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CosmoAlpacaLisa-0.2-1b

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metadata
license: cc
base_model: HuggingFaceTB/cosmo-1b
tags:
  - generated_from_trainer
model-index:
  - name: lisa-out
    results: []

Trying out some LISA training. This one used the same learning rate as the LORA training and only 4 layers each 10 steps. Honestly these numbers are probably noise, with how close they are.

Model AGIEval GPT4All TruthfulQA Bigbench Average
CosmoAlpacaLisa-0.2-1b 23.81 51.75 39.31 29.04 35.98
Model AGIEval GPT4All TruthfulQA Bigbench Average
CosmoAlpacaLisa-1b 23.89 51.93 39.93 28.68 36.11
Model AGIEval GPT4All TruthfulQA Bigbench Average
CosmoAlpacaLight-1b 24.28 51.31 40.33 29.47 36.35
Model AGIEval GPT4All TruthfulQA Bigbench Average
cosmo-1b 22.97 52.01 38.02 28.73 35.43

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

base_model: HuggingFaceTB/cosmo-1b
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lisa-out

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

lisa_n_layers: 4
lisa_step_interval: 10
lisa_layers_attribute: model.layers

wandb_project: CosmoAlpacaLisa-1b-v0.2
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

lisa-out

This model is a fine-tuned version of HuggingFaceTB/cosmo-1b 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: 0.0002
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_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

Training results

Training Loss Epoch Step Validation Loss
1.2281 0.0 1 1.2636
1.0795 0.25 166 1.0653
1.018 0.5 332 1.0559
1.0363 0.75 498 1.0525

Framework versions

  • Transformers 4.40.0.dev0
  • Pytorch 2.1.2+cu118
  • Datasets 2.18.0
  • Tokenizers 0.15.0