diff --git a/.gitignore b/.gitignore index 437f417..3b19191 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ config.py # Unsloth _unsloth_sentencepiece_temp/ +unsloth_compiled_cache/ # ---> Python # Byte-compiled / optimized / DLL files diff --git a/data/booru/.gitignore b/data/booru/.gitignore new file mode 100644 index 0000000..c228bb2 --- /dev/null +++ b/data/booru/.gitignore @@ -0,0 +1,3 @@ +* +!README.md +!.gitignore diff --git a/data/booru/README.md b/data/booru/README.md new file mode 100644 index 0000000..844345a --- /dev/null +++ b/data/booru/README.md @@ -0,0 +1 @@ +Place booru images here, with filenames of the form "12345 - Tag1 Tag_2 Tag3.jpg" diff --git a/data/package-lock.json b/data/package-lock.json index 0e9f8e4..7edd450 100644 --- a/data/package-lock.json +++ b/data/package-lock.json @@ -1,5 +1,5 @@ { - "name": "discord", + "name": "data", "lockfileVersion": 3, "requires": true, "packages": { diff --git a/data/proc_booru.py b/data/proc_booru.py new file mode 100644 index 0000000..ef626a1 --- /dev/null +++ b/data/proc_booru.py @@ -0,0 +1,177 @@ +""" +proc_booru.py +This script assumes you have a folder called 'booru/' in the current directory, +containing a bunch of images following the Shimmie Booru naming scheme, i.e. +'12345 - Tag1 Tag2 Tag3.jpg'. +""" + +import json +import os +from pathlib import Path +from typing import List, Tuple +import re +from unsloth import FastVisionModel +import pandas as pd +import numpy as np +import PIL +import torch +import io +import tqdm + +# names of real-life people tagged in images +NAMES = set(["James", "Vincent", "Myles", "Sam", "Jake", "Nicolai", "David", "ren", "Nazar"]) +# irrelevant tags that should just be removed +IRRELEVANT = set(["_", "Myles'", "Vinny's", "Jake's", "tagme", "Nguyen"]) + +def parse_filename(filename: str) -> Tuple[str, List[str]]: + """ + Parse a filename of format '12345 - Tag1 Tag2 Tag3.jpg' into ID and tags. + Returns tuple of (id, [tags]) + """ + # Remove file extension + name = os.path.splitext(filename)[0] + + # Split into ID and tags + match = re.match(r'(\d+)\s*-\s*(.*)', name) + if not match: + raise ValueError(f"Invalid filename format: {filename}") + + image_id = match.group(1) + tags = match.group(2).strip().split() + + # remove irrelevant tags + irrelevant_overlap = IRRELEVANT.intersection(tags) + if len(irrelevant_overlap) > 0: + for tag in irrelevant_overlap: + tags.remove(tag) + + # remove ambiguous situations with people's names, since the model won't know what they look like + names_overlap = NAMES.intersection(tags) + if len(names_overlap) > 1: + for name in names_overlap: + tags.remove(name) + + return image_id, tags + +def create_prompt(tags: List[str]) -> str: + """ + Create a prompt for the LLM to generate a summary based on tags. + """ + tags_str = ', '.join(tags) + return [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "You are a helpful assistant. You must write a caption describing the following image, given a list of tags describing the image. Your response must contain absolutely nothing apart from a caption. Keep it as concise as you possibly can, at a hard maximum of two sentences. Avoid describing any small details, simply focus on the main subject of the image. Responses that are simply a repeat of the input are strictly forbidden. Your responses should be said with certainty.\n\nExample:\n```\nTags: 1991_Honda_Civic, Cisco_Parking_Lot, Grayscale, Milpitas, UnionPay\nThe image depicts a black and white photograph of a 1991 Honda Civic sedan parked in a Cisco parking lot in Milpitas, with a partial UnionPay advertisement visible.\n```\n\nExample:\n```\nTags: 2015_Honda_CB300F, Encinal_Canyon_Road, Malibu\nThe image features a 2015 Honda CB300F motorcycle parked on the side of Encinal Canyon Road in Malibu.\n```" + }, + {"type": "image"}, + { + "type": "text", + "text": f"Tags: {tags_str}" + }, + ] + } + ] + +def load_image_as_bytes(image_path: Path) -> bytes: + """ + Load an image file and return it as bytes. + """ + with PIL.Image.open(image_path) as img: + # Convert to RGB if necessary + if img.mode != 'RGB': + img = img.convert('RGB') + + # Save to bytes + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format='JPEG') + return img_byte_arr.getvalue() + +def main(): + model, tokenizer = FastVisionModel.from_pretrained( + "unsloth/Llama-3.2-11B-Vision-Instruct", + load_in_4bit = True, # Use 4bit to reduce memory use. False for 16bit LoRA. + use_gradient_checkpointing = "unsloth", # True or "unsloth" for long context + ) + FastVisionModel.for_inference(model) + + # Process all images in the booru directory + booru_dir = Path('booru') + if not booru_dir.exists(): + raise FileNotFoundError("booru directory not found") + + # Create lists to store data + data = [] + + # Get all image files + image_files = [f for f in os.listdir(booru_dir) if f.lower().endswith(('.jpg', '.jpeg', '.png'))] + + # Process each file + for filename in tqdm.tqdm(image_files): + try: + filepath = booru_dir / filename + + # Parse filename + image_id, tags = parse_filename(filename) + + # Create prompt + prompt = create_prompt(tags) + input_text = tokenizer.apply_chat_template(prompt, add_generation_prompt=True) + image = PIL.Image.open(filepath) + inputs = tokenizer( + image, + input_text, + add_special_tokens = False, + return_tensors = "pt", + ).to("cuda") + + # Generate summary using VLLM + outputs = model.generate(**inputs, max_new_tokens=128, + use_cache=True, temperature=1.5, min_p=0.1) + generated_text = tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):], skip_special_tokens=True) + generated_text = generated_text.partition('\n')[0] + + # Load image as bytes + image_bytes = load_image_as_bytes(filepath) + + data_dict = { + 'image_id': image_id, + 'filename': filename, + 'tags': tags, + 'tags_string': ' '.join(tags), + 'summary': generated_text, + } + + print(data_dict) + + data_dict['image_data'] = image_bytes + + # Store data + data.append(data_dict) + + # Print progress + print(f"Processed: {filename}") + + except ValueError as e: + print(f"Error processing {filename}: {e}") + except Exception as e: + print(f"Unexpected error processing {filename}: {e}") + + # Convert to DataFrame + df = pd.DataFrame(data) + + # Save to Parquet + output_path = 'image_summaries.parquet' + df.to_parquet(output_path, compression='snappy') + print(f"\nSaved dataset to {output_path}") + + # Print summary statistics + print(f"\nDataset Summary:") + print(f"Total images processed: {len(df)}") + print(f"Unique tags: {len(set(' '.join(df['tags_string']).split()))}") + print(f"Average summary length: {df['summary'].str.len().mean():.1f} characters") + +if __name__ == "__main__": + main() diff --git a/mikuai-vision-training-notebook.ipynb b/mikuai-vision-training-notebook.ipynb new file mode 100644 index 0000000..6fadf5b --- /dev/null +++ b/mikuai-vision-training-notebook.ipynb @@ -0,0 +1,7864 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "14ac6109", + "metadata": { + "id": "IqM-T1RTzY6C", + "papermill": { + "duration": 0.021668, + "end_time": "2025-01-01T01:36:49.042245", + "exception": false, + "start_time": "2025-01-01T01:36:49.020577", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", + "
