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Lambent/
CosmoAlpacaLisa-1b

Name: CosmoAlpacaLisa-1b
Availability: InStock

generated_from_trainer

HuggingFaceTB/cosmo-1b

finetune

autotrain_compatible

text-generation-inference

endpoints_compatible

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Trying out some LISA training. A few too many numbers changed to be quite directly comparable, but here's the nous-eval comparisons with the CosmoAlpacaLight using LORA:

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

See axolotl config

axolotl version: 0.4.0

yaml

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: 8
lisa_step_interval: 10
lisa_layers_attribute: model.layers

wandb_project: CosmoAlpacaLisa-1b-v0.1
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: 5e-5

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: 1.0634

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: 5e-05
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
1.0796	0.25	166	1.0695
1.0272	0.5	332	1.0644
1.0471	0.75	498	1.0634

Framework versions

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

Lambent/CosmoAlpacaLisa-1b

lisa-out

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Lambent/
CosmoAlpacaLisa-1b