{
"cells": [
{
"cell_type": "markdown",
"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://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
" <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
" <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join Discord if you need help + support us if you can!\n",
"</div>\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": "8LKIA_qnVKOz"
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},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2eSvM9zX_2d3"
},
"outputs": [],
"source": [
"%%capture\n",
"# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
"!pip install unsloth\n",
"# Get latest Unsloth\n",
"!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\""
]
},
{
"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"
}
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"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",
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"text": [
"==((====))== Unsloth: Fast Llama patching release 2024.1\n",
" \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB\n",
"O^O/ \\_/ \\ CUDA capability = 7.5. Xformers = 0.0.22.post7. FA = False.\n",
"\\ / Pytorch version: 2.1.0+cu121. CUDA Toolkit = 12.1\n",
" \"-____-\" bfloat16 = FALSE. Platform = Linux\n",
"\n",
"You passed `quantization_config` to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` attribute will be overwritten with the one you passed to `from_pretrained`.\n"
]
},
{
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],
"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/mistral-7b-bnb-4bit\",\n",
" \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n",
" \"unsloth/llama-2-7b-bnb-4bit\",\n",
" \"unsloth/llama-2-13b-bnb-4bit\",\n",
" \"unsloth/codellama-34b-bnb-4bit\",\n",
" \"unsloth/tinyllama-bnb-4bit\",\n",
" \"unsloth/gemma-7b-bnb-4bit\", # New Google 6 trillion tokens model 2.5x faster!\n",
" \"unsloth/gemma-2b-bnb-4bit\",\n",
"] # More models at https://huggingface.co/unsloth\n",
"\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"unsloth/llama-2-7b-bnb-4bit\", # Choose ANY! eg mistralai/Mistral-7B-Instruct-v0.2\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",
"source": [
"We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
],
"metadata": {
"id": "SXd9bTZd1aaL"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6bZsfBuZDeCL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "aff41a1e-2a0c-4092-d0d8-9438e7057212"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Unsloth 2024.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
]
}
],
"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",
" 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": [
"<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 `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)."
],
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{
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"Downloading readme: 0%| | 0.00/11.6k [00:00<?, ?B/s]"
],
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"Generating train split: 0 examples [00:00, ? examples/s]"
],
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"data": {
"text/plain": [
"Map: 0%| | 0/51760 [00:00<?, ? examples/s]"
],
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"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\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(\"yahma/alpaca-cleaned\", split = \"train\")\n",
"dataset = dataset.map(formatting_prompts_func, batched = True,)"
]
},
{
"cell_type": "markdown",
"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`!"
],
"metadata": {
"id": "idAEIeSQ3xdS"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "95_Nn-89DhsL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 177,
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},
"outputId": "aa0306a5-78f3-4360-b144-3e2478a5ad99"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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},
{
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{
"output_type": "display_data",
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{
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{
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"version_minor": 0,
"model_id": "f9c467b27a574fe8a949c87859e56da4"
}
},
"metadata": {}
}
],
"source": [
"from trl import SFTTrainer\n",
"from transformers import TrainingArguments\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",
" max_steps = 60,\n",
" learning_rate = 2e-4,\n",
" fp16 = not torch.cuda.is_bf16_supported(),\n",
" bf16 = torch.cuda.is_bf16_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",
" ),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2ejIt2xSNKKp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"outputId": "bf02bbcb-39b8-4a33-f76b-6759216cc551"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPU = Tesla T4. Max memory = 14.748 GB.\n",
"4.121 GB of memory reserved.\n"
]
}
],
"source": [
"#@title Show current memory stats\n",
"gpu_stats = torch.cuda.get_device_properties(0)\n",
"start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
"max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
"print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
"print(f\"{start_gpu_memory} GB of memory reserved.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yqxqAZ7KJ4oL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "df526f14-2611-40eb-e3bc-e3c4220fad5f"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Unsloth: `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [60/60 07:44, Epoch 0/1]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1.536900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.858000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.344800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1.578000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1.424600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>1.540000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.350500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.256200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>1.193000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>1.158800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>0.986400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>1.017500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>0.885500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>1.055400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.925300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.963100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>1.063800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>0.806200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>0.667100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.788600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.903000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>0.973000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.793800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>0.787200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>0.918000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>0.741500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>0.913300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.852900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.865900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.757000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>0.803500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>0.790600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>0.848800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>0.870000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>0.773300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>0.801000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>0.827100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>0.820800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>0.618800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>0.935300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>0.797500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>0.631100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>0.710600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>0.695100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>0.665200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>0.686600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>0.785200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>0.801100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>0.767500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>0.982100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>0.833100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>0.760700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>0.871500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>0.962400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>0.818900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>0.984800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>0.804300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>0.915100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>0.851200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>0.904900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
}
],
"source": [
"trainer_stats = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pCqnaKmlO1U9",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3cecbbbd-a23b-45f6-948f-424edb37bdd4"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"476.7837 seconds used for training.\n",
"7.95 minutes used for training.\n",
"Peak reserved memory = 5.77 GB.\n",
"Peak reserved memory for training = 1.649 GB.\n",
"Peak reserved memory % of max memory = 39.124 %.\n",
"Peak reserved memory for training % of max memory = 11.181 %.\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": [
"<a name=\"Inference\"></a>\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": "684a01f7-3545-4078-a361-64800718ba37"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['<s> 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:\\n2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2']"
]
},
"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": "_zsWEKC7t6Tt"
}
},
{
"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": "-C5dS6ZZt6ra",
"outputId": "d5fb7b7a-e2ba-4174-be00-62c86026c116"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<s> 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",
"1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121429, 1\n"
]
}
]
},
{
"cell_type": "markdown",
"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!"
],
"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": "L7z5EkMXuC8c"
}
},
{
"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",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 64)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_tbHr99fuAuL",
"outputId": "5c8468f6-7c2c-4733-d477-305d0df35c57"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[\"<s> 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. It is located on the Champ de Mars in the 7th arrondissement of Paris. It was built in 1889 as the main entrance to the 1889 World's Fair.</s>\"]"
]
},
"metadata": {},
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]
},
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"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": "TGKU509CuMmq"
}
},
{
"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": "I1blTAruuM7X"
},
"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."
],
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"id": "-xp0YDnKuN98"
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"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": "vnFt-4ymuPM1"
},
"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."
],
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"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": "8T822D9fuR0g"
},
"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": "RiRcv_rquUq0"
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},
{
"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. Mistral 7b 2x faster [free Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?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",
"<div class=\"align-center\">\n",
" <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
" <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
" <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Support our work if you can! Thanks!\n",
"</div>"
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