| --- |
| 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: `0.3.0` |
| ```yaml |
| base_model: bigcode/starcoder |
| model_type: AutoModelForCausalLM |
| tokenizer_type: AutoTokenizer |
| is_llama_derived_model: false |
| |
| load_in_8bit: true |
| load_in_4bit: false |
| strict: false |
| |
| datasets: |
| - path: /workspace/axolotl-mdel/mathematica.txt |
| type: completion |
| |
| lora_modules_to_save: |
| - embed_tokens |
| - lm_head |
| |
| dataset_prepared_path: |
| val_set_size: 0.05 |
| output_dir: ./lora-out |
| |
| sequence_len: 2048 |
| 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: starcoder-mathematica |
| wandb_entity: |
| wandb_watch: |
| wandb_name: |
| wandb_log_model: |
| |
| gradient_accumulation_steps: 2 |
| micro_batch_size: 1 |
| num_epochs: 1 |
| optimizer: adamw_bnb_8bit |
| lr_scheduler: cosine |
| learning_rate: 0.0002 |
| |
| train_on_inputs: false |
| 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: true |
| s2_attention: |
| |
| warmup_steps: 10 |
| evals_per_epoch: 4 |
| 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: "[PAD]" |
| bos_token: "<s>" |
| eos_token: "</s>" |
| unk_token: "<unk>" |
| |
| ``` |
|
|
| </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: 3.0968 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
|
|
| The following `bitsandbytes` quantization config was used during training: |
| - quant_method: bitsandbytes |
| - load_in_8bit: True |
| - load_in_4bit: False |
| - llm_int8_threshold: 6.0 |
| - llm_int8_skip_modules: None |
| - llm_int8_enable_fp32_cpu_offload: False |
| - llm_int8_has_fp16_weight: False |
| - bnb_4bit_quant_type: fp4 |
| - bnb_4bit_use_double_quant: False |
| - bnb_4bit_compute_dtype: float32 |
| |
| ### 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 | |
| |:-------------:|:-----:|:----:|:---------------:| |
| | 2.8529 | 0.0 | 1 | 3.1576 | |
| | 0.4365 | 0.25 | 127 | 3.1416 | |
| | 2.953 | 0.5 | 254 | 3.1146 | |
| | 0.35 | 0.75 | 381 | 3.0968 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.7.0 |
| - Transformers 4.37.0.dev0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.0 |
