Upload 4 files

adapter model (LoRA)

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "openlm-research/open_llama_3b_v2",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 16,
+  "lora_dropout": 0.05,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d68e85dfb62b16ac694dfb355f93b4bf0958f069735603080378b9ca4c6da9b
+size 10686701

qLora-Training-Cabra - Paperspace.ipynb

@@ -0,0 +1,809 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UQF7nAH1syz4"
+   },
+   "source": [
+    "Based on [alpaca lora](https://github.com/tloen/alpaca-lora/blob/main/finetune.py)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T11:49:03.267959Z",
+     "iopub.status.busy": "2023-07-18T11:49:03.267686Z",
+     "iopub.status.idle": "2023-07-18T11:51:38.082879Z",
+     "shell.execute_reply": "2023-07-18T11:51:38.082079Z",
+     "shell.execute_reply.started": "2023-07-18T11:49:03.267936Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# !apt update\n",
+    "# !apt upgrade -y cuda-nvcc-12-0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T11:51:38.084514Z",
+     "iopub.status.busy": "2023-07-18T11:51:38.084291Z",
+     "iopub.status.idle": "2023-07-18T11:51:40.683284Z",
+     "shell.execute_reply": "2023-07-18T11:51:40.682579Z",
+     "shell.execute_reply.started": "2023-07-18T11:51:38.084488Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "print(\"Torch Version: \" + torch.__version__ + \"\\n\")\n",
+    "\n",
+    "# !nvcc --version\n",
+    "\n",
+    "# !nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "execution": {
+     "iopub.execute_input": "2023-07-18T11:51:40.684754Z",
+     "iopub.status.busy": "2023-07-18T11:51:40.684408Z",
+     "iopub.status.idle": "2023-07-18T11:55:27.915935Z",
+     "shell.execute_reply": "2023-07-18T11:55:27.915414Z",
+     "shell.execute_reply.started": "2023-07-18T11:51:40.684734Z"
+    },
+    "id": "RXurA0q5jtaf",
+    "outputId": "93942094-5399-4f21-9660-fbfd344598ee"
+   },
+   "outputs": [],
+   "source": [
+    "# !pip install -U cuda-python\n",
+    "# !pip3 install -U torch torchvision torchaudio #--index-url https://download.pytorch.org/whl/cu118\n",
+    "\n",
+    "# # Paperspace\n",
+    "# !git clone https://github.com/timdettmers/bitsandbytes.git\n",
+    "# !cd bitsandbytes && CUDA_VERSION=116 make cuda11x && python setup.py install\n",
+    "# !cp /notebooks/bitsandbytes/bitsandbytes/libbitsandbytes_cuda116.so /usr/lib/python3.9/\n",
+    "# !pip install -U bitsandbytes\n",
+    "\n",
+    "# # Google Colab\n",
+    "# #!pip install -U git+https://github.com/TimDettmers/bitsandbytes\n",
+    "\n",
+    "# !pip install -U git+https://github.com/huggingface/transformers.git\n",
+    "# !pip install -U git+https://github.com/huggingface/peft.git\n",
+    "# !pip install -U datasets accelerate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T01:41:01.400102Z",
+     "iopub.status.busy": "2023-07-18T01:41:01.399691Z",
+     "iopub.status.idle": "2023-07-18T01:41:10.921838Z",
+     "shell.execute_reply": "2023-07-18T01:41:10.920427Z",
+     "shell.execute_reply.started": "2023-07-18T01:41:01.400069Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#!find / -name bitsandbytes\n",
+    "\n",
+    "#!find / -name libbitsandbytes_cuda116.so\n",
+    "\n",
+    "#!cp /notebooks/bitsandbytes/bitsandbytes/libbitsandbytes_cuda116.so /usr/lib/python3.9/\n",
+    "\n",
+    "#!ls /usr/lib/python3.9/\n",
+    "\n",