\n", + " \n", + " \n", + " Join Discord if you need help + support us if you can!\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp)." + ] + }, + { + "cell_type": "markdown", + "id": "992529ab", + "metadata": { + "papermill": { + "duration": 0.017829, + "end_time": "2025-01-01T01:36:49.078747", + "exception": false, + "start_time": "2025-01-01T01:36:49.060918", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Kaggle is slow - you'll have to wait **5 minutes** for it to install.\n", + "\n", + "I suggest you to use our free Colab notebooks instead. I linked our Mistral Colab notebook here: [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5891519d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-01T01:36:49.115782Z", + "iopub.status.busy": "2025-01-01T01:36:49.115557Z", + "iopub.status.idle": "2025-01-01T01:39:57.034466Z", + "shell.execute_reply": "2025-01-01T01:39:57.033580Z" + }, + "papermill": { + "duration": 187.938919, + "end_time": "2025-01-01T01:39:57.036077", + "exception": false, + "start_time": "2025-01-01T01:36:49.097158", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting pip3-autoremove\r\n", + " Downloading pip3_autoremove-1.2.2-py2.py3-none-any.whl.metadata (2.2 kB)\r\n", + "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (from pip3-autoremove) (24.1.2)\r\n", + 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hf_transfer-0.1.8 huggingface_hub-0.27.0 peft-0.14.0 shtab-1.7.1 tokenizers-0.21.0 transformers-4.47.1 trl-0.13.0 tyro-0.9.5 unsloth-2024.12.12 unsloth_zoo-2024.12.7\r\n" + ] + } + ], + "source": [ + "!pip install pip3-autoremove\n", + "!pip-autoremove torch torchvision torchaudio -y\n", + "!pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121\n", + "!pip install unsloth" + ] + }, + { + "cell_type": "markdown", + "id": "82af8803", + "metadata": { + "id": "r2v_X2fA0Df5", + "papermill": { + "duration": 0.055944, + "end_time": "2025-01-01T01:39:57.151102", + "exception": false, + "start_time": "2025-01-01T01:39:57.095158", + "status": "completed" + }, + "tags": [] + }, + "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", + "* [**NEW**] 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." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "81081d4b", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-01T01:39:57.263299Z", + "iopub.status.busy": "2025-01-01T01:39:57.263043Z", + "iopub.status.idle": "2025-01-01T01:40:53.320412Z", + "shell.execute_reply": "2025-01-01T01:40:53.319479Z" + }, + "id": "QmUBVEnvCDJv", + "outputId": "5eff0d61-05b4-471c-eea2-c2e84a915109", + "papermill": { + "duration": 56.11539, + "end_time": "2025-01-01T01:40:53.322099", + "exception": false, + "start_time": "2025-01-01T01:39:57.206709", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "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 Mllama vision patching. Transformers: 4.47.1.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.741 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.post1. 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" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e50032cd57a9417892ea70e3fd0a9f5b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model.safetensors.index.json: 0%| | 0.00/375k [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", + " finetune_vision_layers = True, # False if not finetuning vision part\n", + " finetune_language_layers = True, # False if not finetuning language part\n", + " finetune_attention_modules = True, # False if not finetuning attention layers\n", + " finetune_mlp_modules = True, # False if not finetuning MLP layers\n", + " lora_alpha = 32, # Recommended alpha == r at least\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bbe9e44b", + "metadata": { + "id": "vITh0KVJ10qX", + "papermill": { + "duration": 0.05753, + "end_time": "2025-01-01T01:40:59.328848", + "exception": false, + "start_time": "2025-01-01T01:40:59.271318", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "\n", + "### Data Prep\n", + "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", + "\n", + "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", + "\n", + "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", + "\n", + "If you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n", + "\n", + "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3a74fde3", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-01T01:40:59.444214Z", + "iopub.status.busy": "2025-01-01T01:40:59.443934Z", + "iopub.status.idle": "2025-01-01T01:41:04.410001Z", + "shell.execute_reply": "2025-01-01T01:41:04.409044Z" + }, + "id": "LjY75GoYUCB8", + "outputId": "9f40f734-788c-4793-c1af-e9d003337612", + "papermill": { + "duration": 5.025641, + "end_time": "2025-01-01T01:41:04.411709", + "exception": false, + "start_time": "2025-01-01T01:40:59.386068", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0aca87e0f7d4459ea312cf1a7a3160a4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "image_summaries.parquet: 0%| | 0.00/264M [00:00\n", + "### Train the model\n", + "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "116e1ce1", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-01T01:41:04.644664Z", + "iopub.status.busy": "2025-01-01T01:41:04.644382Z", + "iopub.status.idle": "2025-01-01T01:41:06.493679Z", + "shell.execute_reply": "2025-01-01T01:41:06.493023Z" + }, + "id": "95_Nn-89DhsL", + "outputId": "4b809e6d-271f-446f-dec8-abe0d13259f8", + "papermill": { + "duration": 1.908619, + "end_time": "2025-01-01T01:41:06.495152", + "exception": false, + "start_time": "2025-01-01T01:41:04.586533", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from trl import SFTTrainer, SFTConfig\n", + "from unsloth.trainer import UnslothVisionDataCollator\n", + "from transformers import DataCollatorForSeq2Seq\n", + "from unsloth import is_bf16_supported\n", + "from transformers import AutoProcessor\n", + "\n", + "FastVisionModel.for_training(model) # Enable for training!\n", + "\n", + "processor = AutoProcessor.from_pretrained(\"unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit\")\n", + "\n", + "# unsloth-zoo collator doesn't support messages without images\n", + "def collate_fn(examples):\n", + " # Get the texts and images, and apply the chat template\n", + " texts = [processor.apply_chat_template(example[\"messages\"], tokenize=False) for example in examples]\n", + " # MUST be None or be populated with images; this is what unsloth is missing\n", + " images = None\n", + " for example in examples:\n", + " if \"messages\" in examples:\n", + " for msg in examples[\"messages\"]:\n", + " if isinstance(msg[\"content\"], list):\n", + " has_image = False\n", + " for mode in msg[\"content\"]:\n", + " if mode[\"type\"] == \"image\":\n", + " if images is None:\n", + " images = []\n", + " images.append(mode[\"image\"])\n", + " has_image = True\n", + " if not has_image and isinstance(images, list):\n", + " images.append(None)\n", + "\n", + " # Tokenize the texts and process the images\n", + " batch = processor(texts, images, return_tensors=\"pt\", padding=True)\n", + "\n", + " # The labels are the input_ids, and we mask the padding tokens in the loss computation\n", + " labels = batch[\"input_ids\"].clone()\n", + " labels[labels == processor.tokenizer.pad_token_id] = -100\n", + " batch[\"labels\"] = labels\n", + "\n", + " return batch\n", + "\n", + "trainer = SFTTrainer(\n", + " model = model,\n", + " tokenizer = tokenizer,\n", + " train_dataset = dataset,\n", + " #data_collator = collate_fn,\n", + " data_collator = UnslothVisionDataCollator(model, tokenizer),\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 = SFTConfig(\n", + " per_device_train_batch_size = 2,\n", + " gradient_accumulation_steps = 4,\n", + " warmup_steps = 5,\n", + " #max_steps = 30,\n", + " num_train_epochs = 1, # Set this instead of max_steps for full training runs\n", + " learning_rate = 2e-4,\n", + " fp16 = not is_bf16_supported(),\n", + " bf16 = 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", + " report_to = \"none\", # For Weights and Biases\n", + "\n", + " # You MUST put the below items for vision finetuning:\n", + " remove_unused_columns = False,\n", + " dataset_text_field = \"\",\n", + " dataset_kwargs = {\"skip_prepare_dataset\": True},\n", + " dataset_num_proc = 4,\n", + " max_seq_length = 2048,\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "962391b6", + "metadata": { + "cellView": "form", + "execution": { + "iopub.execute_input": "2025-01-01T01:41:06.614544Z", + "iopub.status.busy": "2025-01-01T01:41:06.614275Z", + "iopub.status.idle": "2025-01-01T01:41:06.619459Z", + "shell.execute_reply": "2025-01-01T01:41:06.618801Z" + }, + "id": "2ejIt2xSNKKp", + "outputId": "4815a050-0c0f-4a6a-9d93-b01c44eaea35", + "papermill": { + "duration": 0.06503, + "end_time": "2025-01-01T01:41:06.620624", + "exception": false, + "start_time": "2025-01-01T01:41:06.555594", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPU = Tesla T4. Max memory = 14.741 GB.\n", + "7.881 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": 7, + "id": "52bb19f4", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-01T01:41:06.736648Z", + "iopub.status.busy": "2025-01-01T01:41:06.736441Z", + "iopub.status.idle": "2025-01-01T01:55:19.558624Z", + "shell.execute_reply": "2025-01-01T01:55:19.557921Z" + }, + "id": "yqxqAZ7KJ4oL", + "outputId": "3cf26aac-6042-4458-c4a6-d8849efb6a95", + "papermill": { + "duration": 852.881724, + "end_time": "2025-01-01T01:55:19.560033", + "exception": false, + "start_time": "2025-01-01T01:41:06.678309", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", + " \\\\ /| Num examples = 183 | Num Epochs = 1\n", + "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n", + "\\ / Total batch size = 8 | Total steps = 23\n", + " \"-____-\" Number of trainable parameters = 134,348,800\n", + "🦥 Unsloth needs about 1-3 minutes to load everything - please wait!\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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231.119600

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "55b8762b", + "metadata": { + "cellView": "form", + "execution": { + "iopub.execute_input": "2025-01-01T01:55:19.678426Z", + "iopub.status.busy": "2025-01-01T01:55:19.677755Z", + "iopub.status.idle": "2025-01-01T01:55:19.684152Z", + "shell.execute_reply": "2025-01-01T01:55:19.683381Z" + }, + "id": "pCqnaKmlO1U9", + "outputId": "cf63d152-e152-468c-ba0d-938e0d2f71a0", + "papermill": { + "duration": 0.065945, + "end_time": "2025-01-01T01:55:19.685303", + "exception": false, + "start_time": "2025-01-01T01:55:19.619358", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "848.9055 seconds used for training.\n", + "14.15 minutes used for training.\n", + "Peak reserved memory = 11.381 GB.\n", + "Peak reserved memory for training = 3.5 GB.\n", + "Peak reserved memory % of max memory = 77.206 %.\n", + "Peak reserved memory for training % of max memory = 23.743 %.\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", + "id": "986aa13e", + "metadata": { + "id": "ekOmTR1hSNcr", + "papermill": { + "duration": 0.057392, + "end_time": "2025-01-01T01:55:19.838995", + "exception": false, + "start_time": "2025-01-01T01:55:19.781603", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "\n", + "### Inference\n", + "Let's run the model! You can change the instruction and input - leave the output blank!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "111e0b71", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-01T01:55:19.955392Z", + "iopub.status.busy": "2025-01-01T01:55:19.955102Z", + "iopub.status.idle": "2025-01-01T01:55:19.959163Z", + "shell.execute_reply": "2025-01-01T01:55:19.958475Z" + }, + "id": "kR3gIAX-SM2q", + "outputId": "5b71f982-38c0-44c8-a4e5-58cd20b5a585", + "papermill": { + "duration": 0.063692, + "end_time": "2025-01-01T01:55:19.960308", + "exception": false, + "start_time": "2025-01-01T01:55:19.896616", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if False:\n", + " # alpaca_prompt = Copied from above\n", + " FastVisionModel.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)" + ] + }, + { + "cell_type": "markdown", + "id": "04376d0a", + "metadata": { + "id": "CrSvZObor0lY", + "papermill": { + "duration": 0.057197, + "end_time": "2025-01-01T01:55:20.074913", + "exception": false, + "start_time": "2025-01-01T01:55:20.017716", + "status": "completed" + }, + "tags": [] + }, + "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": 10, + "id": "ddb50ced", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-01T01:55:20.191902Z", + "iopub.status.busy": "2025-01-01T01:55:20.191675Z", + "iopub.status.idle": "2025-01-01T01:55:20.195589Z", + "shell.execute_reply": "2025-01-01T01:55:20.194954Z" + }, + "id": "e2pEuRb1r2Vg", + "outputId": "084aab62-2122-436a-c0cb-8871986640eb", + "papermill": { + "duration": 0.062828, + "end_time": "2025-01-01T01:55:20.196639", + "exception": false, + "start_time": "2025-01-01T01:55:20.133811", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if False:\n", + " # alpaca_prompt = Copied from above\n", + " FastVisionModel.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)" + ] + }, + { + "cell_type": "markdown", + "id": "55dafc1e", + "metadata": { + "id": "uMuVrWbjAzhc", + "papermill": { + "duration": 0.057403, + "end_time": "2025-01-01T01:55:20.312023", + "exception": false, + "start_time": "2025-01-01T01:55:20.254620", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "aeb56f25", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-01T01:55:20.427410Z", + "iopub.status.busy": "2025-01-01T01:55:20.427199Z", + "iopub.status.idle": "2025-01-01T01:55:26.188793Z", + "shell.execute_reply": "2025-01-01T01:55:26.188001Z" + }, + "id": "upcOlWe7A1vc", + "papermill": { + "duration": 5.820743, + "end_time": "2025-01-01T01:55:26.190033", + "exception": false, + "start_time": "2025-01-01T01:55:20.369290", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cbde2bfd51214dc6b88f049830a8902c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "README.md: 0%| | 0.00/631 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Merge to 4bit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained_merged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"model\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"merged_4bit\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub_merged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"scoliono/miku_vision_instruct-3.2-11b\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"merged_4bit\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhf_token\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# Just LoRA adapters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/save.py\u001b[0m in \u001b[0;36munsloth_generic_push_to_hub_merged\u001b[0;34m(self, repo_id, tokenizer, save_method, use_temp_dir, commit_message, private, token, max_shard_size, create_pr, safe_serialization, revision, commit_description, tags, temporary_location, maximum_memory_usage)\u001b[0m\n\u001b[1;32m 2229\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0marguments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2230\u001b[0m 