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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/sAndreotti/MedicalMeadow/blob/main/Llama_3_2_1B%2B3B_Conversational_%2B_2x_faster_finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IqM-T1RTzY6C"
},
"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 + ⭐ <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://github.com/unslothai/unsloth?tab=readme-ov-file#-installation-instructions).\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).\n",
"\n",
"**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**\n",
"\n",
"Features in the notebook:\n",
"1. Uses Maxime Labonne's [FineTome 100K](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset.\n",
"1. Convert ShareGPT to HuggingFace format via `standardize_sharegpt`\n",
"2. Train on Completions / Assistant only via `train_on_responses_only`\n",
"3. Unsloth now supports Torch 2.4, all TRL & Xformers versions & Python 3.12!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "2eSvM9zX_2d3"
},
"outputs": [],
"source": [
"%%capture\n",
"!pip install unsloth\n",
"# Also get the latest nightly Unsloth!\n",
"!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r2v_X2fA0Df5"
},
"source": [
"* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n",
"* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
"* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
"* [**NEW**] We make Gemma-2 9b / 27b **2x faster**! See our [Gemma-2 9b notebook](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)\n",
"* [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)"
]
},
{
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"text": [
"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
"🦥 Unsloth Zoo will now patch everything to make training faster!\n",
"==((====))== Unsloth 2024.12.12: Fast Llama patching. Transformers: 4.47.1.\n",
" \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform: Linux.\n",
"O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 7.5. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
"\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29. FA2 = False]\n",
" \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n",
"Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
]
},
{
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"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/2.24G [00:00<?, ?B/s]"
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"version_major": 2,
"version_minor": 0,
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"generation_config.json: 0%| | 0.00/184 [00:00<?, ?B/s]"
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"tokenizer_config.json: 0%| | 0.00/54.6k [00:00<?, ?B/s]"
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"version_minor": 0,
"model_id": "45d7468b23d14ea99d103689c891749f"
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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/Meta-Llama-3.1-8B-bnb-4bit\", # Llama-3.1 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\", # 4bit for 405b!\n",
" \"unsloth/Mistral-Small-Instruct-2409\", # Mistral 22b 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",
"\n",
" \"unsloth/Llama-3.2-1B-bnb-4bit\", # NEW! Llama 3.2 models\n",
" \"unsloth/Llama-3.2-1B-Instruct-bnb-4bit\",\n",
" \"unsloth/Llama-3.2-3B-bnb-4bit\",\n",
" \"unsloth/Llama-3.2-3B-Instruct-bnb-4bit\",\n",
"\n",
" \"unsloth/Llama-3.3-70B-Instruct-bnb-4bit\" # NEW! Llama 3.3 70B!\n",
"] # More models at https://huggingface.co/unsloth\n",
"\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"unsloth/Llama-3.2-3B-Instruct\", # or choose \"unsloth/Llama-3.2-1B-Instruct\"\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": 3,
"metadata": {
"id": "6bZsfBuZDeCL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "34f0a776-1747-4b3a-989b-aff287497f2e"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Unsloth 2024.12.12 patched 28 layers with 28 QKV layers, 28 O layers and 28 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",
" # [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": "code",
"source": [
"from datasets import load_dataset\n",
"from unsloth.chat_templates import get_chat_template\n",
"\n",
"# Load the dataset\n",
"dataset = load_dataset(\"medalpaca/medical_meadow_medical_flashcards\", split=\"train\")\n",
"\n",