+    "#!python -m bitsandbytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "execution": {
+     "iopub.execute_input": "2023-07-18T11:55:56.450496Z",
+     "iopub.status.busy": "2023-07-18T11:55:56.449938Z",
+     "iopub.status.idle": "2023-07-18T11:56:04.138278Z",
+     "shell.execute_reply": "2023-07-18T11:56:04.137468Z",
+     "shell.execute_reply.started": "2023-07-18T11:55:56.450472Z"
+    },
+    "id": "fhmLLJD0lM5S",
+    "outputId": "d1ef4a8d-156c-4e0c-b92c-2d499e8ad4ed"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# To choose a specific GPU:\n",
+    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import bitsandbytes as bnb\n",
+    "from datasets import load_dataset\n",
+    "import transformers\n",
+    "from transformers import AutoTokenizer, AutoConfig, LlamaForCausalLM, LlamaTokenizer, AutoModelForCausalLM\n",
+    "from peft import prepare_model_for_kbit_training, prepare_model_for_int8_training, LoraConfig, get_peft_model\n",
+    "from peft.peft_model import PeftModel\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T11:56:06.901323Z",
+     "iopub.status.busy": "2023-07-18T11:56:06.900588Z",
+     "iopub.status.idle": "2023-07-18T11:56:06.904953Z",
+     "shell.execute_reply": "2023-07-18T11:56:06.904367Z",
+     "shell.execute_reply.started": "2023-07-18T11:56:06.901289Z"
+    },
+    "id": "XnTp0gOUlOCU"
+   },
+   "outputs": [],
+   "source": [
+    "MICRO_BATCH_SIZE = 6  # this could actually be 5 but i like powers of 2\n",
+    "BATCH_SIZE = 128\n",
+    "GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE\n",
+    "EPOCHS = 2\n",
+    "LEARNING_RATE = 3e-4  # the Karpathy constant\n",
+    "CUTOFF_LEN = 256\n",
+    "LORA_R = 8\n",
+    "LORA_ALPHA = 16\n",
+    "LORA_DROPOUT = 0.05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 49,
+     "referenced_widgets": [
+      "f3fb12d97dee43aaa51784182caed544",
+      "03717756681149898e8c3d40cbc16d10",
+      "d718c5abc21b4394a4314fdc289d830d",
+      "ee8532b0710541abb5f208e654b6d55a",
+      "af52a9b6412e46c6b82acb9afabd12d9",
+      "109d32c6d765417daf49b425b7ccee68",
+      "56217a5954624e6f8742ba914183cb9e",
+      "42a0b19d55b24e4ea5ce7894e4b8df50",
+      "4348c4efdd4d4deb945bb2838c24cd83",
+      "8f860f6f760e49b889fc450c53e49ac5",
+      "43c5c35af8f44f1f98def57ea60a9615"
+     ]
+    },
+    "execution": {
+     "iopub.execute_input": "2023-07-18T11:56:08.096435Z",
+     "iopub.status.busy": "2023-07-18T11:56:08.095783Z",
+     "iopub.status.idle": "2023-07-18T12:05:29.403294Z",
+     "shell.execute_reply": "2023-07-18T12:05:29.402714Z",
+     "shell.execute_reply.started": "2023-07-18T11:56:08.096410Z"
+    },
+    "id": "vdQfvhHo0afo",
+    "outputId": "71768c73-92d7-4430-cd75-3b262d86fc9b"
+   },
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import snapshot_download\n",
+    "\n",
+    "model = '''openlm-research/open_llama_3b_v2'''\n",
+    "\"\"\"VMware/open-llama-13b-open-instruct\"\"\"\n",
+    "use_fast_tokenizer=False\n",
+    "# snapshot_download(repo_id=model)\n",
+    "\n",
+    "# LlamaTokenizer, is faster, if model is Llama\n",
+    "# tokenizer = LlamaTokenizer.from_pretrained(model, use_fast=use_fast_tokenizer)\n",
+    "# For other models:\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model, use_fast=use_fast_tokenizer)\n",
+    "\n",
+    "# model = LlamaForCausalLM.from_pretrained(model, load_in_8bit=True, low_cpu_mem_usage=True, device_map='auto', torch_dtype=torch.float16)\n",
+    "# For other models:\n",