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merge_and_overwrite_lora(\n\u001b[0m\u001b[1;32m 2133\u001b[0m \u001b[0mget_model_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2134\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mwith\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth_zoo/saving_utils.py\u001b[0m in \u001b[0;36mprepare_saving\u001b[0;34m(model, save_directory, push_to_hub, max_shard_size, private, token, output_dtype, merge_into_original, low_disk_space_usage, min_size_in_bytes, use_temp_file)\u001b[0m\n\u001b[1;32m 460\u001b[0m \u001b[0;31m# Too small - try using the temporary file system (sometimes large like Kaggle)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0mtry_temp_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtempfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTemporaryDirectory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mignore_cleanup_errors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m \u001b[0mtry_save_directory\u001b[0m \u001b[0;34m=\u001b[0m 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model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", + "if True: model.push_to_hub_merged(\"scoliono/miku_vision_instruct-3.2-11b\", tokenizer, save_method = \"merged_4bit\", token = hf_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", + "id": "0614a39c", + "metadata": { + "id": "TCv4vXHd61i7", + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2641cfb7", + "metadata": { + "execution": { + "iopub.status.busy": "2024-05-31T21:47:32.211354Z", + "iopub.status.idle": "2024-05-31T21:47:32.211744Z", + "shell.execute_reply": "2024-05-31T21:47:32.211556Z", + "shell.execute_reply.started": "2024-05-31T21:47:32.211541Z" + }, + "id": "FqfebeAdT073", + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "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 = \"\")" + ] + }, + { + "cell_type": "markdown", + "id": "a66fdd0a", + "metadata": { + "id": "bDp0zNpwe6U_", + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "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)." + ] + }, + { + "cell_type": "markdown", + "id": "66b076c5", + "metadata": { + "id": "Zt9CHJqO6p30", + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "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", + "\n", + "

\n", + " \n", + " \n", + " Support our work if you can! Thanks!\n", + "
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"id": "0ff91594", - "metadata": { - "id": "IqM-T1RTzY6C", - "papermill": { - "duration": 0.022416, - "end_time": "2024-11-19T19:01:59.936783", - "exception": false, - "start_time": "2024-11-19T19:01:59.914367", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", - "
\n", - " \n", - " \n", - " Join Discord if you need help + support us if you can!\n", - "
\n", - "\n", - "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n", - "\n", - "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp)." - ] - }, - { - "cell_type": "markdown", - "id": "9f31fd0e", - "metadata": { - "papermill": { - "duration": 0.01882, - "end_time": "2024-11-19T19:01:59.975791", - "exception": false, - "start_time": "2024-11-19T19:01:59.956971", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Kaggle is slow - you'll have to wait **5 minutes** for it to install.\n", - "\n", - "I suggest you to use our free Colab notebooks instead. I linked our Mistral Colab notebook here: [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5da70b6b", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-19T19:02:00.014824Z", - "iopub.status.busy": "2024-11-19T19:02:00.014491Z", - "iopub.status.idle": "2024-11-19T19:06:21.486688Z", - "shell.execute_reply": "2024-11-19T19:06:21.485746Z" - }, - "papermill": { - "duration": 261.495285, - "end_time": "2024-11-19T19:06:21.489744", - "exception": false, - "start_time": "2024-11-19T19:01:59.994459", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting pip3-autoremove\r\n", - " Downloading pip3_autoremove-1.2.2-py2.py3-none-any.whl.metadata (2.2 kB)\r\n", - "Requirement already satisfied: pip in /opt/conda/lib/python3.10/site-packages (from pip3-autoremove) (24.0)\r\n", - "Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from pip3-autoremove) (70.0.0)\r\n", - 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"Redoing requirement with just package name...\r\n", - "torch 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n", - " sympy 1.13.3 (/opt/conda/lib/python3.10/site-packages)\r\n", - " mpmath 1.3.0 (/opt/conda/lib/python3.10/site-packages)\r\n", - "torchvision 0.19.0 (/opt/conda/lib/python3.10/site-packages)\r\n", - " torch 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n", - " sympy 1.13.3 (/opt/conda/lib/python3.10/site-packages)\r\n", - " mpmath 1.3.0 (/opt/conda/lib/python3.10/site-packages)\r\n", - "torchaudio 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n", - " torch 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n", - " sympy 1.13.3 (/opt/conda/lib/python3.10/site-packages)\r\n", - " mpmath 1.3.0 (/opt/conda/lib/python3.10/site-packages)\r\n", - "Found existing installation: sympy 1.13.3\r\n", - "Uninstalling sympy-1.13.3:\r\n", - " Successfully uninstalled sympy-1.13.3\r\n", - "Found existing installation: torchvision 0.19.0\r\n", - "Uninstalling torchvision-0.19.0:\r\n", - " Successfully uninstalled torchvision-0.19.0\r\n", - "Found existing installation: mpmath 1.3.0\r\n", - "Uninstalling mpmath-1.3.0:\r\n", - " Successfully uninstalled mpmath-1.3.0\r\n", - "Found existing installation: torch 2.4.0\r\n", - "Uninstalling torch-2.4.0:\r\n", - " Successfully uninstalled torch-2.4.0\r\n", - "Found existing installation: torchaudio 2.4.0\r\n", - "Uninstalling torchaudio-2.4.0:\r\n", - " Successfully uninstalled torchaudio-2.4.0\r\n", - "Looking in indexes: https://download.pytorch.org/whl/cu121\r\n", - "Collecting torch\r\n", - " Downloading https://download.pytorch.org/whl/cu121/torch-2.5.1%2Bcu121-cp310-cp310-linux_x86_64.whl (780.4 MB)\r\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m780.4/780.4 MB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", - "\u001b[?25hCollecting torchvision\r\n", - " Downloading https://download.pytorch.org/whl/cu121/torchvision-0.20.1%2Bcu121-cp310-cp310-linux_x86_64.whl (7.3 MB)\r\n", - 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"\u001b[?25hCollecting triton==3.1.0 (from torch)\r\n", - " Downloading https://download.pytorch.org/whl/triton-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (209.5 MB)\r\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.5/209.5 MB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", - "\u001b[?25hCollecting sympy==1.13.1 (from torch)\r\n", - " Downloading https://download.pytorch.org/whl/sympy-1.13.1-py3-none-any.whl (6.2 MB)\r\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.2/6.2 MB\u001b[0m \u001b[31m85.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", - "\u001b[?25hCollecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)\r\n", - " Downloading https://download.pytorch.org/whl/cu121/nvidia_nvjitlink_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (19.8 MB)\r\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.8/19.8 MB\u001b[0m \u001b[31m72.