| | --- |
| | license: bigcode-openrail-m |
| | library_name: peft |
| | tags: |
| | - generated_from_trainer |
| | base_model: aurora-m/aurora-m-v0.1 |
| | model-index: |
| | - name: lora-out |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
| | <details><summary>See axolotl config</summary> |
| |
|
| | axolotl version: `0.4.0` |
| | ```yaml |
| | base_model: aurora-m/aurora-m-v0.1 # this can be swapped for mdel model when the model is released |
| | model_type: AutoModelForCausalLM |
| | tokenizer_type: AutoTokenizer |
| | is_llama_derived_model: false |
| | |
| | load_in_8bit: false # when this is true inference quality is terrible |
| | load_in_4bit: false |
| | strict: false |
| | |
| | datasets: |
| | - path: tatsu-lab/alpaca # change this to where your dataset is |
| | type: alpaca # change this to 'alpaca' if you are using alpaca formatting |
| | |
| | lora_modules_to_save: |
| | - embed_tokens |
| | - lm_head |
| | |
| | dataset_prepared_path: |
| | val_set_size: 0.05 |
| | output_dir: ./lora-out |
| | |
| | sequence_len: 4096 # this can be tweaked for efficiency |
| | sample_packing: true |
| | pad_to_sequence_len: true |
| | |
| | adapter: lora |
| | lora_model_dir: |
| | lora_r: 32 |
| | lora_alpha: 16 |
| | lora_dropout: 0.05 |
| | lora_target_linear: true |
| | lora_fan_in_fan_out: |
| | |
| | wandb_project: aurora-instruct-alpaca # give this a name |
| | wandb_entity: |
| | wandb_watch: |
| | wandb_name: |
| | wandb_log_model: |
| | |
| | gradient_accumulation_steps: 2 # this can be tweaked for efficiency |
| | micro_batch_size: 1 # this can be tweaked for efficiency |
| | num_epochs: 1 # this can be experimented with |
| | optimizer: adamw_bnb_8bit |
| | lr_scheduler: cosine |
| | learning_rate: 0.0002 |
| | |
| | train_on_inputs: true |
| | group_by_length: false |
| | bf16: true |
| | fp16: false |
| | tf32: false |
| | |
| | gradient_checkpointing: true |
| | early_stopping_patience: |
| | resume_from_checkpoint: |
| | local_rank: |
| | logging_steps: 1 |
| | xformers_attention: |
| | flash_attention: false # when this is true, inference quality is terrible |
| | s2_attention: |
| | |
| | warmup_steps: 10 # this can be tweaked for efficiency |
| | evals_per_epoch: 10 # this can be tweaked for efficiency |
| | eval_table_size: |
| | eval_table_max_new_tokens: 128 |
| | saves_per_epoch: 1 |
| | debug: |
| | deepspeed: |
| | weight_decay: 0.0 |
| | fsdp: |
| | fsdp_config: |
| | special_tokens: |
| | pad_token: "<|endoftext|>" |
| | eos_token: "<|endoftext|>" |
| | |
| | ``` |
| |
|
| | </details><br> |
| |
|
| | # lora-out |
| |
|
| | This model is a fine-tuned version of [aurora-m/aurora-m-v0.1](https://huggingface.co/aurora-m/aurora-m-v0.1) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.9600 |
| |
|
| | ## 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: 1 |
| | - eval_batch_size: 1 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 2 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:| |
| | | 3.9777 | 0.0 | 1 | 3.8904 | |
| | | 1.228 | 0.1 | 73 | 1.1761 | |
| | | 1.2383 | 0.2 | 146 | 1.0635 | |
| | | 0.9985 | 0.3 | 219 | 1.0268 | |
| | | 1.0444 | 0.4 | 292 | 1.0058 | |
| | | 0.9859 | 0.5 | 365 | 0.9904 | |
| | | 0.9736 | 0.6 | 438 | 0.9759 | |
| | | 1.0146 | 0.7 | 511 | 0.9655 | |
| | | 1.0007 | 0.8 | 584 | 0.9610 | |
| | | 0.9943 | 0.9 | 657 | 0.9600 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - PEFT 0.8.2 |
| | - Transformers 4.38.0.dev0 |
| | - Pytorch 2.1.2+cu118 |
| | - Datasets 2.16.1 |
| | - Tokenizers 0.15.0 |
