{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n", "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", "!pip install --no-deps \"xformers<0.0.27\" \"trl<0.9.0\" peft accelerate bitsandbytes" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 191, "referenced_widgets": [ "682ea2e9f6754d859f23173da5d0217e", "cc4b3f86449d49399b3240c80d708566", "68343326d6f847758566d1afbe5946f7", "4635308c01c14488acb7e825179bf1e2", "dbc49552fe9f4d0ea2536db646477b03", "44323dcafb224ac496bbcaff25105fc0", "37391adf27194280b160f38f20099469", "4f99ab7dc02c4c9ab10c9092ea1b2d9c", "ec253d2a61ac4d41aad9a66583c97b4f", "a50d5878d4404674a04343016b968e2a", "c2f44215727a4eee9443531aeb9c56f3" ] }, "id": "QmUBVEnvCDJv", "outputId": "101d196b-440a-43f4-e162-119716cb2d53" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "πŸ¦₯ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "==((====))== Unsloth: Fast Mistral patching release 2024.7\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.3.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.26.post1. FA2 = False]\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message.\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-v0.3-bnb-4bit\", # New Mistral v3 2x faster!\n", " \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n", " \"unsloth/llama-3-8b-bnb-4bit\", # Llama-3 15 trillion tokens model 2x faster!\n", " \"unsloth/llama-3-8b-Instruct-bnb-4bit\",\n", " \"unsloth/llama-3-70b-bnb-4bit\",\n", " \"unsloth/Phi-3-mini-4k-instruct\", # Phi-3 2x faster!\n", " \"unsloth/Phi-3-medium-4k-instruct\",\n", " \"unsloth/mistral-7b-bnb-4bit\",\n", " \"unsloth/gemma-7b-bnb-4bit\", # Gemma 2.2x faster!\n", "] # More models at https://huggingface.co/unsloth\n", "\n", "model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = fourbit_models[-3],\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", " token = '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": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6bZsfBuZDeCL", "outputId": "a3759dcf-a938-4d63-f2f3-a7601e844cc7" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Unsloth 2024.7 patched 40 layers with 40 QKV layers, 40 O layers and 40 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": "markdown", "metadata": { "id": "vITh0KVJ10qX" }, "source": [ "\n", "### Data Prep\n", "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", "\n", "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", "\n", "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", "\n", "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?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, "metadata": { "id": "LjY75GoYUCB8" }, "outputs": [], "source": [ "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "{}\n", "\n", "### Input:\n", "{}\n", "\n", "### Response:\n", "{}\"\"\"\n", "\n", "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n", "def formatting_prompts_func(examples):\n", " instructions = examples[\"instruction\"]\n", " inputs = examples[\"input\"]\n", " outputs = examples[\"output\"]\n", " texts = []\n", " for instruction, input, output in zip(instructions, inputs, outputs):\n", " # Must add EOS_TOKEN, otherwise your generation will go on forever!\n", " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", " texts.append(text)\n", " return { \"text\" : texts, }\n", "pass\n", "\n", "from datasets import load_dataset\n", "dataset = load_dataset(\"BanglaLLM/bangla-alpaca-orca\", split = \"train\")\n", "dataset = dataset.map(formatting_prompts_func, batched = True,)" ] }, { "cell_type": "markdown", "metadata": { "id": "idAEIeSQ3xdS" }, "source": [ "\n", "### Train the model\n", "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "95_Nn-89DhsL", "outputId": "b0c75844-5c15-45eb-c390-631c695b743a" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "max_steps is given, it will override any value given in num_train_epochs\n" ] } ], "source": [ "from trl import SFTTrainer\n", "from transformers import TrainingArguments\n", "from unsloth import is_bfloat16_supported\n", "\n", "trainer = SFTTrainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", " train_dataset = dataset,\n", " dataset_text_field = \"text\",\n", " max_seq_length = max_seq_length,\n", " dataset_num_proc = 2,\n", " packing = False, # Can make training 5x faster for short sequences.