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", - "\u001b[?25hCollecting mpmath<1.4,>=1.1.0 (from sympy==1.13.1->torch)\r\n", - " Downloading https://download.pytorch.org/whl/mpmath-1.3.0-py3-none-any.whl (536 kB)\r\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.2/536.2 kB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", - "\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision) (1.26.4)\r\n", - "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision) (10.3.0)\r\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch) (2.1.5)\r\n", - "Installing collected packages: mpmath, triton, sympy, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, xformers, torchvision, torchaudio\r\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n", - "fastai 2.7.17 requires torch<2.5,>=1.10, but you have torch 2.5.1+cu121 which is incompatible.\u001b[0m\u001b[31m\r\n", - "\u001b[0mSuccessfully installed mpmath-1.3.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.1.105 nvidia-nvtx-cu12-12.1.105 sympy-1.13.1 torch-2.5.1+cu121 torchaudio-2.5.1+cu121 torchvision-0.20.1+cu121 triton-3.1.0 xformers-0.0.28.post3\r\n", - "Collecting unsloth[kaggle-new]\r\n", - " Downloading unsloth-2024.11.7-py3-none-any.whl.metadata (59 kB)\r\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.7/59.7 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", - "\u001b[?25hCollecting unsloth-zoo>=2024.11.1 (from unsloth[kaggle-new])\r\n", - " Downloading unsloth_zoo-2024.11.5-py3-none-any.whl.metadata (16 kB)\r\n", - "Requirement already satisfied: torch>=2.4.0 in /opt/conda/lib/python3.10/site-packages (from unsloth[kaggle-new]) (2.5.1+cu121)\r\n", - "Requirement already satisfied: xformers>=0.0.27.post2 in /opt/conda/lib/python3.10/site-packages (from unsloth[kaggle-new]) (0.0.28.post3)\r\n", - "Collecting bitsandbytes (from unsloth[kaggle-new])\r\n", - " Downloading bitsandbytes-0.44.1-py3-none-manylinux_2_24_x86_64.whl.metadata (3.5 kB)\r\n", - "Requirement already satisfied: triton>=3.0.0 in /opt/conda/lib/python3.10/site-packages (from unsloth[kaggle-new]) (3.1.0)\r\n", - "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from unsloth[kaggle-new]) (21.3)\r\n", - "Collecting tyro (from unsloth[kaggle-new])\r\n", - " Downloading tyro-0.9.1-py3-none-any.whl.metadata (9.3 kB)\r\n", - "Collecting transformers>=4.46.1 (from unsloth[kaggle-new])\r\n", - 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"Requirement already satisfied: accelerate>=0.34.1 in /opt/conda/lib/python3.10/site-packages (from unsloth[kaggle-new]) (0.34.2)\r\n", - "Collecting trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9 (from unsloth[kaggle-new])\r\n", - " Downloading trl-0.12.1-py3-none-any.whl.metadata (10 kB)\r\n", - "Collecting peft!=0.11.0,>=0.7.1 (from unsloth[kaggle-new])\r\n", - " Downloading peft-0.13.2-py3-none-any.whl.metadata (13 kB)\r\n", - "Requirement already satisfied: protobuf<4.0.0 in /opt/conda/lib/python3.10/site-packages (from unsloth[kaggle-new]) (3.20.3)\r\n", - "Requirement already satisfied: huggingface-hub in /opt/conda/lib/python3.10/site-packages (from unsloth[kaggle-new]) (0.25.1)\r\n", - "Collecting hf-transfer (from unsloth[kaggle-new])\r\n", - " Downloading hf_transfer-0.1.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB)\r\n", - "Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from accelerate>=0.34.1->unsloth[kaggle-new]) (6.0.2)\r\n", - 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"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (12.1.105)\r\n", - "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (12.1.105)\r\n", - "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (12.1.105)\r\n", - "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (9.1.0.70)\r\n", - "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (12.1.3.1)\r\n", - "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (11.0.2.54)\r\n", - "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (10.3.2.106)\r\n", - "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (11.4.5.107)\r\n", - "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (12.1.0.106)\r\n", - "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (2.21.5)\r\n", - "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (12.1.105)\r\n", - "Requirement already satisfied: sympy==1.13.1 in /opt/conda/lib/python3.10/site-packages (from torch>=2.4.0->unsloth[kaggle-new]) (1.13.1)\r\n", - "Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=2.4.0->unsloth[kaggle-new]) (12.1.105)\r\n", - "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from sympy==1.13.1->torch>=2.4.0->unsloth[kaggle-new]) (1.3.0)\r\n", - "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.46.1->unsloth[kaggle-new]) (2024.5.15)\r\n", - "Requirement already satisfied: tokenizers<0.21,>=0.20 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.46.1->unsloth[kaggle-new]) (0.20.0)\r\n", - "Requirement already satisfied: rich in /opt/conda/lib/python3.10/site-packages (from trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth[kaggle-new]) (13.7.1)\r\n", - "Requirement already satisfied: docstring-parser>=0.16 in /opt/conda/lib/python3.10/site-packages (from tyro->unsloth[kaggle-new]) (0.16)\r\n", - "Collecting shtab>=1.5.6 (from tyro->unsloth[kaggle-new])\r\n", - " Downloading shtab-1.7.1-py3-none-any.whl.metadata (7.3 kB)\r\n", - "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets>=2.16.0->unsloth[kaggle-new]) (1.3.1)\r\n", - "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets>=2.16.0->unsloth[kaggle-new]) (23.2.0)\r\n", - 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"Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth[kaggle-new]) (1.26.18)\r\n", - "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth[kaggle-new]) (2024.8.30)\r\n", - "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from rich->trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth[kaggle-new]) (3.0.0)\r\n", - "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/conda/lib/python3.10/site-packages (from rich->trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth[kaggle-new]) (2.18.0)\r\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=2.4.0->unsloth[kaggle-new]) (2.1.5)\r\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets>=2.16.0->unsloth[kaggle-new]) (2.9.0.post0)\r\n", - 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"Found existing installation: unsloth 2024.11.7\r\n", - "Uninstalling unsloth-2024.11.7:\r\n", - " Successfully uninstalled unsloth-2024.11.7\r\n", - "Collecting git+https://github.com/unslothai/unsloth.git@a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n", - " Cloning https://github.com/unslothai/unsloth.git (to revision a2f8db3e7341f983af5814a2c56f54fa29ee548d) to /tmp/pip-req-build-7w3hakz0\r\n", - " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-7w3hakz0\r\n", - " Running command git rev-parse -q --verify 'sha^a2f8db3e7341f983af5814a2c56f54fa29ee548d'\r\n", - " Running command git fetch -q https://github.com/unslothai/unsloth.git a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n", - " Running command git checkout -q a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n", - " Resolved https://github.com/unslothai/unsloth.git to commit a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n", - " Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \bdone\r\n", - "\u001b[?25h Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n", - "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n", - "\u001b[?25hBuilding wheels for collected packages: unsloth\r\n", - " Building wheel for unsloth (pyproject.toml) ... \u001b[?25l-\b \b\\\b \bdone\r\n", - "\u001b[?25h Created wheel for unsloth: filename=unsloth-2024.10.7-py3-none-any.whl size=164376 sha256=318d24041afad463f487f3927388d766e913ffa5b694f3e2e3b1a7851fa67a1c\r\n", - " Stored in directory: /root/.cache/pip/wheels/d5/c3/0d/98b9068092121456c620edb0a24e05fda5934229b776b63a7b\r\n", - "Successfully built unsloth\r\n", - "Installing collected packages: unsloth\r\n", - "Successfully installed unsloth-2024.10.7\r\n" - ] - } - ], - "source": [ - "#%%capture\n", - "!pip install pip3-autoremove\n", - "!pip-autoremove torch torchvision torchaudio -y\n", - "!pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121\n", - "# https://github.com/unslothai/unsloth/issues/1284\n", - "!pip install unsloth[kaggle-new]\n", - "# Also get the latest nightly Unsloth!