87 lines
3.6 KiB
YAML
87 lines
3.6 KiB
YAML
# this is the huggingface model that contains *.pt, *.safetensors, or *.bin files
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# this can also be a relative path to a model on disk
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base_model: decapoda-research/llama-7b-hf-int4
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# you can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
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base_model_ignore_patterns:
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# if the base_model repo on hf hub doesn't include configuration .json files,
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# you can set that here, or leave this empty to default to base_model
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base_model_config: decapoda-research/llama-7b-hf
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# If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
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model_type: AutoModelForCausalLM
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# Corresponding tokenizer for the model AutoTokenizer is a good choice
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tokenizer_type: AutoTokenizer
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# whether you are training a 4-bit quantized model
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load_4bit: true
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# this will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
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load_in_8bit: true
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# a list of one or more datasets to finetune the model with
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datasets:
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# this can be either a hf dataset, or relative path
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- path: vicgalle/alpaca-gpt4
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# The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
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type: alpaca
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# axolotl attempts to save the dataset as an arrow after packing the data together so
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# subsequent training attempts load faster, relative path
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dataset_prepared_path: data/last_run_prepared
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# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc
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val_set_size: 0.04
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# if you want to use lora, leave blank to train all parameters in original model
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adapter: lora
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# if you already have a lora model trained that you want to load, put that here
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lora_model_dir:
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# the maximum length of an input to train with, this should typically be less than 2048
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# as most models have a token/context limit of 2048
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sequence_len: 2048
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# max sequence length to concatenate training samples together up to
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# inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
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max_packed_sequence_len: 1024
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# lora hyperparameters
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lora_r: 8
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules:
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- q_proj
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- v_proj
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# - k_proj
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# - o_proj
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lora_fan_in_fan_out: false
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# wandb configuration if your're using it
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wandb_project:
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wandb_watch:
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wandb_run_id:
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wandb_log_model: checkpoint
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# where to save the finsihed model to
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output_dir: ./completed-model
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# training hyperparameters
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batch_size: 8
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micro_batch_size: 2
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num_epochs: 3
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warmup_steps: 100
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learning_rate: 0.00003
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# whether to mask out or include the human's prompt from the training labels
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train_on_inputs: false
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# don't use this, leads to wonky training (according to someone on the internet)
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group_by_length: false
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# Use CUDA bf16
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bf16: true
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# Use CUDA tf32
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tf32: true
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# does not work with current implementation of 4-bit LoRA
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gradient_checkpointing: false
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# stop training after this many evaluation losses have increased in a row
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# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
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early_stopping_patience: 3
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# specify a scheduler to use with the optimizer. only one_cycle is supported currently
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lr_scheduler:
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# whether to use xformers attention patch https://github.com/facebookresearch/xformers:
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xformers_attention:
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# whether to use flash attention patch https://github.com/HazyResearch/flash-attention:
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flash_attention:
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# resume from a specific checkpoint dir
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resume_from_checkpoint:
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# if resume_from_checkpoint isn't set and you simply want it to start where it left off
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# be careful with this being turned on between different models
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auto_resume_from_checkpoints: false
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# don't mess with this, it's here for accelerate and torchrun
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local_rank:
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