `x.gguf` file or `x-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. Llama 7b [free Kaggle](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp)\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", "\n", "

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