| # Axolotl | |
| #### Go ahead and axolotl questions | |
| ## Support Matrix | |
| | | fp16/fp32 | fp16/fp32 w/ lora | 4bit-quant | 4bit-quant w/flash attention | flash attention | xformers attention | | |
| |----------|:----------|:------------------|------------|------------------------------|-----------------|--------------------| | |
| | llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | |
| | Pythia | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ | | |
| | cerebras | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ | | |
| ## Getting Started | |
| - Point the config you are using to a huggingface hub dataset (see [configs/llama_7B_4bit.yml](https://github.com/winglian/axolotl/blob/main/configs/llama_7B_4bit.yml#L6-L8)) | |
| ```yaml | |
| datasets: | |
| - path: vicgalle/alpaca-gpt4 | |
| type: alpaca | |
| ``` | |
| - Optionally Download some datasets, see [data/README.md](data/README.md) | |
| - 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) | |
| - `pip3 install -e .[int4_triton]` | |
| - `pip3 install -e .` | |
| - | |
| - If not using `int4` or `int4_triton`, run `pip install "peft @ git+https://github.com/huggingface/peft.git"` | |
| - Configure accelerate `accelerate config` or update `~/.cache/huggingface/accelerate/default_config.yaml` | |
| ```yaml | |
| compute_environment: LOCAL_MACHINE | |
| distributed_type: MULTI_GPU | |
| downcast_bf16: 'no' | |
| gpu_ids: all | |
| machine_rank: 0 | |
| main_training_function: main | |
| mixed_precision: bf16 | |
| num_machines: 1 | |
| num_processes: 4 | |
| rdzv_backend: static | |
| same_network: true | |
| tpu_env: [] | |
| tpu_use_cluster: false | |
| tpu_use_sudo: false | |
| 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 | |
| ``` | |