"def convert_to_conversations(examples):\n",
" \"\"\"Convert the dataset format to conversations\"\"\"\n",
" conversations = []\n",
" for instruction, input_text, output in zip(\n",
" examples['instruction'],\n",
" examples['input'],\n",
" examples['output']\n",
" ):\n",
" # Combine instruction and input\n",
" user_content = f\"{instruction}: {input_text}\"\n",
" conv = [\n",
" {\"role\": \"user\", \"content\": user_content},\n",
" {\"role\": \"assistant\", \"content\": output}\n",
" ]\n",
" conversations.append(conv)\n",
" return {\"conversations\": conversations}\n",
"\n",
"# Convert to conversation format\n",
"formatted_dataset = dataset.map(\n",
" convert_to_conversations,\n",
" batched=True,\n",
" remove_columns=dataset.column_names\n",
")\n",
"\n",
"# Set up the tokenizer with Llama-3.1 chat template\n",
"tokenizer = get_chat_template(\n",
" tokenizer,\n",
" chat_template=\"llama-3.1\",\n",
")\n",
"\n",
"def formatting_prompts_func(examples):\n",
" \"\"\"Apply the chat template to the conversations\"\"\"\n",
" convos = examples[\"conversations\"]\n",
" texts = [\n",
" tokenizer.apply_chat_template(\n",
" convo,\n",
" tokenize=False,\n",
" add_generation_prompt=False\n",
" ) for convo in convos\n",
" ]\n",
" return {\"text\": texts}\n",
"\n",
"# Apply the formatting\n",
"formatted_dataset = formatted_dataset.map(\n",
" formatting_prompts_func,\n",
" batched=True\n",
")\n",
"\n",
"# Save the formatted dataset to make it available in next cells\n",
"import pickle\n",
"with open('formatted_dataset.pkl', 'wb') as f:\n",
" pickle.dump(formatted_dataset, f)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
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{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/33955 [00:00<?, ? examples/s]"
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"cell_type": "code",
"source": [
"# Print an example to verify the format\n",
"print(\"Example of formatted conversation:\")\n",
"print(formatted_dataset[0]['text'])\n",
"\n",
"# The dataset is now ready for training\n",
"# You can access it as formatted_dataset['text']\n",
"\n",
"# If you need to split it into train/validation sets:\n",
"train_val = formatted_dataset.train_test_split(test_size=0.1, seed=42)\n",
"train_data = train_val['train']\n",
"val_data = train_val['test']\n",
"\n",
"print(\"\\nDataset sizes:\")\n",
"print(f\"Train: {len(train_data)}\")\n",
"print(f\"Validation: {len(val_data)}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YPZSwk6319Dw",
"outputId": "f5deb7bb-1e8a-470e-ca58-49a05cb98ab8"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Example of formatted conversation:\n",
"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
"\n",
"Cutting Knowledge Date: December 2023\n",
"Today Date: 26 July 2024\n",
"\n",
"<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
"\n",
"Answer this question truthfully: What is the relationship between very low Mg2+ levels, PTH levels, and Ca2+ levels?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
"\n",
"Very low Mg2+ levels correspond to low PTH levels which in turn results in low Ca2+ levels.<|eot_id|>\n",
"\n",
"Dataset sizes:\n",
"Train: 30559\n",
"Validation: 3396\n"
]
}
]
},
{
"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": 12,
"metadata": {
"id": "95_Nn-89DhsL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
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{
"output_type": "display_data",
"data": {
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"Map (num_proc=2): 0%| | 0/33955 [00:00<?, ? examples/s]"
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],
"source": [
"from trl import SFTTrainer\n",
"from transformers import TrainingArguments, DataCollatorForSeq2Seq\n",
"from unsloth import is_bfloat16_supported\n",
"\n",
"trainer = SFTTrainer(\n",
" model = model,\n",
" tokenizer = tokenizer,\n",
" train_dataset = formatted_dataset,\n",
" dataset_text_field = \"text\",\n",
" max_seq_length = max_seq_length,\n",
" data_collator = DataCollatorForSeq2Seq(tokenizer = tokenizer),\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 = 60,\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",
")"
]
},
{
"cell_type": "markdown",
"source": [
"We also use Unsloth's `train_on_completions` method to only train on the assistant outputs and ignore the loss on the user's inputs."
