Upload 2-PKTDC-llama-13B-gptq-lora-24gb.yml

trainer config/2-PKTDC-llama-13B-gptq-lora-24gb.yml

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+# accelerate launch ./scripts/finetune.py 2-PKTDC-llama-13B-gptq-lora-24gb.yml
+#
+# base model settings (local or huggingface repo)
+base_model: PocketDoc/llama-13b-gptq-4bit-128g
+base_model_config: PocketDoc/llama-13b-gptq-4bit-128g
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+trust_remote_code:
+
+# wandb configuration
+wandb_project: llama-13b-gptq-4bit-128g-lora
+wandb_watch:
+wandb_run_id:
+wandb_log_model: 
+
+# where to save the finished model to
+output_dir: ./llama-13b-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: 30
+micro_batch_size: 6
+eval_batch_size: 6
+num_epochs: 12
+warmup_steps: 10
+learning_rate: 0.000004
+
+logging_steps: 1
+eval_steps: 5
+save_steps: 10
+
+# 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: 0.0001
+
+
+# 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: false
+
+
+# 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: