Delete 2-PKTDC-llama-30B-gptq-lora-24gb.yml

2-PKTDC-llama-30B-gptq-lora-24gb.yml

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-# accelerate launch ./scripts/finetune.py 2-PKTDC-llama-30B-gptq-lora-24gb.yml
-#
-# base model settings (local or huggingface repo)
-base_model: PocketDoc/llama-30b-gptq-4bit-128g
-base_model_config: PocketDoc/llama-30b-gptq-4bit-128g
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-trust_remote_code:
-
-# wandb configuration
-wandb_project: llama-30b-gptq-4bit-128g-lora
-wandb_watch:
-wandb_run_id:
-wandb_log_model: 
-
-# where to save the finished model to
-output_dir: ./llama-30b-gptq-4bit-128g-lora
-
-# dataset settings (local or huggingface repo)
-datasets:
-  - path: dansmeth.json
-    type: pygmalion
-
-dataset_prepared_path: data/last_run_prepared
-
-# percentage of the dataset to set aside as evaluation.
-val_set_size: 0.02
-
-# max token length / prompt
-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: 2048
-
-# quantized model loading settings
-gptq: true
-gptq_groupsize: 128 # group size
-gptq_model_v1: false # v1 or v2
-strict: false
-
-# this will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
-load_in_8bit: true
-
-load_in_4bit: 
-
-# Use CUDA bf16
-bf16: false
-# Use CUDA fp16
-fp16: true
-# Use CUDA tf32
-tf32: true
-
-# training hyperparameters
-gradient_accumulation_steps: 32
-micro_batch_size: 1
-eval_batch_size: 1
-num_epochs: 3
-warmup_steps: 350
-learning_rate: 0.00003
-
-logging_steps: 1
-eval_steps: 25
-save_steps: 175
-
-# 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: 
-# specify a scheduler to use with the optimizer. only one_cycle is supported currently
-lr_scheduler: linear
-# specify optimizer
-optimizer: paged_adamw_8bit
-# specify weight decay
-weight_decay: 0.05
-
-
-# if you already have a lora model trained that you want to load, put that here
-lora_model_dir: 
-
-# LoRA hyperparameters
-adapter: lora # blank for full finetune
-lora_r: 32
-lora_alpha: 64
-lora_dropout: 0.05
-lora_target_linear: 
-lora_target_modules:
-  - q_proj
-  - v_proj
-#  - k_proj
-#  - o_proj
-#  - gate_proj
-#  - down_proj
-#  - up_proj
-lora_modules_to_save:
-#  - embed_tokens
-#  - lm_head
-lora_out_dir:
-lora_fan_in_fan_out: false
-
-
-# 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: true
-
-
-# does not work with current implementation of 4-bit LoRA
-gradient_checkpointing: true
-
-
-# whether to use xformers attention patch https://github.com/facebookresearch/xformers:
-xformers_attention: true
-# whether to use flash attention patch https://github.com/HazyResearch/flash-attention:
-flash_attention:  # require a100 for llama
-# whether to use scaled-dot-product attention
-# https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
-sdp_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: 
-
-
-# don't mess with this, it's here for accelerate and torchrun
-local_rank:
-
-# add or change special tokens
-special_tokens:
-  # sys_role_token: "<|system|>"
-  # user_role_token: "<|user|>"
-  # model_role_token: "<|model|>"
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-  
-# add extra tokens
-tokens:
-
-
-# FSDP
-fsdp:
-
-fsdp_config:
-
-# Deepspeed
-deepspeed: 
-
-# TODO
-torchdistx_path:
-
-# Debug mode
-debug: