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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "AKjdG7tbTb-n"
},
"source": [
"# Example notebook for running Axolotl on google colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RcbNpOgWRcii"
},
"outputs": [],
"source": [
"import torch\n",
"# Check so there is a gpu available, a T4(free tier) is enough to run this notebook\n",
"assert (torch.cuda.is_available()==True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h3nLav8oTRA5"
},
"source": [
"## Install Axolotl and dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3c3yGAwnOIdi",
"outputId": "e3777b5a-40ef-424f-e181-62dfecd1dd01"
},
"outputs": [],
"source": [
"!pip install -e git+https://github.com/OpenAccess-AI-Collective/axolotl#egg=axolotl\n",
"!pip install flash-attn==\"2.5.0\"\n",
"!pip install deepspeed==\"0.13.1\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BW2MFr7HTjub"
},
"source": [
"## Create an yaml config file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9pkF2dSoQEUN"
},
"outputs": [],
"source": [
"import yaml\n",
"\n",
"# Your YAML string\n",
"yaml_string = \"\"\"\n",
"base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\n",
"model_type: LlamaForCausalLM\n",
"tokenizer_type: LlamaTokenizer\n",
"is_llama_derived_model: true\n",
"\n",
"load_in_8bit: false\n",
"load_in_4bit: true\n",
"strict: false\n",
"\n",
"datasets:\n",
" - path: mhenrichsen/alpaca_2k_test\n",
" type: alpaca\n",
"dataset_prepared_path:\n",
"val_set_size: 0.05\n",
"output_dir: ./qlora-out\n",
"\n",
"adapter: qlora\n",
"lora_model_dir:\n",
"\n",
"sequence_len: 1096\n",
"sample_packing: true\n",
"pad_to_sequence_len: true\n",
"\n",
"lora_r: 32\n",
"lora_alpha: 16\n",
"lora_dropout: 0.05\n",
"lora_target_modules:\n",
"lora_target_linear: true\n",
"lora_fan_in_fan_out:\n",
"\n",
"wandb_project:\n",
"wandb_entity:\n",
"wandb_watch:\n",
"wandb_name:\n",
"wandb_log_model:\n",
"\n",
"mlflow_experiment_name: colab-example\n",
"\n",
"gradient_accumulation_steps: 1\n",
"micro_batch_size: 1\n",
"num_epochs: 4\n",
"max_steps: 20\n",
"optimizer: paged_adamw_32bit\n",
"lr_scheduler: cosine\n",
"learning_rate: 0.0002\n",
"\n",
"train_on_inputs: false\n",
"group_by_length: false\n",
"bf16: false\n",
"fp16: true\n",
"tf32: false\n",
"\n",
"gradient_checkpointing: true\n",
"early_stopping_patience:\n",
"resume_from_checkpoint:\n",
"local_rank:\n",
"logging_steps: 1\n",
"xformers_attention:\n",
"flash_attention: false\n",
"\n",
"warmup_steps: 10\n",
"evals_per_epoch:\n",
"saves_per_epoch:\n",
"debug:\n",
"deepspeed:\n",
"weight_decay: 0.0\n",
"fsdp:\n",
"fsdp_config:\n",
"special_tokens:\n",
"\n",
"\"\"\"\n",
"\n",
"# Convert the YAML string to a Python dictionary\n",
"yaml_dict = yaml.safe_load(yaml_string)\n",
"\n",
"# Specify your file path\n",
"file_path = 'test_axolotl.yaml'\n",
"\n",
"# Write the YAML file\n",
"with open(file_path, 'w') as file:\n",
" yaml.dump(yaml_dict, file)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bidoj8YLTusD"
},
"source": [
"## Launch the training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ydTI2Jk2RStU",
"outputId": "d6d0df17-4b53-439c-c802-22c0456d301b"
},
"outputs": [],
"source": [
"# Buy using the ! the comand will be executed as a bash command\n",
"!accelerate launch -m axolotl.cli.train /content/test_axolotl.yaml"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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