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--- |
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license: apache-2.0 |
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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: Qwen/Qwen2-7B |
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model-index: |
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- name: outputs/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: `0.4.1` |
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```yaml |
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base_model: Qwen/Qwen2-7B |
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trust_remote_code: true |
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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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datasets: |
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- path: tatsu-lab/alpaca |
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type: alpaca |
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dataset_prepared_path: |
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val_set_size: 0.05 |
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output_dir: ./outputs/out |
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sequence_len: 2048 |
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sample_packing: true |
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eval_sample_packing: true |
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pad_to_sequence_len: true |
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adapter: qlora |
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lora_model_dir: |
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lora_r: 32 |
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lora_alpha: 64 |
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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: 8 |
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micro_batch_size: 1 |
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num_epochs: 4 |
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optimizer: adamw_torch |
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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: true |
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gradient_checkpointing: false |
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gradient_checkpointing_kwargs: |
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use_reentrant: false |
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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: false |
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warmup_steps: 10 |
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evals_per_epoch: 4 |
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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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special_tokens: |
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``` |
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</details><br> |
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# outputs/out |
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This model is a fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.3265 |
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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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- gradient_accumulation_steps: 8 |
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- total_train_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: 10 |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 10.7953 | 0.0031 | 1 | 10.8104 | |
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| 5.4963 | 0.2513 | 80 | 5.4101 | |
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| 5.0323 | 0.5026 | 160 | 5.0758 | |
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| 4.9877 | 0.7538 | 240 | 4.8417 | |
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| 4.7408 | 1.0051 | 320 | 4.6180 | |
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| 4.5097 | 1.2442 | 400 | 4.5066 | |
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| 4.3959 | 1.4955 | 480 | 4.4513 | |
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| 4.2488 | 1.7468 | 560 | 4.4107 | |
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| 4.3507 | 1.9980 | 640 | 4.3784 | |
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| 4.2352 | 2.2352 | 720 | 4.3684 | |
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| 4.2141 | 2.4865 | 800 | 4.3505 | |
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| 4.2739 | 2.7377 | 880 | 4.3375 | |
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| 4.4037 | 2.9890 | 960 | 4.3310 | |
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| 4.195 | 3.2269 | 1040 | 4.3287 | |
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| 4.1996 | 3.4782 | 1120 | 4.3268 | |
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| 4.1353 | 3.7295 | 1200 | 4.3265 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |