quickstart instructions for starting from runpod (#5)

README.md

@@ -24,7 +24,97 @@ datasets:
 - Optionally Download some datasets, see [data/README.md](data/README.md)
 
 
-- Create a new or update the existing YAML config [config/pythia_1_2B_alpaca.yml](config/pythia_1_2B_alpaca.yml)
+- Create a new or update the existing YAML config [config/sample.yml](config/sample.yml)
+
+```yaml
+# this is the huggingface model that contains *.pt, *.safetensors, or *.bin files
+# this can also be a relative path to a model on disk
+base_model: decapoda-research/llama-7b-hf-int4
+# you can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
+base_model_ignore_patterns:
+# if the base_model repo on hf hub doesn't include configuration .json files,
+# you can set that here, or leave this empty to default to base_model
+base_model_config: decapoda-research/llama-7b-hf
+# If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
+model_type: AutoModelForCausalLM
+# Corresponding tokenizer for the model AutoTokenizer is a good choice
+tokenizer_type: AutoTokenizer
+# whether you are training a 4-bit quantized model
+load_4bit: true
+# this will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
+load_in_8bit: true
+# a list of one or more datasets to finetune the model with
+datasets:
+  # this can be either a hf dataset, or relative path
+  - path: vicgalle/alpaca-gpt4
+  # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
+    type: alpaca
+# axolotl attempts to save the dataset as an arrow after packing the data together so
+# subsequent training attempts load faster, relative path
+dataset_prepared_path: data/last_run_prepared
+# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc
+val_set_size: 0.04
+# if you want to use lora, leave blank to train all parameters in original model
+adapter: lora
+# if you already have a lora model trained that you want to load, put that here
+lora_model_dir:
+# the maximum length of an input to train with, this should typically be less than 2048
+# as most models have a token/context limit of 2048
+sequence_len: 2048
+# max sequence length to concatenate training samples together up to
+# inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
+max_packed_sequence_len: 1024
+# lora hyperparameters
+lora_r: 8
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+  - q_proj
+  - v_proj
+#  - k_proj
+#  - o_proj
+lora_fan_in_fan_out: false
+# wandb configuration if your're using it
+wandb_project:
+wandb_watch:
+wandb_run_id:
+wandb_log_model: checkpoint
+# where to save the finsihed model to
+output_dir: ./completed-model
+# training hyperparameters
+batch_size: 8
+micro_batch_size: 2
+num_epochs: 3
+warmup_steps: 100
+learning_rate: 0.00003
+# whether to mask out or include the human's prompt from the training labels
+train_on_inputs: false
+# don't use this, leads to wonky training (according to someone on the internet)
+group_by_length: false
+# Use CUDA bf16
+bf16: true
+# Use CUDA tf32
+tf32: true
+# does not work with current implementation of 4-bit LoRA
+gradient_checkpointing: false
+# stop training after this many evaluation losses have increased in a row
+# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
+early_stopping_patience: 3
+# specify a scheduler to use with the optimizer. only one_cycle is supported currently
+lr_scheduler:
+# whether to use xformers attention patch https://github.com/facebookresearch/xformers:
+xformers_attention:
+# whether to use flash attention patch https://github.com/HazyResearch/flash-attention:
+flash_attention:
+# resume from a specific checkpoint dir
+resume_from_checkpoint:
+# if resume_from_checkpoint isn't set and you simply want it to start where it left off
+# be careful with this being turned on between different models
+auto_resume_from_checkpoints: false
+# don't mess with this, it's here for accelerate and torchrun
+local_rank:
+```
+
 - Install python dependencies with ONE of the following:
 
     - `pip3 install -e .[int4]` (recommended)
@@ -54,3 +144,29 @@ use_cpu: false
 
 - Train! `accelerate launch scripts/finetune.py`, make sure to choose the correct YAML config file
 - Alternatively you can pass in the config file like: `accelerate launch scripts/finetune.py configs/llama_7B_alpaca.yml`~~
+
+
+## How to start training on Runpod in under 10 minutes
+
+- Choose your Docker container wisely.
+- I recommend `huggingface:transformers-pytorch-deepspeed-latest-gpu` see https://hub.docker.com/r/huggingface/transformers-pytorch-deepspeed-latest-gpu/
+- Once you start your runpod, and SSH into it:
+```shell
+source <(curl -s https://raw.githubusercontent.com/winglian/axolotl/main/scripts/setup-runpod.sh)
+```
+
+- Once the setup script completes
+```shell
+accelerate launch scripts/finetune.py configs/quickstart.yml
+```
+
+- Here are some helpful environment variables you'll want to manually set if you open a new shell
+```shell
+export WANDB_MODE=offline
+export WANDB_CACHE_DIR=/workspace/data/wandb-cache
+export HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
+export HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
+export TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
+export NCCL_P2P_DISABLE=1
+```
+