\n", - "!pip uninstall unsloth -y && pip install git+https://github.com/unslothai/unsloth.git@a2f8db3e7341f983af5814a2c56f54fa29ee548d" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6018b225", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-19T19:06:21.619747Z", - "iopub.status.busy": "2024-11-19T19:06:21.618961Z", - "iopub.status.idle": "2024-11-19T19:06:41.479598Z", - "shell.execute_reply": "2024-11-19T19:06:41.478738Z" - }, - "papermill": { - "duration": 19.925903, - "end_time": "2024-11-19T19:06:41.482153", - "exception": false, - "start_time": "2024-11-19T19:06:21.556250", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/unslothai/unsloth-zoo.git\r\n", - " Cloning https://github.com/unslothai/unsloth-zoo.git to /tmp/pip-req-build-0xpxksif\r\n", - " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth-zoo.git /tmp/pip-req-build-0xpxksif\r\n", - " Resolved https://github.com/unslothai/unsloth-zoo.git to commit f5421838ef8278cf96d0092d8271ecd6d433588c\r\n", - " Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \bdone\r\n", - "\u001b[?25h Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n", - "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n", - "\u001b[?25hRequirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (2.5.1+cu121)\r\n", - "Requirement already satisfied: triton in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (3.1.0)\r\n", - "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (21.3)\r\n", - "Requirement already satisfied: tyro in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (0.9.1)\r\n", - "Requirement already satisfied: transformers>=4.46.1 in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (4.46.3)\r\n", - 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"Requirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth_zoo==2024.11.5) (0.1.2)\r\n", - "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets>=2.16.0->unsloth_zoo==2024.11.5) (1.16.0)\r\n" - ] - } - ], - "source": [ - "!pip install git+https://github.com/unslothai/unsloth-zoo.git\n", - "import os\n", - "os.environ[\"UNSLOTH_IS_PRESENT\"] = \"1\"" - ] - }, - { - "cell_type": "markdown", - "id": "6c8091fe", - "metadata": { - "id": "r2v_X2fA0Df5", - "papermill": { - "duration": 0.064606, - "end_time": "2024-11-19T19:06:41.612002", - "exception": false, - "start_time": "2024-11-19T19:06:41.547396", - "status": "completed" - }, - "tags": [] - }, - "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", - "* [**NEW**] 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." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c7d55dc3", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-19T19:06:41.737888Z", - "iopub.status.busy": "2024-11-19T19:06:41.737538Z", - "iopub.status.idle": "2024-11-19T19:08:58.672000Z", - "shell.execute_reply": "2024-11-19T19:08:58.671103Z" - }, - "id": "QmUBVEnvCDJv", - "outputId": "5eff0d61-05b4-471c-eea2-c2e84a915109", - "papermill": { - "duration": 136.999725, - "end_time": "2024-11-19T19:08:58.674026", - "exception": false, - "start_time": "2024-11-19T19:06:41.674301", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", - "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", - "==((====))== Unsloth 2024.10.7: Fast Llama patching. Transformers = 4.46.3.\n", - " \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform = Linux.\n", - "O^O/ \\_/ \\ Pytorch: 2.5.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", - "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. 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" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "52307514a7d14c388004fc8ae3e7378e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "model.safetensors.index.json: 0%| | 0.00/23.9k [00:00.\n" - ] - } - ], - "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/llama-3-8b-bnb-4bit\",\n", - " \"unsloth/llama-3-70b-bnb-4bit\",\n", - "] # More models at https://huggingface.co/unsloth\n", - "\n", - "model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name = \"mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\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", - "id": "2775c72b", - "metadata": { - "id": "SXd9bTZd1aaL", - "papermill": { - "duration": 0.072004, - "end_time": "2024-11-19T19:08:58.812761", - "exception": false, - "start_time": "2024-11-19T19:08:58.740757", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d4d1a72a", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-19T19:08:58.951114Z", - "iopub.status.busy": "2024-11-19T19:08:58.950567Z", - "iopub.status.idle": "2024-11-19T19:09:04.490905Z", - "shell.execute_reply": "2024-11-19T19:09:04.490238Z" - }, - "id": "6bZsfBuZDeCL", - "outputId": "b630cc80-ff95-45a2-cc0d-38666010d73b", - "papermill": { - "duration": 5.61606, - "end_time": "2024-11-19T19:09:04.492928", - "exception": false, - "start_time": "2024-11-19T19:08:58.876868", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Unsloth 2024.10.7 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 = 32, # 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 = \"unsloth\", # 4x longer contexts auto supported!\n", - " random_state = 3407,\n", - " use_rslora = False, # We support rank stabilized LoRA\n", - " loftq_config = None, # And LoftQ\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "cca764a5", - "metadata": { - "id": "vITh0KVJ10qX", - "papermill": { - "duration": 0.063926, - "end_time": "2024-11-19T19:09:04.622692", - "exception": false, - "start_time": "2024-11-19T19:09:04.558766", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "### Data Prep\n", - "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", - "\n", - "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", - "\n", - "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", - "\n", - "If you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n", - "\n", - "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "69a832a3", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-19T19:09:04.754265Z", - "iopub.status.busy": "2024-11-19T19:09:04.753481Z", - "iopub.status.idle": "2024-11-19T19:09:06.180842Z", - "shell.execute_reply": "2024-11-19T19:09:06.180121Z" - }, - "id": "LjY75GoYUCB8", - "outputId": "9f40f734-788c-4793-c1af-e9d003337612", - "papermill": { - "duration": 1.495636, - "end_time": "2024-11-19T19:09:06.182870", - "exception": false, - "start_time": "2024-11-19T19:09:04.687234", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from datasets import load_dataset\n", - "import json\n", - "from unsloth.chat_templates import get_chat_template\n", - "\n", - "tokenizer = get_chat_template(\n", - " tokenizer,\n", - " chat_template = \"llama-3\", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth\n", - " #mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"}, # ShareGPT style\n", - " map_eos_token = True, # Maps <|im_end|> to instead\n", - ")\n", - "\n", - "def formatting_prompts_func(convos):\n", - " texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n", - " return { \"text\" : texts, }\n", - "\n", - "with open(\"/kaggle/input/the-group-chat/output-10k-c-dropout-nonames-replies.json\") as chatfile:\n", - " convos = [json.loads(j) for j in chatfile.readlines()]\n", - "\n", - "with open(\"/kaggle/input/toxicqa/toxicQAfinal.json\") as chatfile:\n", - " convos += [json.loads(j) for j in chatfile.readlines()]\n", - " \n", - "dataset = formatting_prompts_func(convos)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "6b4a347d", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-19T19:09:06.314334Z", - "iopub.status.busy": "2024-11-19T19:09:06.313377Z", - "iopub.status.idle": "2024-11-19T19:09:06.739552Z", - "shell.execute_reply": "2024-11-19T19:09:06.738597Z" - }, - "papermill": { - "duration": 0.493416, - "end_time": "2024-11-19T19:09:06.741610", - "exception": false, - "start_time": "2024-11-19T19:09:06.248194", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from datasets import Dataset\n", - "dataset = Dataset.from_dict(dataset)" - ] - }, - { - "cell_type": "markdown", - "id": "4c45849c", - "metadata": { - "id": "idAEIeSQ3xdS", - "papermill": { - "duration": 0.064215, - "end_time": "2024-11-19T19:09:06.871810", - "exception": false, - "start_time": "2024-11-19T19:09:06.807595", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "### Train the model\n", - "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7bbc400a", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-19T19:09:07.001740Z", - "iopub.status.busy": "2024-11-19T19:09:07.000573Z", - "iopub.status.idle": "2024-11-19T19:09:24.425284Z", - "shell.execute_reply": "2024-11-19T19:09:24.424466Z" - }, - "id": "95_Nn-89DhsL", - "outputId": "4b809e6d-271f-446f-dec8-abe0d13259f8", - "papermill": { - "duration": 17.491445, - "end_time": "2024-11-19T19:09:24.427211", - "exception": false, - "start_time": "2024-11-19T19:09:06.935766", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0f0c38ccb6c0402f84a66639ce3b0a2c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Map (num_proc=2): 0%| | 0/17983 [00:00\n", - " \n", - " \n", - " [2248/2248 8:44:22, Epoch 1/1]\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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StepTraining Loss
12.630800
23.890600
32.046200
42.309300
52.590900
62.039400
71.953500
81.769300
92.016900
101.801700
111.576400
121.695400
132.032200
141.696800
152.109500
162.254800
171.357900
181.598300
191.539700
201.648300
211.754000
221.735000
232.434300
241.987900
251.295100
262.180100
272.082700
281.410300
291.446500
301.435300
311.730600
321.551800
331.482700
341.575600
352.223500
362.106000
371.657500
381.472100
391.612800
401.556300
411.471300
421.350800
431.383000
441.837300
451.466900
461.402600
471.303800
481.289400
492.615500
501.423800
511.415600
521.592000
531.259700
541.572500
551.458800
561.322500
571.411800
581.847200
591.725800
601.620000
611.664900
621.662400
632.695700
641.526500
651.645500
661.431200
672.222500
681.723900
691.636600
701.557700
711.690900
722.912400
731.290300
741.954400
751.888500
761.399600
771.522700
781.376900
791.562900
801.479700
811.277100
821.612200
831.596400
841.767900
851.235800
861.574400
871.754300
882.280800
891.484400
901.970000
912.784900
921.193300
931.194500
941.298700
951.497400
961.489800
971.329000
981.558400
991.790600
1001.515000
1011.375700
1021.339900
1031.910300
1041.336000
1051.809300
1061.585100
1071.396300
1081.708700
1091.253400
1101.390500
1111.351900
1121.464300
1131.383400
1141.834200
1151.374400
1161.805500
1171.491200
1181.725400
1191.205700
1201.665300
1211.563900
1221.415700
1231.667000
1241.442300
1251.666900
1261.890500
1272.156600
1281.403700
1291.523600
1301.526900
1311.265600
1321.518900
1331.244900
1341.950600
1351.854500
1361.391500
1371.904300
1381.427200
1391.240400
1401.249600
1411.292200
1422.007600
1431.521400
1442.039100
1451.960900
1461.939400
1471.669800
1481.558000
1491.829700
1501.790300
1512.170600
1521.371500
1532.082900
1541.852700
1551.285800
1561.336700
1571.375500
1581.764100
1591.345000
1601.140800
1611.412700
1621.752300
1631.368300
1641.309200
1651.354100
1661.696000
1671.723500
1681.511200
1691.483300
1701.415700
1711.287400
1721.483200
1731.562700
1741.361400
1752.597900
1761.436000
1771.306000
1781.416300
1791.876800
1801.555100
1811.620600
1822.247000
1831.629000
1842.113300
1851.246700
1861.565700
1871.504300
1881.827100
1891.559800
1901.321600
1911.795400
1921.367100
1931.439100
1941.281500
1951.306900
1961.875500
1971.467800
1982.002600
1991.374800
2001.397600
2011.292000
2021.574200
2031.563400
2041.942800
2051.298600
2061.613900
2071.309400
2081.714200
2091.500000
2101.453800
2111.489000
2121.647800
2131.430400
2141.426000
2151.686200
2161.649600
2171.787100
2181.456800
2191.419800
2201.432400
2211.380800
2221.598300
2231.831000
2241.740900
2251.429500
2261.643800
2272.096100
2281.973500
2291.752800
2301.656700
2311.238000
2321.988300
2331.654400
2341.746100
2351.384200
2361.585900
2371.876900
2381.353400
2391.578700
2401.437200
2411.253500
2421.366800
2431.374000
2441.716100
2451.207600
2461.333400
2471.428700
2481.522900
2491.558700
2501.341900
2511.483800
2521.863500
2531.339100
2541.506000
2551.699300
2561.450600
2571.396900
2581.934000
2591.916600
2601.467200
2611.481400
2621.489800
2631.483800
2641.201600
2651.425700
2661.960000
2671.179500
2681.530600
2691.489700
2701.752900
2711.117600
2721.522700
2731.337000
2741.435400
2751.799900
2761.513800
2771.675900
2781.576400
2791.201000
2801.514300
2811.182400
2821.476700
2831.749500
2841.393500
2851.219900
2862.029000
2871.613700
2881.534200
2891.598400
2901.638300
2911.297900
2921.190500
2931.308400
2941.684000
2951.529900
2961.313900
2971.689900
2981.836100
2990.988100
3002.004800
3011.471100
3021.772600
3031.634900
3041.552100
3051.773300
3061.281600
3071.880300
3081.302500
3091.628900
3101.379500
3111.751200
3121.635100
3131.433400
3141.383600
3151.943200
3161.407600
3171.611600
3181.418900
3191.279200
3201.244300
3211.520300
3221.269600
3231.691100
3241.492600
3251.520900
3261.526200
3271.318200
3281.447700
3291.462800
3301.310700
3311.142200
3321.602700
3331.547900
3341.257900
3351.455500
3361.856100
3371.951500
3381.285300
3391.459400
3401.330600
3411.553900
3421.273900
3431.747800
3441.244400
3451.430000
3461.529500
3471.239300
3481.446900
3491.354200
3501.366100
3511.577100
3521.198800
3531.002100
3541.733200
3551.396900
3561.196100
3572.214000
3581.258000
3591.507500
3601.523100
3611.775900
3621.635000
3631.403300
3641.290600
3651.910600
3661.062600
3671.305800
3681.496100
3691.966700
3701.938000
3711.379900
3721.668600
3731.817900
3741.280400
3751.392400
3761.321900
3771.183100
3781.154900
3791.798800
3801.418800
3811.549300
3821.545200
3831.501500
3841.887700
3851.446700
3861.279900
3871.308700
3881.602800
3891.582900
3901.423400
3911.529300
3921.696300
3931.673200
3941.109700
3951.248800
3961.089700
3971.326600
3981.688600
3991.681000
4001.423900
4011.131800
4021.154600
4031.463200
4041.229600
4052.188300
4061.538900
4071.662500
4081.718800