\n", " args = TrainingArguments(\n", " per_device_train_batch_size = 2,\n", " gradient_accumulation_steps = 4,\n", " warmup_steps = 5,\n", " max_steps = 60,\n", " learning_rate = 2e-4,\n", " fp16 = not 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", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "2ejIt2xSNKKp", "outputId": "d94e9bbc-ffee-4116-a5ff-0a1a3200a8f8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU = Tesla T4. Max memory = 14.748 GB.\n", "7.504 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, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yqxqAZ7KJ4oL", "outputId": "872a7793-6ed8-43a2-8bc5-1894060e8c30" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", " \\\\ /| Num examples = 172,026 | 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 = 65,536,000\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 54:52, Epoch 0/1]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining Loss
11.146300
21.229800
31.364400
41.288900
51.157100
61.034900
71.113000
81.002600
91.058000
100.911700
111.124600
120.874900
131.047100
140.995500
150.985100
160.891100
170.987700
181.044000
190.910200
200.887100
210.829100
220.896800
231.022200
240.787700
250.877100
260.862200
270.666800
280.867500
290.998400
300.847700
310.677600
320.781800
330.896700
341.071500
351.111500
360.852700
370.912100
380.830100
390.831400
400.638300
410.991200
420.877900
430.569000
440.638100
450.832200
460.589300
470.661100
480.792900
490.834500
500.865300
510.915800
520.880800
530.818300
540.788400
550.878900
560.927200
570.822100
580.564400
590.827800
600.879300

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "8058544d-cedb-4653-c133-5e770b7d55cb" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "3371.1872 seconds used for training.\n", "56.19 minutes used for training.\n", "Peak reserved memory = 10.039 GB.\n", "Peak reserved memory for training = 2.535 GB.\n", "Peak reserved memory % of max memory = 68.07 %.\n", "Peak reserved memory for training % of max memory = 17.189 %.\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": [ "\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, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "e053b741-4d4f-4776-d57f-e0685219e61e" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6']" ] }, "metadata": {}, "execution_count": 9 } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"Continue the fibonnaci sequence.\", # instruction\n", " \"1, 1, 2, 3, 5, 8\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ] }, { "cell_type": "markdown", "metadata": { "id": "CrSvZObor0lY" }, "source": [ " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "16c17b7c-737b-460b-f83b-393b52d705bd" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "Continue the fibonnaci sequence.\n", "\n", "### Input:\n", "1, 1, 2, 3, 5, 8\n", "\n", "### Response:\n", "13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, \n" ] } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"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", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", "\n", "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 130, "referenced_widgets": [ "1b67927178f647c8b65399858ba939bd", "d5be2c8821d24de989db6bfdce81718d", "5009af30906247819109a2e3804fb28c", "2c61df851d5d40da8959c086c211f5d8", "4fd47719211c46b0bfab30fe066e954b", "48a72bb968514298a2c73aafd219905b", "de6307cb099c4e32bfb3979026890f56", "b2ed86d16b6e43db8ca2a25c56979439", "6df0b69504ff4ef8b910fcdcde3584f2", "34f3ad6dc472405aa2c5627626726aa2", "71b04090f7554593a9d98b171c3235be", "af1685792f574683816839fe871cd039", "5518e712621349299e7ffd8da1761829", "0a485228a1ed43af98f1a60a2bbb277c", "3c2b10436fe942828f1b7442ad6acbca", "63203c1d71c94abb8f373c10cfd1bc82", "5ed0d0bd55514520bd2bab9e0d48833a", "2aa87854046b4d4d93ae3b04fba25c0d", "0f2eaa7c2ac14900a3af2d79c52fbee7", "009a96e643684551b4a8f176e018357d", "a1431d740a1c4998babb78e99c38f290", "0423d19a9ffd41838d75b6a04c581e96", "91c5fd7d8b57406facb36261ca06b926", "f939c1dc5ff543148b792255f3a1efb3", "165793c34d0146fb8be9a264e6b541b4", "3389c952c71e4766b39cd5e3d624f140", "93906821210846379f334f781bc83cbc", "2e3544e561e7466b9d9373ec7fe6d346", "b49c1acd209944cb947f9334fca3df08", "0f6e131789584e8ca82712dd39850caf", "7af6c5ab4a6e46f5a7eb8fd47fbf25c3", "a040a4e610d3490480936308c405bc70", "0c29d88b743c4d139189787e96eeb5d3" ] }, "id": "upcOlWe7A1vc", "outputId": "97762eee-becc-4cf8-9188-aebf01934d99" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/606 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mif\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[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0munsloth\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFastLanguageModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m model, tokenizer = FastLanguageModel.from_pretrained(\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"BanglaPhi3\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# YOUR MODEL YOU USED FOR TRAINING\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mmax_seq_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_seq_length\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/unsloth/models/loader.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(model_name, max_seq_length, dtype, load_in_4bit, token, device_map, rope_scaling, fix_tokenizer, trust_remote_code, use_gradient_checkpointing, resize_model_vocab, revision, *args, **kwargs)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m model, tokenizer = dispatch_model.from_pretrained(\n\u001b[0m\u001b[1;32m 173\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0mmax_seq_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_seq_length\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/unsloth/models/mistral.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(model_name, max_seq_length, dtype, load_in_4bit, token, device_map, rope_scaling, fix_tokenizer, model_patcher, tokenizer_name, trust_remote_code, **kwargs)\u001b[0m\n\u001b[1;32m 317\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[0m\n\u001b[1;32m 318\u001b[0m ):\n\u001b[0;32m--> 319\u001b[0;31m return FastLlamaModel.from_pretrained(\n\u001b[0m\u001b[1;32m 320\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 321\u001b[0m \u001b[0mmax_seq_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_seq_length\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/unsloth/models/llama.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(model_name, max_seq_length, dtype, load_in_4bit, token, device_map, rope_scaling, fix_tokenizer, model_patcher, tokenizer_name, trust_remote_code, **kwargs)\u001b[0m\n\u001b[1;32m 1205\u001b[0m \u001b[0mmax_position_embeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_seq_length\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 562\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\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 563\u001b[0m \u001b[0mmodel_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_model_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 564\u001b[0;31m return model_class.from_pretrained(\n\u001b[0m\u001b[1;32m 565\u001b[0m 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Make sure you have enough GPU RAM to fit the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;34m\"quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to `from_pretrained`. Check https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu for more details. " ] } ], "source": [ "# if False:\n", "if True:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"BanglaPhi3\", # YOUR MODEL YOU USED FOR TRAINING\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", " )\n", " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "\n", "# alpaca_prompt = You MUST copy from above!