4166 lines
130 KiB
Plaintext
4166 lines
130 KiB
Plaintext
{
|
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"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",
|
||
"<div class=\"align-center\">\n",
|
||
"<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
|
||
"<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
|
||
"<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
|
||
"</div>\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"
|
||
]
|
||
},
|
||
{
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||
"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": {
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||
"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 300,
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]
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},
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"id": "QmUBVEnvCDJv",
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||
"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": [
|
||
"<a name=\"Data\"></a>\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,
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"referenced_widgets": [
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]
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},
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||
"id": "LjY75GoYUCB8",
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||
"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": [
|
||
"<a name=\"Train\"></a>\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": [
|
||
"<a name=\"Inference\"></a>\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": [
|
||
"<a name=\"Save\"></a>\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",
|
||
"<div class=\"align-center\">\n",
|
||
" <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
|
||
" <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
|
||
" <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
|
||
"\n",
|
||
" Join Discord if you need help + ⭐️ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n",
|
||
"</div>\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": "HTMLView",
|
||
"description": "",
|
||
"description_tooltip": null,
|
||
"layout": "IPY_MODEL_8c195b5809604905b5e404baa30e8449",
|
||
"placeholder": "",
|
||
"style": "IPY_MODEL_2247efac4283489bbd228330344388ab",
|
||
"value": "Map: 100%"
|
||
}
|
||
},
|
||
"00d425bca350451da6400f9f05c4a659": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "1.5.0",
|
||
"model_name": "ProgressStyleModel",
|
||
"state": {
|
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