],
"metadata": {
"id": "C_sGp5XlG6dq"
}
},
{
"cell_type": "code",
"source": [
"from unsloth.chat_templates import train_on_responses_only\n",
"trainer = train_on_responses_only(\n",
" trainer,\n",
" instruction_part = \"<|start_header_id|>user<|end_header_id|>\\n\\n\",\n",
" response_part = \"<|start_header_id|>assistant<|end_header_id|>\\n\\n\",\n",
")"
],
"metadata": {
"id": "juQiExuBG5Bt",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"3be9f1c4ca424b08b995986b31bc6f73",
"cbaaf16637334b8c907be3c1eea8e36b",
"ab35ff19da2b4ab49ea4f6ab3bc32981",
"d9add78673484904995f4b3181c2d3d6",
"d44d61caea1f4aa79d3fae256b5e351e",
"4be9d0609a1d4a28b622ada5052fd653",
"fbf5b0b982d5483bb160f1c745e5fa1d",
"55be8a889c8a414f83c2af904513f96d",
"567af20e2fab400aae2131ade113d39d",
"a17fe71fa4f24581a5a5758d367888c3",
"106ddc5392a747e88905c314032671fa"
]
},
"outputId": "69af3385-fc25-41d3-8207-5fa2e9091867"
},
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/33955 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3be9f1c4ca424b08b995986b31bc6f73"
}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"We verify masking is actually done:"
],
"metadata": {
"id": "Dv1NBUozV78l"
}
},
{
"cell_type": "code",
"source": [
"tokenizer.decode(trainer.train_dataset[5][\"input_ids\"])"
],
"metadata": {
"id": "LtsMVtlkUhja",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 55
},
"outputId": "f1c670d5-ed1b-4b00-df24-4ad6fc0909b0"
},
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nAnswer this question truthfully: What does low Mobility and bulging of TM suggest?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nLow Mobility and bulging of TM is suggestive of Acute otitis media.<|eot_id|>'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"space = tokenizer(\" \", add_special_tokens = False).input_ids[0]\n",
"tokenizer.decode([space if x == -100 else x for x in trainer.train_dataset[5][\"labels\"]])"
],
"metadata": {
"id": "_rD6fl8EUxnG",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"outputId": "05599f09-3443-4fc1-82c1-4fc35958ed7f"
},
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"' \\n\\nLow Mobility and bulging of TM is suggestive of Acute otitis media.<|eot_id|>'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"source": [
"[link text](https://)We can see the System and Instruction prompts are successfully masked!"
],
"metadata": {
"id": "3enWUM0jV-jV"
}
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "2ejIt2xSNKKp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "39fbd3c6-7979-484d-bf73-6bacf4dc8189"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPU = Tesla T4. Max memory = 14.748 GB.\n",
"3.324 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": 17,
"metadata": {
"id": "yqxqAZ7KJ4oL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "3790f36d-d523-48c4-d930-718fb512a1a7"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
" \\\\ /| Num examples = 33,955 | Num Epochs = 1\n",
"O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n",
"\\ / Total batch size = 8 | Total steps = 60\n",
" \"-____-\" Number of trainable parameters = 24,313,856\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 02:49, 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>1.291800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.210600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.020600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.976700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>0.968400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>1.065500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.091000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>0.828600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>0.804600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>0.832100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>0.695600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>0.702200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>0.747300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>0.753600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.803100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.856100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>0.883200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>0.863200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>0.730900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.716000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.851000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>0.849400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.736900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>0.807300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>0.845800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>0.920700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>0.739800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.816900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.783700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.701500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>0.734100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>0.804600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>0.882300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>0.776600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>0.731700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>1.109600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>0.821800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>0.616000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>0.712900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>0.735600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>0.902300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>0.832600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>0.791600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>0.813500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>0.876400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>0.814500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>0.773300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>0.739800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>0.830400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>0.934300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>0.864700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>1.015700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>0.777400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>0.705100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>0.844800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>0.861400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>0.848500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>0.768700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>0.684100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>0.956200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
}
],
"source": [
"trainer_stats = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "pCqnaKmlO1U9",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "df7922d2-20c8-4c99-99dd-10eff2caf2e6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"178.5531 seconds used for training.\n",
"2.98 minutes used for training.\n",
"Peak reserved memory = 3.324 GB.\n",
"Peak reserved memory for training = 0.0 GB.\n",
"Peak reserved memory % of max memory = 22.539 %.\n",
"Peak reserved memory for training % of max memory = 0.0 %.\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",
"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",
"**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**\n",
"\n",
"We use `min_p = 0.1` and `temperature = 1.5`. Read this [Tweet](https://x.com/menhguin/status/1826132708508213629) for more information on why."
