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See axolotl config

axolotl version: 0.4.0

base_model: openbmb/MiniCPM-2B-sft-bf16
load_in_8bit: false
load_in_4bit: false
strict: false
push_dataset_to_hub:
datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
adapter:
lora_model_dir:
sequence_len: 4096
max_packed_sequence_len:
lora_r: 8
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./qlora-out
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1.5
optimizer: paged_adamw_8bit
torchdistx_path:
lr_scheduler: cosine
learning_rate: 0.0001
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention: true
flash_attention:
gptq_groupsize:
gptq_model_v1:
warmup_steps: 10
evals_per_epoch: 2
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
trust_remote_code: true

qlora-out

This model is a fine-tuned version of openbmb/MiniCPM-2B-sft-bf16 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0525

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 1.5

Training results

Training Loss Epoch Step Validation Loss
1.0903 0.0 1 1.7199
0.8959 0.5 1620 1.1007
0.995 1.0 3240 1.0342
0.864 1.5 4860 1.0525

Framework versions

  • Transformers 4.38.0.dev0
  • Pytorch 2.1.2+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.0
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