| --- |
| license: bigcode-openrail-m |
| library_name: peft |
| tags: |
| - generated_from_trainer |
| base_model: bigcode/starcoder |
| 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: bigcode/starcoder # 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 |
| load_in_4bit: false |
| strict: false |
| |
| datasets: |
| - path: /workspace/axolotl-mdel/mtg.txt # change this to where your dataset is |
| type: completion # change this to 'alpaca' if you are using alpaca |
| |
| 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: mtg-starcoder-experiement-cleaner # 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 #true |
| 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|>" # I need to talk with Huu/Taishi about this |
| eos_token: "<|endoftext|>" |
| |
| ``` |
|
|
| </details><br> |
|
|
| # lora-out |
|
|
| This model is a fine-tuned version of [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7371 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:| |
| | 4.0386 | 0.0 | 1 | 3.7331 | |
| | 1.8941 | 0.1 | 25 | 1.6178 | |
| | 1.0615 | 0.21 | 50 | 0.9739 | |
| | 0.9228 | 0.31 | 75 | 0.8470 | |
| | 0.8614 | 0.41 | 100 | 0.8104 | |
| | 0.8562 | 0.52 | 125 | 0.7776 | |
| | 0.7939 | 0.62 | 150 | 0.7530 | |
| | 0.7714 | 0.73 | 175 | 0.7430 | |
| | 0.7999 | 0.83 | 200 | 0.7389 | |
| | 0.8647 | 0.93 | 225 | 0.7371 | |
|
|
|
|
| ### Framework versions |
|
|
| - PEFT 0.7.1 |
| - Transformers 4.37.0 |
| - Pytorch 2.1.2+cu118 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.0 |
