--- 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](https://huggingface.co/Lambent/CosmoAlpacaLisa-0.2-1b)| 23.81| 51.75| 39.31| 29.04| 35.98| | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |-----------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |[CosmoAlpacaLisa-1b](https://huggingface.co/Lambent/CosmoAlpacaLisa-1b)| 23.89| 51.93| 39.93| 28.68| 36.11| | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |-------------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |[CosmoAlpacaLight-1b](https://huggingface.co/Lambent/CosmoAlpacaLight-1b)| 24.28| 51.31| 40.33| 29.47| 36.35| | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |---------------------------------------------------------|------:|------:|---------:|-------:|------:| |[cosmo-1b](https://huggingface.co/HuggingFaceTB/cosmo-1b)| 22.97| 52.01| 38.02| 28.73| 35.43| [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
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: 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](https://huggingface.co/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