+    "model = AutoModelForCausalLM.from_pretrained(model, load_in_8bit=True, low_cpu_mem_usage=True, device_map='auto', torch_dtype=torch.float16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T12:05:29.404911Z",
+     "iopub.status.busy": "2023-07-18T12:05:29.404341Z",
+     "iopub.status.idle": "2023-07-18T12:05:43.916286Z",
+     "shell.execute_reply": "2023-07-18T12:05:43.915849Z",
+     "shell.execute_reply.started": "2023-07-18T12:05:29.404890Z"
+    },
+    "id": "Xkb9pQTflS-b"
+   },
+   "outputs": [],
+   "source": [
+    "model = prepare_model_for_int8_training(model)\n",
+    "\n",
+    "config = LoraConfig(\n",
+    "    r=LORA_R,\n",
+    "    lora_alpha=LORA_ALPHA,\n",
+    "    target_modules=[\"q_proj\", \"v_proj\"],\n",
+    "    lora_dropout=LORA_DROPOUT,\n",
+    "    bias=\"none\",\n",
+    "    task_type=\"CAUSAL_LM\",\n",
+    ")\n",
+    "model = get_peft_model(model, config)\n",
+    "# model = PeftModel.from_pretrained(model, \"open-llama-3bv2-lora-cabra-adapter-120steps\", config=config)\n",
+    "tokenizer.pad_token_id = 0  # unk. we want this to be different from the eos token\n",
+    "data = load_dataset(\"json\", data_files=\"https://huggingface.co/datasets/Gustrd/dolly-15k-libretranslate-pt/resolve/main/dolly-15k-libretranslate-pt.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T12:05:43.917470Z",
+     "iopub.status.busy": "2023-07-18T12:05:43.916924Z",
+     "iopub.status.idle": "2023-07-18T12:05:43.929680Z",
+     "shell.execute_reply": "2023-07-18T12:05:43.929220Z",
+     "shell.execute_reply.started": "2023-07-18T12:05:43.917450Z"
+    },
+    "id": "ad0PFPPmFRMv"
+   },
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "# Create a slice of the dataset to handle time constraints\n",
+    "# Calculate the number of rows to select for 1/2 of the data\n",
+    "dataSliceNumber = 1\n",
+    "num_rows = math.ceil(len(data['train']) // (1/dataSliceNumber))\n",
+    "data['train'] = data['train'].shuffle().select(range(num_rows))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T12:05:43.931063Z",
+     "iopub.status.busy": "2023-07-18T12:05:43.930761Z",
+     "iopub.status.idle": "2023-07-18T12:05:43.935033Z",
+     "shell.execute_reply": "2023-07-18T12:05:43.934613Z",
+     "shell.execute_reply.started": "2023-07-18T12:05:43.931044Z"
+    },
+    "id": "_VCfL3BhlV_x"
+   },
+   "outputs": [],
+   "source": [
+    "def generate_prompt(data_point):\n",
+    "    # desculpe o desastre de formatação, preciso ser rápido\n",
+    "    if data_point[\"context\"]:\n",
+    "        return f\"\"\"Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.\n",
+    "### Instrução:\n",
+    "{data_point[\"instruction\"]}\n",
+    "### Entrada:\n",
+    "{data_point[\"context\"]}\n",
+    "### Resposta:\n",
+    "{data_point[\"response\"]}\"\"\"\n",
+    "    else:\n",
+    "        return f\"\"\"Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido.\n",
+    "### Instrução:\n",
+    "{data_point[\"instruction\"]}\n",
+    "### Resposta:\n",
+    "{data_point[\"response\"]}\"\"\"\n",
+    "\n",
+    "def tokenize(prompt):\n",
+    "    # there's probably a way to do this with the tokenizer settings\n",
+    "    # but again, gotta move fast\n",
+    "    result = tokenizer(\n",
+    "        prompt,\n",
+    "        truncation=True,\n",
+    "        max_length=CUTOFF_LEN + 1,\n",
+    "        padding=\"max_length\",\n",
+    "    )\n",
+    "    return {\n",
