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--- |
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license: other |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: google/gemma-7b |
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model-index: |
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- name: gemma-python |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<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) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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# use google/gemma-7b if you have access |
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base_model: google/gemma-7b |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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load_in_8bit: false |
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load_in_4bit: true |
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strict: false |
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# huggingface repo |
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datasets: |
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- path: ./dataset/data1.jsonl |
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type: input_output |
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val_set_size: 0.1 |
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output_dir: ./gemma-python |
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adapter: qlora |
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lora_r: 32 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_target_linear: true |
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sequence_len: 4096 |
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sample_packing: false |
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pad_to_sequence_len: true |
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wandb_project: |
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wandb_entity: |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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gradient_accumulation_steps: 3 |
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micro_batch_size: 2 |
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num_epochs: 10 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.0002 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_ratio: 0.1 |
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evals_per_epoch: 4 |
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eval_table_size: |
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eval_max_new_tokens: 128 |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: deepspeed_configs/zero1.json |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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``` |
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</details><br> |
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# gemma-python |
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This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1143 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 3 |
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- total_train_batch_size: 24 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 2 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 19.0016 | 0.12 | 1 | 18.6992 | |
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| 19.4686 | 0.25 | 2 | 16.2578 | |
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| 11.468 | 0.5 | 4 | 8.2891 | |
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| 7.5305 | 0.75 | 6 | 5.8847 | |
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| 5.7572 | 1.0 | 8 | 4.3635 | |
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| 4.3903 | 1.25 | 10 | 3.2849 | |
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| 2.9497 | 1.5 | 12 | 2.8539 | |
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| 2.8738 | 1.75 | 14 | 2.6203 | |
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| 2.7298 | 2.0 | 16 | 2.4534 | |
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| 2.4284 | 2.25 | 18 | 2.3077 | |
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| 2.394 | 2.5 | 20 | 2.1876 | |
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| 2.069 | 2.75 | 22 | 2.1294 | |
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| 1.9355 | 3.0 | 24 | 2.1048 | |
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| 1.9635 | 3.25 | 26 | 2.0707 | |
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| 2.092 | 3.5 | 28 | 2.0596 | |
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| 1.9675 | 3.75 | 30 | 2.0287 | |
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| 1.9693 | 4.0 | 32 | 2.0220 | |
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| 2.0198 | 4.25 | 34 | 2.0124 | |
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| 1.9357 | 4.5 | 36 | 1.9946 | |
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| 1.8147 | 4.75 | 38 | 1.9979 | |
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| 1.9084 | 5.0 | 40 | 1.9751 | |
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| 1.6678 | 5.25 | 42 | 2.0049 | |
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| 1.7639 | 5.5 | 44 | 1.9885 | |
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| 1.7475 | 5.75 | 46 | 1.9777 | |
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| 1.4848 | 6.0 | 48 | 1.9939 | |
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| 1.3065 | 6.25 | 50 | 2.0264 | |
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| 1.4792 | 6.5 | 52 | 2.0125 | |
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| 1.4233 | 6.75 | 54 | 2.0204 | |
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| 1.2534 | 7.0 | 56 | 2.0318 | |
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| 1.2409 | 7.25 | 58 | 2.0445 | |
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| 1.4309 | 7.5 | 60 | 2.0641 | |
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| 1.1622 | 7.75 | 62 | 2.0633 | |
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| 1.228 | 8.0 | 64 | 2.0930 | |
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| 1.3076 | 8.25 | 66 | 2.1077 | |
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| 1.2323 | 8.5 | 68 | 2.1060 | |
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| 1.1635 | 8.75 | 70 | 2.1039 | |
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| 1.261 | 9.0 | 72 | 2.1068 | |
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| 1.0122 | 9.25 | 74 | 2.1110 | |
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| 1.218 | 9.5 | 76 | 2.1180 | |
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| 1.1022 | 9.75 | 78 | 2.1226 | |
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| 1.2072 | 10.0 | 80 | 2.1143 | |
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### Framework versions |
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- PEFT 0.9.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.0 |
