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 |
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