{ "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", "# 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", "* 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.\n", "* [**NEW**] We make Gemma 6 trillion tokens **2.5x faster**! See our [Gemma notebook](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)" ], "metadata": { "id": "r2v_X2fA0Df5" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 403, "referenced_widgets": [ "4b6316870ae142ba9abf2b399edaaeea", "c3315eb9e7ef42c298b7ba5f7636c373", "b553db45b4204434b015593b84861d9d", "ee88fbb010e349d4a840275150cc59eb", "5d9ac0fdb1c24c59a89390b3fbb6481e", "ee2d57cc909b47e7a96a6348c3bc64c0", "e8f5782342af48549b22edf99517e71d", "b520ce59a04444f6b5c5c9351c02877d", "52a94645e9ca44768f44d46e5a4f2f87", "f800e837619e4ebbb860a5648a21a078", "be41d9ff020b4a4a8f7ec9a5c5aba8f8", "78d1378806944b54b6dc7d9088d0540a", "16486c7ebb0c40cbb05b0f1a146004bf", "3dc95ac19cb948b78179a8617917ec44", "aacd5f3b4d6c4730b13c4e75b461bcca", "9c439e6ecc57445092c0ea43d6c46640", "07cde8ce0ca54e3a95a183541d26bc2e", "2f6b00e4bbfc48f5881a8ae386fa8a3a", "e15aa09fe7d74cb7b348bfe142a8b172", "63f47e74c4b644f1b8e35f392a6a626e", "1f5d1e8ff5ea47e4aa04c12bdf2f8df7", "b25110a1ed2b4fe8ae7fc5cdd724362b", "661c67646b5948449c2d71f5814776b4", "2ea177d7a1904285a4f641c7581736b9", "8234c35bfe33441a8fdcf48dccc0563b", "50d47462e5ea45a7a3e75b88fa3e272e", "75c8a575acd94d0ab2ee673af56f56c1", "f07da0c5e3da4213ade400cb3a6db827", "6ef59867eace4a8aba3bc6652b579b7e", "6d603e8d4b504239adf1820f6c1b608e", "cab2232116d647ef94aa89ffa0515774", "077d992a3da3424194ee45301e4c02ba", "0c5fe7ba93aa496a9eea2bdb8d692fb5", "3f40e8b99e1043e8917ff2c3e1e3dd29", "0fabb8d03b0d42178538a829c843dfed", "5c6d56ca98e146d6853289851fe5baf7", "07e004c9526c4481836d68ef53c9b669", "128211077ca940d499d90667ad27dacb", "6e6b1089e6504aa5a2ccc61953b5e433", "0ef52843237c48dd9566c4dff3e7b42c", "c7fd527ba5e74ddfb3a1376bb219b6c1", "c4a594e3a66d49989c914bbc93b679c1", "c87c9608e866410ba46d49b44d0867ca", "e5a03a389d9a4f68bbc7c7939ecf7206", "ced6f4b20c304ab6b19858766505a596", "62ddc3bbcdcc4cb2993828d860ffaacd", "11d662dad31041baba79243d3ed687c0", "4f1a242530a3417c8309a7eb824f0c4a", "10ba528042394217b6cf5d3bd6288db0", "f5a09713f5d2419cac940042d7900921", "eb11542214a344c7827fa28472f4b5ef", "cd0f5f8d4f3345e09bb76f11ad197934", "ccfcff888b8941a9987071ea8c284d17", "c947d50a70694e0f8cbe5351b8219938", "e32de93685f249828a0a73a52cf1438b", "3f03fca984754fd7897ac389e661511e", "8907ec143bb8415fb045fa3480a0c024", "7347484610f9459ba1b11f32825b9a6f", "63f7b027083f44139b387ed6595a5f10", "2a5c6adc4491433392b708c854d05786", "b1eb529e794c4afb9591a92f383234c7", "e2906dc03b4640d6aea44b52f6292189", "04bb72bc05384a7fab8384b96a9165b0", "a9c322a9110d44b8b06438c6f6f5acac", "de7ab932d6954f73adef9fffaa6afee0", "a5b22ec5d43345a2b957301fed556b94", "ff1229fe81b2467892fcebb5516c586a", "9bc4151c386e4fe49b2aa882159aff88", "4d2e7f9d8ec34c24a869d75c1e08cf54", "e2b109ab9d664847b1611783ed75b240", "9195aa2c13a247b6836701b7d47f8f6d", "bd68af52e4014418b87adc1338bc7047", "0f5ea613817347839697acfec3aef00b", "8d8d93e079fb43479c6aa736837482c7", "2178818d79ff4963b663e74c04303912", "9653b6a3d7fd4014ad6261b8a91d0225", "4f2138b17ad24fa894566001543e7e55" ] }, "id": "QmUBVEnvCDJv", "outputId": "5eff0d61-05b4-471c-eea2-c2e84a915109" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:66: UserWarning: Running `ldconfig /usr/lib64-nvidia` to link CUDA.\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!\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": [ "9ae302db43ac477fbfac4524d9aa3382", "094b6478ec2b402fac8d4fc0c2921687", "64ae5dbec3fa4ea3abbada99ddc5b95f", "d2f03ac06cf24ecbaf9fecebfbc75704", "e00d429535b2418cb3f2caafcb661b2b", "2aaf95a7cdeb424bb369b658b0d91850", "439a8d79e8064125b694e23d8b3cda99", "05c7968b32744985bdd67900cc5cc5d0", "e90def295593458e99241c538186c3b5", "8fb7fa4f8a274d3eb3e58953b1e673b0", "6404a027f5b0439786996b45695ea17c", "e7163211ff064cf68f2a9a92a1a44b14", "75da1ca588cd427083ebcc98af74ad45", "a0e64b31b0744d6f9a540d6d71f29898", "0fda3d9d81764040b701f44c022d3a97", "a629a4f39f904d918cfb1940f2756cb1", "25b8bf991b234f30af21842b423e8c7d", "dd1377f789ad4439b6bf99843fe4ca9a", "92a3e0edfd034150b1ef3f07f21957c5", "caf3a07d739144cca5a246be40ed1d2c", "3f4d8437a14d42f1adb2314b8f090f91", "de5acf3da485445d9955603111bc9bb1", "49606713136e4b4f809e88edec0ee31f", "41a9ff9396454511ac9ec7cb8cd73999", "90f3e5054101499a9d623edd73a58aaf", "8d2a2929aed1450f909f67a82efb072c", "00daf2c57f724c5b8581554ca2f2b2c1", "221f374f77914569ad4cf0464e8c183e", "f52c132e71ce4f919abcff5a7df10a52", "45e6fb6a4f144daab3f859cadabff196", "fdcf4a1a700f4688ab19d55af784cf42", "f02cb5a79f404c3db58a86e010d929b5", "64763f108b13475d85f44f4eb1f4a7f7", "add655fe6f304c5b8c7f5c374f73fe84", "76de4b26f31b45f9a8eb31413badd4d0", "4c31d1803e134ed38a67c9cd61097a28", "3edd0e9e876f4429be155e3378922f30", "b1e594f36f3c4751b648abcbb74c28cd", "304eef6608044f45ae0e78c4ce32233a", "fb2e02192e1f4972bc75441d217168e1", "92d2d40d99304e18b11bcb01093ad510", "4dc2fb7510824a78bd12f58fc2594a89", "8b01eafd97dd48cfb86952d0946aa856", "0ba86603e45d473f89ce572c6874f2fe" ] }, "outputId": "9f40f734-788c-4793-c1af-e9d003337612" }, "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": 49, "referenced_widgets": [ "28408ab4afb7494f8cb2f834458f411c", "b85ef639d1f54fb3ac8902fb66be79eb", "0c266c0637064fbe8d548d0876c6b315", "aa315e0b196d4b80a114690a689d5fbf", "5b6f098adcb84717b162152ddaadfa0b", "caaf2d5a555f41fab3b74f3096375ba9", "1789d926e02549b19e4aca59f44f320a", "3816c6f816784e5ca11a458bbf790031", "954d6540772d40a99f642c2524db3a0c", "fdc5073e7cc24edc8c5b920a363b4a40", "f69d18a0b745434d8d078939e3a0c7fc" ] }, "outputId": "4b809e6d-271f-446f-dec8-abe0d13259f8" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map (num_proc=2): 0%| | 0/51760 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 07:56, Epoch 0/1]\n", "
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StepTraining Loss
11.408900
21.709700
31.148400
41.177400
50.986600
60.998200
71.032900
80.887500
90.937400
100.931900
110.902100
120.923200
130.813900
140.978600
150.820900
160.879200
171.005600
180.720100
190.588800
200.721400
210.884100
220.929400
230.754300
240.748600
250.895600
260.735300
270.859500
280.861600
290.785600
300.746100
310.793800
320.757500
330.858700
340.851300
350.739500
360.754100
370.767000
380.801800
390.885900
400.927400
410.734400
420.596500
430.699700
440.661200
450.965600
460.576600
470.752800
480.757400
490.715200
500.961300
510.829200
520.713100
530.843700
540.921400
550.715100
560.948600
570.740700
580.893900
590.772700
600.880000

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pCqnaKmlO1U9", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "cf63d152-e152-468c-ba0d-938e0d2f71a0" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "488.38 seconds used for training.\n", "8.14 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", "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": "5b71f982-38c0-44c8-a4e5-58cd20b5a585" }, "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", "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": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "084aab62-2122-436a-c0cb-8871986640eb" }, "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", " 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": "05e5a193-dab0-41db-e07c-4b3afbdd7932" }, "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 structures in the world.']" ] }, "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", "9. Gemma 6 trillion tokens is 2.5x faster! [free Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)\n", "\n", "

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