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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: meta-llama/Meta-Llama-3-8B |
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model-index: |
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- name: lora-out |
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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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base_model: meta-llama/Meta-Llama-3-8B |
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model_type: LlamaForCausalLM |
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tokenizer_type: AutoTokenizer |
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load_in_8bit: true |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: kloodia/raw_bio |
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type: oasst |
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dataset_prepared_path: |
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val_set_size: 0.05 |
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output_dir: ./lora-out |
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sequence_len: 4096 |
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sample_packing: true |
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pad_to_sequence_len: true |
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adapter: lora |
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lora_model_dir: |
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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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lora_fan_in_fan_out: |
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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: 4 |
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micro_batch_size: 1 |
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num_epochs: 3 |
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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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s2_attention: |
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warmup_steps: 10 |
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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: |
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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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pad_token: <|end_of_text|> |
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``` |
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</details><br> |
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# lora-out |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7317 |
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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: 1 |
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- eval_batch_size: 1 |
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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: 4 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 4 |
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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: 10 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.795 | 0.0 | 1 | 1.8088 | |
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| 1.7519 | 0.25 | 137 | 1.7284 | |
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| 1.7442 | 0.5 | 274 | 1.7260 | |
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| 1.7182 | 0.75 | 411 | 1.7249 | |
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| 1.743 | 1.0 | 548 | 1.7237 | |
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| 1.7075 | 1.24 | 685 | 1.7265 | |
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| 1.7264 | 1.49 | 822 | 1.7267 | |
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| 1.6604 | 1.74 | 959 | 1.7260 | |
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| 1.6562 | 1.99 | 1096 | 1.7255 | |
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| 1.6455 | 2.22 | 1233 | 1.7308 | |
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| 1.6258 | 2.47 | 1370 | 1.7315 | |
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| 1.6792 | 2.72 | 1507 | 1.7317 | |
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| 1.6364 | 2.97 | 1644 | 1.7317 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
