{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "HcpwFPAh51-R" }, "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://docs.unsloth.ai/get-started/installing-+-updating).\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)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "iM0HrYe551-S" }, "source": [ "### News" ] }, { "cell_type": "markdown", "metadata": { "id": "YlBNJDGN51-S" }, "source": [ "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n", "\n", "Read our **[Qwen3 Guide](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** and check out our new **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants which outperforms other quantization methods!\n", "\n", "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" ] }, { "cell_type": "markdown", "metadata": { "id": "9JSHb-Ht51-S" }, "source": [ "### Installation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "W-MEvzGu51-S" }, "outputs": [], "source": [ "%%capture\n", "import os\n", "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n", " %pip install unsloth\n", "else:\n", " # Do this only in Colab notebooks! Otherwise use pip install unsloth\n", " !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n", " !pip install sentencepiece protobuf \"datasets>=3.4.1\" huggingface_hub hf_transfer\n", " !pip install --no-deps unsloth" ] }, { "cell_type": "markdown", "metadata": { "id": "QfXZVzCx51-S" }, "source": [ "### Unsloth" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 300, "referenced_widgets": [ "6e3c281f112b4a86af7a3ef95933d221", "92395f250a154006923aaf9ea0a9c30b", "f84bfc5390054ec687c157c4d68199a6", "4228734651ca45e19fc7bda79817f9b3", "4613edbbec6846edb5b1677c25d542b6", "d032fe2ba5d647d99026fdade758c0cd", "e9971d220fe24552a1e9aa299765cfb9", "2be29a4553ad4dfea8a9bc620c81a3ae", "94cbb87829d1486899e2ff6325c2ecdf", "f878c2e00bc240c7b0333cce950080e1", "6b908368de51428585552dfef6a83088", "ac5eacaaee8346c080e54ea7a52648a4", "7981edf408d54d41bbeac42da7492c6b", "2b848e5a85bc42bc87945fd9ed5db038", "e88c33f37d6849e0b1a6b41254104cb9", "33843107b93647b28985bfc37ea781ca", "76e5410e286a4a5abd6c213a38aa38bb", "ae7e90a811f94e75997d6a9ed1be8596", "bb9f3379310d4b04be694996f3137b28", "75082ba15db445df907f5612976590ae", "89934c4f26834f15b9889ec36fee3b65", "e887160635cb4803b9f33845df615ec6", "1c7bc5fdb7dd4c39af8d4c2c504ec3ed", "843a27e619534ea8914f9d36386c364b", "8f5adc70fbf248f2811527f620553be5", "4ffb4b2f015046fb94c1115ed0397a20", "5a232ed040f94633a2a374031284c1f6", "2006be31c09349738e221295bb84939f", "07055fc12b0841aaa5317f8252b5d347", "1eb90e686e214122ae763b1b79ae321d", "7a935956348e47c68fbdf05ddf4752f3", "8653acb618ad4e76bbf1daa00ea71238", "e3c3bd9c4c124b0a8c88c83c1fc747d3", "1bd75ddaf57c4438a4e2c3070b9cef65", "a3a3ef6d6337403cabea8b23f7c3021b", "c2ea0a3f01f34ffa8c94ab9b5098e9da", "68ea1d7cb8274a639b3fb5326f4218c3", "39fef7b257614a0595f39355fa226b69", "d125995cc0934239a01ba01b78529f21", "634ae4c6cfe04673b1cdc9c9cac4cbf9", "d7f92e8332374313bee87ccd427446a4", "36799fbcd90d43128620ff98225a825d", "5310346dd579424fa676b8e8e64790e7", "0c1835f404db4846bb13b5da8d8f4447", "29c5b713f07043dda51820523e5c8ff3", "d7375f0f048841b29a20601c122666e8", "f433ced9bfcd4a57ba691d3c1caeed08", "da8ffc70820a48f5a12c6d4b5967015b", "1a6db9aea6a64ae3aaef51d6265b35b2", "331f516c7a76456d801bc2a2feb228aa", "9be9074028da42d39d044a78393a861f", "51cd9026b1664819a67712996ca97bd5", "ea55293415ca48a4be97c2e1e4769122", "8ea52b105a7e44978caca33c0e7e815b", "3662f1445ef34a50b462e601ed31bb69" ] }, "id": "QmUBVEnvCDJv", "outputId": "0a47b925-663d-4543-9c61-994a6302f3c5" }, "outputs": [], "source": [ "from unsloth import FastLanguageModel\n", "import torch\n", "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n", "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", "\n", "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", "fourbit_models = [\n", " \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\", # Llama-3.1 15 trillion tokens model 2x faster!\n", " \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n", " \"unsloth/Meta-Llama-3.1-70B-bnb-4bit\",\n", " \"unsloth/Meta-Llama-3.1-405B-bnb-4bit\", # We also uploaded 4bit for 405b!\n", " \"unsloth/Mistral-Nemo-Base-2407-bnb-4bit\", # New Mistral 12b 2x faster!\n", " \"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit\",\n", " \"unsloth/mistral-7b-v0.3-bnb-4bit\", # Mistral v3 2x faster!\n", " \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n", " \"unsloth/Phi-3.5-mini-instruct\", # Phi-3.5 2x faster!\n", " \"unsloth/Phi-3-medium-4k-instruct\",\n", " \"unsloth/gemma-2-9b-bnb-4bit\",\n", " \"unsloth/gemma-2-27b-bnb-4bit\", # Gemma 2x faster!