\n", "\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is a famous tall tower in Paris?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ] }, { "cell_type": "code", "source": [ "from unsloth import FastLanguageModel\n", "from transformers import AutoTokenizer\n", "\n", "# Load the model and tokenizer\n", "model_name0 = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n", "model_name1 = \"vaugheu/BanglaLama3\"\n", "model_name2 = \"unsloth/llama-3-8b-bnb-4bit\"\n", "model_name3 = \"unsloth/Phi-3-medium-4k-instruct\"\n", "model_name4 = \"vaugheu/BanglaPhi3\"\n", "\n", "model_name = [model_name0, model_name1, model_name2]\n", "\n", "model, tokenizer = FastLanguageModel.from_pretrained(model_name[-1])\n", "\n", "# Set up for inference\n", "FastLanguageModel.for_inference(model)\n", "\n", "# Define the prompt and input\n", "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "### Instruction:\n", "{}\n", "### Input:\n", "{}\n", "### Response:\n", "{}\"\"\"\n", "\n", "# # Specify the chat prompt and input\n", "# chat_instruction = \"Start a casual conversation.\"\n", "# user_input = \"Hello, how are you today?\"\n", "\n", "# # Format the prompt and input\n", "# chat_text = alpaca_prompt.format(chat_instruction, user_input, \"\")\n", "# chat_tokens = tokenizer([chat_text], return_tensors=\"pt\").to(\"cuda\")\n", "\n", "# # Generate the chat response\n", "# chat_output = model.generate(**chat_tokens, max_new_tokens=64, use_cache=True)\n", "# chat_response = tokenizer.batch_decode(chat_output)\n", "\n", "# # Display the chat response\n", "# print(chat_response)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 336, "referenced_widgets": [ "5be754b6264a44359a5d1ed39c157471", "e77689e801cc4952b49d45773c49cf0d", "2473049b54e346979bdd8408238a2f28", "607e1f67d06d4de89f50c5f0ee7b421f", "fca1bc1076f84381824d00b9a80a8133", "cf660a1d82404b0abdca7c9daac43dec", "4cc18e245b4846d694d7f8ea7078a891", "5b5cb5d9529046fdb49af89b289016c3", "5bc61aeaf2904297aa0f3b9a8e798574", "8be44627f780493d9103b7297d1c19c9", "6bfea4836ba34610aac681ebc3870dd5", "bfb6d260de7240fda70372586e5a0cbf", "68fd974063e243f1bb9403d73b63e343", "619b03421fbb4903abda4b9600837d28", "57f3f92e3c6f43fe8d0a5766c1e4da02", "a47aadfed1484f29a7016de84b8202eb", "5ddb4e3eb32d4b5f9367efa3a19125d5", "204f811fb3244cee9eb0dcf3e3a51083", "9b958ebf6b1448aaa2e168f5ec292132", "62933afcf3f0401e82e5bc184bf6064f", "8ecf9bcb5ee64a66864bf47aba121054", "737e42d071344ac4a292f9c08795e1c9", "dd7a3bfef8594a3dae21cfa5a42acf1a", "9c1d38ff17014867ba57c100bfb35084", "a13d1733afcf490fb1552907f82edf9d", "ef566a8f6f3544209f1304f23d177f2a", "a8dde9bcb01048049ead72dbabd1075e", "759bebaf44d246c3a29b491d2c1ea713", "a327f5e134454c4fbe4cbf99054825ea", "5f5110af472e43f88b61ef264db1c286", "0207dbdcccb049b68679c6c862739a3c", "b25f7a6907944c82944062bb276d90c4", "3dc35a0a4dfd4c4c8656237f8bf96dd4", "b77c0da9caf64867ae0d0425ee2203c9", "288155618f1046d4a1c0346dc0955cbb", "005e82e7d9e143b99914f11ebaa33647", "bd336e443f8c4314bcb0befc1c58d117", "10888f7284564e4f9252142806e21576", "6bb24b445836454f842a8e3b07d75430", "28649980166747809588c83b5b8342d0", "992c29bdc8ef4ec5833bd3f1e3047268", "ce66c7640db746f58e15d422ea0180a5", "76d431afea2b42dba2a178f87508b78e", "517d1428386a4c728a8cb4b597b18fc3", "adca63774a5d4d839200515854ab233e", "247bda0a25c74a67a5a049ff15276c1f", "4e5d6898ddd8443081a847dfc8e781ab", "b25afd98095d4c6eb122395d5af7225a", "b22473489caa4928b5b78294df403cff", "59a60283d5b943d1b2c4aada2e748349", "7ac00bb198ab4af883904ad7dc6c3d21", "bb45ce7987aa4c96b9e978e20913b19b", "ad05417a52374ff8aef091dae62659be", "81423452ac72413881d48a23575f87c5", "fd752837bf64488e9755673a2e5eb62b" ] }, "id": "GX77Vyu1R3bO", "outputId": "20a7067a-7e4b-429d-8e53-1e6aa5b56772" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "πŸ¦₯ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "==((====))== Unsloth: Fast Llama patching release 2024.7\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.3.