]
},
{
"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",
"source": [
"from unsloth.chat_templates import get_chat_template\n",
"from transformers import TextStreamer\n",
"\n",
"# Setup tokenizer with Llama-3.1 template\n",
"tokenizer = get_chat_template(\n",
" tokenizer,\n",
" chat_template = \"llama-3.1\",\n",
")\n",
"\n",
"# Enable faster inference\n",
"FastLanguageModel.for_inference(model)\n",
"\n",
"# Example medical question from our dataset\n",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Answer this question truthfully: What is the relationship between very low Mg2+ levels, PTH levels, and Ca2+ levels?\"},\n",
"]\n",
"\n",
"# Standard generation\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" tokenize = True,\n",
" add_generation_prompt = True,\n",
" return_tensors = \"pt\",\n",
").to(\"cuda\")\n",
"\n",
"print(\"Standard Generation:\")\n",
"outputs = model.generate(\n",
" input_ids = inputs,\n",
" max_new_tokens = 128,\n",
" use_cache = True,\n",
" temperature = 0.7, # Lower temperature for medical answers to be more precise\n",
" min_p = 0.1\n",
")\n",
"print(tokenizer.batch_decode(outputs))\n",
"\n",
"# Streaming generation\n",
"print(\"\\nStreaming Generation:\")\n",
"text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" tokenize = True,\n",
" add_generation_prompt = True,\n",
" return_tensors = \"pt\",\n",
").to(\"cuda\")\n",
"\n",
"_ = model.generate(\n",
" input_ids = inputs,\n",
" streamer = text_streamer,\n",
" max_new_tokens = 128,\n",
" use_cache = True,\n",
" temperature = 0.7,\n",
" min_p = 0.1\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qsrXkJwN3jvL",
"outputId": "4492cea0-4064-4be7-d623-b68bee1101ce"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Standard Generation:\n",
"['<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nAnswer this question truthfully: What is the relationship between very low Mg2+ levels, PTH levels, and Ca2+ levels?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nVery low Mg2+ levels, PTH levels, and Ca2+ levels are related.<|eot_id|>']\n",
"\n",
"Streaming Generation:\n",
"Very low Mg2+ levels are associated with very high PTH levels and very low Ca2+ levels.<|eot_id|>\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# New medical question\n",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Answer this question truthfully: Who is roberto baggio?\"},\n",
"]\n",
"\n",
"# Standard generation\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" tokenize = True,\n",
" add_generation_prompt = True,\n",
" return_tensors = \"pt\",\n",
").to(\"cuda\")\n",
"\n",
"print(\"Standard Generation:\")\n",
"outputs = model.generate(\n",
" input_ids = inputs,\n",
" max_new_tokens = 128,\n",
" use_cache = True,\n",
" temperature = 0.7,\n",
" min_p = 0.1\n",
")\n",
"print(tokenizer.batch_decode(outputs))\n",
"\n",
"# Streaming generation\n",
"print(\"\\nStreaming Generation:\")\n",
"text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" tokenize = True,\n",
" add_generation_prompt = True,\n",
" return_tensors = \"pt\",\n",
").to(\"cuda\")\n",
"\n",
"_ = model.generate(\n",
" input_ids = inputs,\n",
" streamer = text_streamer,\n",
" max_new_tokens = 128,\n",
" use_cache = True,\n",
" temperature = 0.7,\n",
" min_p = 0.1\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fcRMYYe74ovE",
"outputId": "aba9c862-5ab7-418e-f875-b781cf9e8b0c"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Standard Generation:\n",
"['<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nAnswer this question truthfully: Who is roberto baggio?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nRoberto Baggio is a former Italian professional footballer who played as a midfielder for the Italy national team. He is widely regarded as one of the greatest players of his generation and is known for his exceptional dribbling skills and vision on the pitch. Baggio played for several top-tier clubs, including Juventus, AC Milan, and Inter Milan, and won numerous honors, including the FIFA World Cup Golden Ball award. He is also a legendary figure in Italian football, known for his iconic goal against Brazil in the 1994 World Cup. Baggio is now a coach and a commentator for Italian television, and is still widely respected and admired']\n",
"\n",
"Streaming Generation:\n",
"Roberto Baggio is an Italian former professional footballer and coach. He is a former Italy international and is considered one of the greatest players of his generation. Baggio is best known for his time with Juventus and AC Milan, where he won numerous titles and individual awards. He is also known for his iconic goals, including the famous \"Di Stéfano\" goal in the 1996 World Cup.<|eot_id|>\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Test with a novel medical question\n",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Answer this question truthfully: What are the potential risks and benefits of using ACE inhibitors in a patient with diabetic nephropathy who also has bilateral renal artery stenosis?\"},\n",
