Lambent/cosmo-upscale-lisa

Tried depth-upscaling Cosmo-1b by duplicating 6 layers, then LISA-training on a dataset reasonably similar to the original one in an attempt to 'self-repair'.

Not sure if it worked out exactly how I pictured but the nous eval's not overall much worse than the original at least.

(Took I think about 8 hours for, I want to say, ~80 million tokens on one RTX 3090?)

Thought about doing LORA first but I couldn't get peft_layers_to_transform working on axolotl and decided to go straight to LISA.

It's probably good(?) for (random selection of layers) to get experience trying to work around (random thick doubled layers) in some kind of brain-exercise sense anyway.

Capabilities assessment vs original and upscaled version:

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
cosmo-upscale-lisa	22.76	49.69	39.49	28.7	35.16

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
cosmo-1b	22.97	52.01	38.02	28.73	35.43

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
cosmo-upscale	22.23	48.35	42.01	28.36	35.24

I'm not sure this helped.

See axolotl config

axolotl version: 0.4.0

base_model: Lambent/cosmo-upscale
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: HuggingFaceTB/cosmopedia-100k
    type: completion
  - path: Vezora/Tested-22k-Python-Alpaca
    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: cosmouplisa
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 Lambent/cosmo-upscale on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0353

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
1.4298	0.0	1	1.4591
1.1229	0.25	1480	1.0594
1.0711	0.5	2960	1.0418
1.0511	0.75	4440	1.0353

Framework versions

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