4091.526500
4101.792600
4111.354700
4121.364100
4131.441500
4141.432600
4151.684900
4161.885400
4172.052100
4181.424000
4191.474100
4201.130200
4212.011000
4221.323600
4231.810000
4241.666700
4251.281500
4261.930800
4271.210800
4282.097600
4291.300800
4301.525600
4312.123900
4321.948600
4331.202800
4341.412100
4351.424500
4361.254200
4371.594300
4381.343600
4392.224800
4401.648500
4411.470300
4421.676900
4431.660600
4441.278800
4451.455500
4461.843400
4471.452500
4481.401100
4491.349800
4501.570700
4511.419100
4521.579500
4531.726000
4541.226900
4551.650000
4562.521900
4571.394800
4581.665600
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4601.723900
4611.355500
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4641.365700
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4671.463700
4681.329600
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4711.581700
4721.282900
4731.661500
4741.645100
4751.325900
4761.704000
4771.312400
4781.279000
4791.162900
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4821.411800
4831.143400
4841.720400
4851.269200
4861.291000
4871.524500
4881.729100
4891.271900
4901.582800
4911.221200
4921.439500
4931.528500
4941.775500
4951.594600
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4971.791400
4981.397800
4991.740400
5001.209500
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5021.062200
5031.355400
5041.768400
5051.225800
5061.263000
5071.456800
5081.314900
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5101.589900
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5211.249500
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5271.852700
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5301.575700
5311.728200
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5401.526600
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5491.710700
5501.859800
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5521.769000
5531.948400
5541.805500
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5571.152000
5581.914600
5591.250400
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5701.357200
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5721.378900
5731.306300
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5901.694700
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6041.417000
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7031.686500
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7061.263900
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7081.991300
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "trainer_stats = trainer.train()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c4c8f35c", - "metadata": { - "cellView": "form", - "execution": { - "iopub.execute_input": "2024-11-20T03:54:09.565224Z", - "iopub.status.busy": "2024-11-20T03:54:09.564605Z", - "iopub.status.idle": "2024-11-20T03:54:09.570920Z", - "shell.execute_reply": "2024-11-20T03:54:09.570099Z" - }, - "id": "pCqnaKmlO1U9", - "outputId": "cf63d152-e152-468c-ba0d-938e0d2f71a0", - "papermill": { - "duration": 0.079663, - "end_time": "2024-11-20T03:54:09.572687", - "exception": false, - "start_time": "2024-11-20T03:54:09.493024", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "31481.3839 seconds used for training.\n", - "524.69 minutes used for training.\n", - "Peak reserved memory = 9.363 GB.\n", - "Peak reserved memory for training = 3.191 GB.\n", - "Peak reserved memory % of max memory = 63.517 %.\n", - "Peak reserved memory for training % of max memory = 21.647 %.\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", - "id": "6176fe1e", - "metadata": { - "id": "ekOmTR1hSNcr", - "papermill": { - "duration": 0.070529, - "end_time": "2024-11-20T03:54:09.714353", - "exception": false, - "start_time": "2024-11-20T03:54:09.643824", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "### Inference\n", - "Let's run the model! You can change the instruction and input - leave the output blank!" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "04171c34", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-20T03:54:09.856688Z", - "iopub.status.busy": "2024-11-20T03:54:09.855912Z", - "iopub.status.idle": "2024-11-20T03:54:09.860764Z", - "shell.execute_reply": "2024-11-20T03:54:09.859922Z" - }, - "id": "kR3gIAX-SM2q", - "outputId": "5b71f982-38c0-44c8-a4e5-58cd20b5a585", - "papermill": { - "duration": 0.077355, - "end_time": "2024-11-20T03:54:09.862368", - "exception": false, - "start_time": "2024-11-20T03:54:09.785013", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "if False:\n", - " # 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)" - ] - }, - { - "cell_type": "markdown", - "id": "ec51acf7", - "metadata": { - "id": "CrSvZObor0lY", - "papermill": { - "duration": 0.070507, - "end_time": "2024-11-20T03:54:10.004648", - "exception": false, - "start_time": "2024-11-20T03:54:09.934141", - "status": "completed" - }, - "tags": [] - }, - "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": 12, - "id": "97035490", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-20T03:54:10.145654Z", - "iopub.status.busy": "2024-11-20T03:54:10.145402Z", - "iopub.status.idle": "2024-11-20T03:54:10.150359Z", - "shell.execute_reply": "2024-11-20T03:54:10.149572Z" - }, - "id": "e2pEuRb1r2Vg", - "outputId": "084aab62-2122-436a-c0cb-8871986640eb", - "papermill": { - "duration": 0.077256, - "end_time": "2024-11-20T03:54:10.151976", - "exception": false, - "start_time": "2024-11-20T03:54:10.074720", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "if False:\n", - " # 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)" - ] - }, - { - "cell_type": "markdown", - "id": "a4179ebd", - "metadata": { - "id": "uMuVrWbjAzhc", - "papermill": { - "duration": 0.070519, - "end_time": "2024-11-20T03:54:10.292849", - "exception": false, - "start_time": "2024-11-20T03:54:10.222330", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "\n", - "### Saving, loading finetuned models\n", - "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", - "\n", - "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3974619b", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-20T03:54:10.434709Z", - "iopub.status.busy": "2024-11-20T03:54:10.434458Z", - "iopub.status.idle": "2024-11-20T03:54:15.042600Z", - "shell.execute_reply": "2024-11-20T03:54:15.041442Z" - }, - "id": "upcOlWe7A1vc", - "papermill": { - "duration": 4.681486, - "end_time": "2024-11-20T03:54:15.044402", - "exception": false, - "start_time": "2024-11-20T03:54:10.362916", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "20ebd44d861249a9bbba6b4d7cf7a1af", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "README.md: 0%| | 0.00/615 [00:00\n", - " \n", - " \n", - " Support our work if you can! Thanks!\n", - "" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kaggle": { - "accelerator": "nvidiaTeslaT4", - "dataSources": [ - { - "datasetId": 5080560, - "sourceId": 8511152, - "sourceType": "datasetVersion" - }, - { - "datasetId": 4675483, - "sourceId": 9945992, - "sourceType": "datasetVersion" - } - ], - "isGpuEnabled": true, - "isInternetEnabled": true, - "language": "python", - "sourceType": "notebook" - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.10.14" - }, - "papermill": { - "default_parameters": {}, - "duration": 31943.963858, - "end_time": "2024-11-20T03:54:20.121228", - "environment_variables": {}, - "exception": null, - 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