"]\n",
"\n",
"# Standard generation\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" tokenize = True,\n",
" add_generation_prompt = True,\n",
" return_tensors = \"pt\",\n",
").to(\"cuda\")\n",
"\n",
"print(\"Standard Generation:\")\n",
"outputs = model.generate(\n",
" input_ids = inputs,\n",
" max_new_tokens = 2048, # Increased for more detailed response\n",
" use_cache = True,\n",
" temperature = 0.7,\n",
" min_p = 0.1\n",
")\n",
"print(tokenizer.batch_decode(outputs))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rfXL8wK4544g",
"outputId": "93cfcb0c-bf4a-4042-be88-bd1844f74092"
},
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Standard Generation:\n",
"[\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nAnswer this question truthfully: What are the potential risks and benefits of using ACE inhibitors in a patient with diabetic nephropathy who also has bilateral renal artery stenosis?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nACE inhibitors are a type of medication that can be used to treat high blood pressure and other conditions. In a patient with diabetic nephropathy who also has bilateral renal artery stenosis, ACE inhibitors can have both potential risks and benefits. The benefits of ACE inhibitors in this patient include the potential to improve blood pressure control and slow the progression of diabetic nephropathy. However, the risks of ACE inhibitors in this patient include the potential for worsening renal function and increased risk of kidney failure. It is important to carefully monitor the patient's renal function and blood pressure while using ACE inhibitors in this patient.<|eot_id|>\"]\n"
]
}
]
},
{
"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": {
"id": "upcOlWe7A1vc",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c962e43a-2027-4a39-ea03-870b707a22d4"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('lora_model/tokenizer_config.json',\n",
" 'lora_model/special_tokens_map.json',\n",
" 'lora_model/tokenizer.json')"
]
},
"metadata": {},
"execution_count": 17
}
],
"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": {
"id": "MKX_XKs_BNZR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f22dbd75-ea37-48bb-9f75-4178aebe9353"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The Eiffel Tower, located in the heart of Paris, stands tall among the city's historic and cultural landmarks. This iron structure, standing at an impressive 324 meters high, offers breathtaking views of the City of Light's iconic landscape. The Eiffel Tower was built for the 1889 World's Fair and has since become a symbol of French engineering and culture.<|eot_id|>\n"
]
}
],
"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",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Describe a tall tower in the capital of France.\"},\n",
"]\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" tokenize = True,\n",
" add_generation_prompt = True, # Must add for generation\n",
" return_tensors = \"pt\",\n",
").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
"_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,\n",
" use_cache = True, temperature = 1.5, min_p = 0.1)"
]
},
{
"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 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 = \"\")"
]
},
{
"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/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FqfebeAdT073"
},
"outputs": [],
"source": [
"# Save to 8bit Q8_0\n",
"if False: model.save_pretrained_gguf(\"model\", 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 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 = \"\")\n",
"\n",
"# Save to multiple GGUF options - much faster if you want multiple!\n",
"if False:\n",
" model.push_to_hub_gguf(\n",
" \"hf/model\", # Change hf to your username!\n",
" tokenizer,\n",
" quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n",
" token = \"\", # Get a token at https://huggingface.co/settings/tokens\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bDp0zNpwe6U_"
},
"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).\n",
"\n",
"**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zt9CHJqO6p30"
},
"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. [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)\n",
"10. [**NEW**] We make Gemma-2 9b / 27b **2x faster**! See our [Gemma-2 9b notebook](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)\n",
"11. [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)\n",
"12. [**NEW**] We make Mistral NeMo 12B 2x faster and fit in under 12GB of VRAM! [Mistral NeMo notebook](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?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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