\n", "] # More models at https://huggingface.co/unsloth\n", "\n", "model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"unsloth/Meta-Llama-3.1-8B\",\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", " # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "SXd9bTZd1aaL" }, "source": [ "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6bZsfBuZDeCL", "outputId": "3e2a4618-6aa0-4f1c-d3a0-0ec45eb33237" }, "outputs": [], "source": [ "model = FastLanguageModel.get_peft_model(\n", " model,\n", " r = 16, # Choose any number > 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", "metadata": { "id": "vITh0KVJ10qX" }, "source": [ "\n", "### Data Prep\n", "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", "\n", "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", "\n", "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", "\n", "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Alpaca.ipynb)\n", "\n", "For text completions like novel writing, try this [notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_(7B)-Text_Completion.ipynb)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "5e8825fb770b41529f2129113cebc4a9", "0a9dc233674e4096b7a988a5e4ebaf84", "374fa9beda4042e1bf9a9b13de6e6674", "e6533d3c91fd4359bc84ffd8e59af5a3", "f9b01aebcbdc48a585b7942b0ee60a2d", "d4b3770433bc41818372b7aed243fb31", "19bcefcc1d874840ae9a9ca983e474b6", "4011ce9370d74fad857ec8e1e99d314f", "41f5fed060ad4c8d87b24602b720ef04", "88fcd51819b5483c9ab22df7ef89ab64", "e6ffac074f1b476ba2ade11b37732af3", "98a6716e7438429ea322adb3e3264f91", "68c686291b50430faeef0de7840e2c4b", "953625aa1e824f8a8d203197b316b302", "f899a815142542219bde22ff792fb60c", "51ca174d26e94b5cb1e895aa3c770655", "f4519637bb43400a80ce83505101e8a5", "805676b197c94f5aa45956daa354640b", "ce9fcc5eff1f460d80b703a4ca32dad1", "3fef797403d14440afe599a3bf06b626", "4e7cb8e988114ed4b6fe09ff9f682dff", "a14bc1c2130842568a5fde6698731e5f", "85cc6f24cba54563acb5598f54fed7b9", "80a72037771e4da9be989eefabbc8e76", "ba68b274c50b44ec9e02642378d271a6", "f84d2fe4f1c24a34948755abf1f32b7f", "86511967834f4484a5ec4af387b7d7a9", "c97c40c2bf2a41a8ae1c75e0a9c8ebff", "484e507f14424f2b9173595b985f4101", "68a32b398e1c490393e01befdc260785", "04cc963133d242779572d2e847fa3d65", "94730f13e92a4c9aac35c2cfb21fc48c", "620c0de28ec74f71a021a2be96dccf3a", "6e1aff64771c402ab070f650562fa4c9", "0078f897f2174217a307d95d4f9bd775", "ecdeaab4f8c94d6dade63bb06857c969", "735b85f0a0e9411cac4d704a504fcfc1", "8e992e60416145a8b6eed744287ca0fb", "8c195b5809604905b5e404baa30e8449", "2247efac4283489bbd228330344388ab", "e93d063faf984cc4aa51462418d9b57e", "9d45b9a5de3e4cba9ac35ad2cb187f51", "e99423a1ed3f4f72886b39368468b7c1", "5f174718e5974a7cab024d113f662513" ] }, "id": "LjY75GoYUCB8", "outputId": "80d6c3b9-28c2-4ebf-9c57-6a0b77ce82b1" }, "outputs": [], "source": [ "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", "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n", "def formatting_prompts_func(examples):\n", " instructions = examples[\"instruction\"]\n", " inputs = examples[\"input\"]\n", " outputs = examples[\"output\"]\n", " texts = []\n", " for instruction, input, output in zip(instructions, inputs, outputs):\n", " # Must add EOS_TOKEN, otherwise your generation will go on forever!\n", " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", " texts.append(text)\n", " return { \"text\" : texts, }\n", "pass\n", "\n", "from datasets import load_dataset\n", "dataset = load_dataset(path = \"/home/alexander/GitHub/finetune\", data_files=\"trainingsdata.jsonl\", split=\"train\")\n", "dataset = dataset.map(formatting_prompts_func, batched = True)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "idAEIeSQ3xdS" }, "source": [ "\n", "### Train the model\n", "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "3719bf6f9c6a4c6fbef93c5328c11a07", "03f492b4b56f4d8e80e9395a65058b1b", "39d9ef9fb35f47119f319f48eb222070", "3d7cfb33ceaf417e851ac4393c65148b", "ece66fa2f128456fa2a82b8a28d1211c", "9695a640b0ff4e91af495bb59548e4b6", "d4bd5559d4134d64a943d57972c6ef39", "fbae6e599d1644f39e5d86efa0f9f997", "00d425bca350451da6400f9f05c4a659", "6a27d9ad4f064586a87636b10455d15b", "77f4367616964a01a8c42416f5f4c147" ] }, "id": "95_Nn-89DhsL", "outputId": "29798478-b975-42d3-b32b-020a805cac35" }, "outputs": [], "source": [ "from trl import SFTTrainer\n", "from transformers import TrainingArguments\n", "from unsloth import is_bfloat16_supported\n", "\n", "\n", "trainer = SFTTrainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", " train_dataset = dataset,\n", " dataset_text_field = \"text\",\n", " max_seq_length = max_seq_length,\n", " dataset_num_proc = 2,\n", " packing = False, # Can make training 5x faster for short sequences.\n", " args = TrainingArguments(\n", " per_device_train_batch_size = 2,\n", " gradient_accumulation_steps = 4,\n", " warmup_steps = 5,\n", " # num_train_epochs = 1, # Set this for 1 full training run.\n", " max_steps = 120,\n", " learning_rate = 2e-4,\n", " fp16 = not is_bfloat16_supported(),\n", " bf16 = is_bfloat16_supported(),\n", " logging_steps = 1,\n", " optim = \"adamw_8bit\",\n", " weight_decay = 0.01,\n", " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", " report_to = \"none\", # Use this for WandB etc\n", " ),\n", ")\n", "t_data = trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "ekOmTR1hSNcr" }, "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "CrSvZObor0lY" }, "source": [ " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "b13f5e53-4ca4-4551-dffa-aaa3c514dca4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<|begin_of_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", "Was muss ich beachten, wenn ich ein medium im Urlaub verloren habe\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "Bitte melden Sie den Verlust unverzüglich, damit die Bibliothek Maßnahmen ergreifen kann.<|end_of_text|>\n" ] } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"Was muss ich beachten, wenn ich ein medium im Urlaub verloren habe\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", "\n", "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "upcOlWe7A1vc", "outputId": "030a6e13-9371-4717-c5c5-d4e3563e0cca" }, "outputs": [], "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "tokenizer.save_pretrained(\"lora_model\")\n", "#model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", "#tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" ] }, { "cell_type": "markdown", "metadata": { "id": "AEEcJ4qfC7Lp" }, "source": [ "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MKX_XKs_BNZR", "outputId": "f8e7d3fe-8e4d-49ee-944f-08e70cdc1d87" }, "outputs": [], "source": [ "if True:\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", " \"Wie hoch ist die maximale Säumnisgebühr?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ] }, { "cell_type": "markdown", "metadata": { "id": "QQMjaNrjsU5_" }, "source": [ "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yFfaXG0WsQuE" }, "outputs": [], "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\")" ] }, { "cell_type": "markdown", "metadata": { "id": "f422JgM9sdVT" }, "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iHjt_SMYsd3P" }, "outputs": [], "source": [ "# Merge to 16bit\n", "if True: model.save_pretrained_merged(\"model_16\", 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 True: 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 True:\n", " model.save_pretrained(\"model\")\n", " tokenizer.save_pretrained(\"model\")\n", "if False:\n", " model.push_to_hub(\"hf/model\", token = \"\")\n", " tokenizer.push_to_hub(\"hf/model\", token = \"\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "TCv4vXHd61i7" }, "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.\n", "\n", "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Move the model to CPU to unload GPU memory\n", "# model.to(\"cpu\")\n", "\n", "# Save the model in GGUF format\n", "model.save_pretrained_gguf(\"finetunedmodel_gguf\", tokenizer)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(tokenizer._ollama_modelfile)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FqfebeAdT073" }, "outputs": [], "source": [ "# # Save to 8bit Q8_0\n", "if True: model.save_pretrained_gguf(\"finetunedmodel\", tokenizer,)\n", "# # Remember to go to https://huggingface.co/settings/tokens for a token!\n", "# # And change hf to your username!\n", "# if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", "\n", "# # Save to 16bit GGUF\n", "# if True: model.save_pretrained_gguf(\"finetunedmodel_f16\", 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 True: model.save_pretrained_gguf(\"finetuned_q4\", tokenizer, quantization_method = \"q4_k_m\")\n", "# if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", "\n", "# Save to multiple GGUF options - much faster if you want multiple!\n", "# if True:\n", "# model.save_pretrained_gguf(\n", "# \"finetuned_multiple\", # Change hf to your username!\n", "# tokenizer,\n", "# quantization_method = [\"q4_k_m\",],\n", "# )" ] }, { "cell_type": "markdown", "metadata": { "id": "3DRvrmz051-X" }, "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 Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n", "\n", "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) 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. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n", "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", "\n", "
\n", " \n", " \n", " \n", "\n", " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", "
\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [ { "file_id": "https://github.com/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb", "timestamp": 1750013020648 } ] }, "kernelspec": { "display_name": "finetune", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.18" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0078f897f2174217a307d95d4f9bd775": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": 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