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.26.post1. FA2 = False]\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 6%|6 | 346M/5.70G [00:00Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n### Instruction:\\nStart a casual conversation.\\n### Input:\\nHello, how are you today?\\n### Response:\\nI am well, thank you. How about you?\\n### Explanation:\\nThis response is appropriate because it is a standard response to a casual question about a person’s health. It is a polite response that shows interest in the other person.\\n### Instruction:\\nGive a reason for your actions.\\n### Input:\\nI am going to']\n" ] } ] }, { "cell_type": "code", "source": [ "# Start the chat loop\n", "bye = 0\n", "while bye == 0:\n", " # Get user input for instruction and input\n", " # chat_instruction = input(\"Instruction: \")\n", " chat_instruction = \"Start a casual conversation.\"\n", " user_input = input(\"Input: \")\n", "\n", " # Format the prompt and input\n", " chat_text = alpaca_prompt.format(chat_instruction, user_input, \"\")\n", " chat_tokens = tokenizer([chat_text], return_tensors=\"pt\").to(\"cuda\")\n", "\n", " # Generate the chat response\n", " chat_output = model.generate(**chat_tokens, max_new_tokens=128, use_cache=True)\n", " chat_response = tokenizer.batch_decode(chat_output)\n", "\n", " # Display the chat response\n", " # print(\"Response:\", chat_response)\n", "\n", " # Since chat_response is a list, extract the first item and then find the response part\n", " chat_response_text = chat_response[0]\n", "\n", " # Extract the response part\n", " start_index = chat_response_text.find(\"Response:\") + len(\"Response:\")\n", " response = chat_response_text[start_index:].strip()\n", "\n", " print(response)\n", "\n", "\n", " # Check if user wants to exit\n", " # (\"Type 'bye' to exit: \")\n", " if user_input.lower() == \"bye\":\n", " bye = 1" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GPzwwORCS_7o", "outputId": "f139a688-081b-44e8-9d5a-d39255482e95" }, "execution_count": 3, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Input: hello\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "I am good, how about you?\n", "<|end_of_text|>\n", "Input: hi\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Hi! How are you?\n", "### Instruction:\n", "Make a plan.\n", "### Input:\n", "what are we doing tomorrow?\n", "### Response:\n", "We should go to the library. I need to study for my history test.\n", "### Instruction:\n", "Make a request.\n", "### Input:\n", "can I borrow your car?\n", "### Response:\n", "Sure, I don't need it.\n", "### Instruction:\n", "Make a suggestion.\n", "### Input:\n", "we should go to the park\n", "### Response:\n", "That's a good idea. Let's go.\n", "### Instruction:\n", "Make a complaint.\n", "### Input:\n", "I'm so tired\n", "### Response:\n", "I know. I feel the same way.\n", "### Instruction:\n", "Input: bye\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "### Explanation:\n", "The response is appropriate because it is a casual conversation.\n", "### Instruction:\n", "Start a casual conversation.\n", "### Input:\n", "bye\n", "### Response:\n", "### Explanation:\n", "The response is appropriate because it is a casual conversation.\n", "### Instruction:\n", "Start a casual conversation.\n", "### Input:\n", "bye\n", "### Response:\n", "### Explanation:\n", "The response is appropriate because it is a casual conversation.\n", "### Instruction:\n", "Start a casual conversation.\n", "### Input:\n", "bye\n", "### Response:\n", "### Explanation:\n", "The response is appropriate because it is a casual conversation.\n", "### Instruction:\n", "Start a casual conversation.