+    "        \"input_ids\": result[\"input_ids\"][:-1],\n",
+    "        \"attention_mask\": result[\"attention_mask\"][:-1],\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T12:05:43.936195Z",
+     "iopub.status.busy": "2023-07-18T12:05:43.935598Z",
+     "iopub.status.idle": "2023-07-18T12:06:03.137794Z",
+     "shell.execute_reply": "2023-07-18T12:06:03.136986Z",
+     "shell.execute_reply.started": "2023-07-18T12:05:43.936177Z"
+    },
+    "id": "81oSm3GL9z72"
+   },
+   "outputs": [],
+   "source": [
+    "data = data.shuffle().map(lambda x: tokenize(generate_prompt(x)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T12:06:03.139179Z",
+     "iopub.status.busy": "2023-07-18T12:06:03.138947Z",
+     "iopub.status.idle": "2023-07-18T12:06:03.292197Z",
+     "shell.execute_reply": "2023-07-18T12:06:03.291371Z",
+     "shell.execute_reply.started": "2023-07-18T12:06:03.139159Z"
+    },
+    "id": "INGJJZ6dkpJu"
+   },
+   "outputs": [],
+   "source": [
+    "trainer = transformers.Trainer(\n",
+    "    model=model,\n",
+    "    train_dataset=data[\"train\"],\n",
+    "    args=transformers.TrainingArguments(\n",
+    "        per_device_train_batch_size=MICRO_BATCH_SIZE,\n",
+    "        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
+    "        warmup_steps=100,\n",
+    "        num_train_epochs=EPOCHS,\n",
+    "        learning_rate=LEARNING_RATE,\n",
+    "        fp16=True,\n",
+    "        logging_steps=20,\n",
+    "        output_dir=\"lora-cabra-3Bv2\",\n",
+    "        save_total_limit=4, \n",
+    "        save_steps=20\n",
+    "    ),\n",
+    "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
+    ")\n",
+    "model.config.use_cache = False\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T12:06:03.293628Z",
+     "iopub.status.busy": "2023-07-18T12:06:03.293172Z",
+     "iopub.status.idle": "2023-07-18T19:30:15.565676Z",
+     "shell.execute_reply": "2023-07-18T19:30:15.564948Z",
+     "shell.execute_reply.started": "2023-07-18T12:06:03.293609Z"
+    },
+    "id": "XrdM-8F8_59v"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "trainer.train(resume_from_checkpoint=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T19:30:15.567903Z",
+     "iopub.status.busy": "2023-07-18T19:30:15.567445Z",
+     "iopub.status.idle": "2023-07-18T19:30:15.617103Z",
+     "shell.execute_reply": "2023-07-18T19:30:15.616512Z",
+     "shell.execute_reply.started": "2023-07-18T19:30:15.567879Z"
+    },
+    "id": "3JY4QEi7lXyY"
+   },
+   "outputs": [],
+   "source": [
+    "model.save_pretrained(\"open-llama-3bv2-lora-cabra-adapter-140steps\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-18T19:45:30.280575Z",
+     "iopub.status.busy": "2023-07-18T19:45:30.280294Z",
+     "iopub.status.idle": "2023-07-18T19:45:31.880370Z",
+     "shell.execute_reply": "2023-07-18T19:45:31.879581Z",
+     "shell.execute_reply.started": "2023-07-18T19:45:30.280556Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# !tar -czvf open-llama-13b-lora-cabra-adapter.tar.gz ./open-llama-13b-lora-cabra-adapter"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuClass": "premium",
+   "gpuType": "T4",
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "gpuClass": "premium",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "03717756681149898e8c3d40cbc16d10": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_109d32c6d765417daf49b425b7ccee68",
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