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base_model: Delta-Vector/Holland-4B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: NewEden/CivitAI-SD-Prompts
# type:
# system_prompt: ""
# system_format: "<|im_start|>system\n{system}<|im_end|>\n"
# field_system: instruction
# field_instruction: input
# field_input: ""
# field_output: output
# no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
# system_prompt: ""
# field_instruction: instruction
# field_input: input
# field_output: output
# format: |-
# <|im_start|>system
# {instruction}<|im_end|>
# <|im_start|>user
# {input}<|im_end|>
# <|im_start|>assistant
# {output}
type: alpaca
conversation: mpt-30b-instruct
# field_system: instruction
# field_instruction: input
# field_input: input
# field_output: output
chat_template: alpaca
dataset_prepared_path:
val_set_size: 0.02
output_dir: ./outputs/out2
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: SDprompterV2
wandb_entity:
wandb_watch:
wandb_name: SDprompterV2
wandb_log_model:
gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.05
evals_per_epoch: 4
saves_per_epoch: 1
debug:
weight_decay: 0.05
deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
special_tokens:
pad_token: <|finetune_right_pad_id|>
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