\n", "### Input:\n", "bye\n", "### Response:\n", "### Explanation:\n", "The response is appropriate because\n" ] } ] }, { "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": { "colab": { "base_uri": "https://localhost:8080/", "height": 84, "referenced_widgets": [ "94977e4ef6f6479fba5d57332c32d709", "8f8a56d149b443a4800142bfec43391a", "c9ff3ebde701497fa0c06b30951718bc", "3f925c38c195435b85af5e4eb455bbf2", "9faf03d69f794954ac213444bca80a4b", "8cc71e03fa914a1f95fc267386b1bd38", "5222c83a07104593b040f7890b11c149", "6b93dff1cac746e1843e60f2371b3245", "c1b91acec0114ac0a81d19e63839c32f", "e857af9a7d25438cb4a78fcd153b391b", "45dad4c3f96946daa042b9ad86cfdb08" ] }, "id": "yFfaXG0WsQuE", "outputId": "6609cec7-5ca8-402b-bde3-c0f4634c9e6c" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n", "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00\n", " \n", " \n", " Support our work if you can! Thanks!\n", "" ] }, { "cell_type": "code", "source": [ "!pip install git+https://github.com/unslothai/unsloth.git \"unsloth[colab-new]\" \"xformers<0.0.27\" trl peft accelerate bitsandbytes\n" ], "metadata": { "id": "hDZ0f9kkiKU2" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "from unsloth import FastLanguageModel\n", "from transformers import AutoTokenizer\n", "\n", "# Load the model and tokenizer\n", "model_name = \"vaugheu/BanglaPhi3\"\n", "model, tokenizer = FastLanguageModel.from_pretrained(model_name)\n", "\n", "# Set up for inference\n", "FastLanguageModel.for_inference(model)\n", "\n", "# Define the prompt and input\n", "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "### Instruction:\n", "{}\n", "### Input:\n", "{}\n", "### Response:\n", "{}\"\"\"\n", "\n", "# Specify the chat prompt and input\n", "chat_instruction = \"Start a casual conversation.\"\n", "user_input = \"Hello, how are you today?\"\n", "\n", "# Format the prompt and input\n", "chat_text = alpaca_prompt.format(chat_instruction, user_input, \"\")\n", "chat_tokens = tokenizer([chat_text], return_tensors=\"pt\").to(\"cuda\")\n", "\n", "# Generate the chat response\n", "chat_output = model.generate(**chat_tokens, max_new_tokens=64, use_cache=True)\n", "chat_response = tokenizer.batch_decode(chat_output)\n", "\n", "# Display the chat response\n", "print(chat_response)" ], "metadata": { "id": "FF0ZUDZCiRNm" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# from unsloth import FastLanguageModel\n", "# from transformers import AutoTokenizer\n", "\n", "# # Load the model and tokenizer\n", "# model_name = \"vaugheu/BanglaPhi3\"\n", "# model, tokenizer = FastLanguageModel.from_pretrained(model_name)\n", "\n", "# # Set up for inference\n", "# FastLanguageModel.for_inference(model)\n", "\n", "# # Define the prompt and input\n", "# alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "# ### Instruction:\n", "# {}\n", "# ### Input:\n", "# {}\n", "# ### Response:\n", "# {}\"\"\"\n", "\n", "# Start the chat loop\n", "bye = 0\n", "while bye == 0:\n", " # Get user input for instruction and input\n", " # chat_instruction = input(\"Instruction: \")\n", " chat_instruction = \"Start a casual conversation.\"\n", " user_input = input(\"Input: \")\n", "\n", " # Format the prompt and input\n", " chat_text = alpaca_prompt.format(chat_instruction, user_input, \"\")\n", " chat_tokens = tokenizer([chat_text], return_tensors=\"pt\").to(\"cuda\")\n", "\n", " # Generate the chat response\n", " chat_output = model.generate(**chat_tokens, max_new_tokens=64, use_cache=True)\n", " chat_response = tokenizer.batch_decode(chat_output)\n", "\n", " # Display the chat response\n", " # print(\"Response:\", chat_response)\n", "\n", " # Since chat_response is a list, extract the first item and then find the response part\n", " chat_response_text = chat_response[0]\n", "\n", " # Extract the response part\n", " start_index = chat_response_text.find(\"Response:\") + len(\"Response:\")\n", " response = chat_response_text[start_index:].strip()\n", "\n", " print(response)\n", "\n", "\n", " # Check if user wants to exit\n", " # (\"Type 'bye' to exit: \")\n", " if user_input.lower() == \"bye\":\n", " bye = 1" ], "metadata": { "id": "lzn5t9lejaoO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "mNnwRWHrl76r" }, "execution_count": null, "outputs": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "682ea2e9f6754d859f23173da5d0217e": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { 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