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---
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library_name: peft
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license: apache-2.0
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base_model: Qwen/Qwen2.5-7B-Instruct
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tags:
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- generated_from_trainer
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datasets:
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- aaditya/mimicraw_clinicaltrial_train
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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model-index:
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- name: 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.6.0`
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```yaml
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base_model: Qwen/Qwen2.5-7B-Instruct
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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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: aaditya/mimicraw_clinicaltrial_train
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type: alpaca
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val_set_size: 0.05
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output_dir: ./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: qlora
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lora_r: 256
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lora_alpha: 512
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lora_dropout: 0.05
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lora_target_linear: true
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lora_target_modules:
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- q_proj
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- k_proj
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- v_proj
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- o_proj
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- gate_proj
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- down_proj
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- up_proj
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wandb_project: qwen_mimicrawclinicaltrail
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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: 6
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num_epochs: 3
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optimizer: adamw_torch
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lr_scheduler: cosine
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learning_rate: 2e-6
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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: false
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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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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_steps: 100
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evals_per_epoch: 3
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eval_table_size:
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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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save_total_limit: 4
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```
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</details><br>
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# out
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This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the aaditya/mimicraw_clinicaltrial_train dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6060
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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: 2e-06
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- train_batch_size: 6
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- eval_batch_size: 6
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- seed: 42
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- distributed_type: multi-GPU
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 24
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 100
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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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| 0.8273 | 0.0008 | 1 | 0.8615 |
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| 0.6312 | 0.3335 | 400 | 0.6677 |
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| 0.6221 | 0.6671 | 800 | 0.6416 |
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| 0.1335 | 1.0 | 1200 | 0.6267 |
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| 0.6062 | 1.3327 | 1600 | 0.6176 |
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| 0.5861 | 1.6662 | 2000 | 0.6119 |
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| 0.6194 | 1.9998 | 2400 | 0.6084 |
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| 0.5953 | 2.3319 | 2800 | 0.6068 |
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| 0.6394 | 2.6654 | 3200 | 0.6060 |
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### Framework versions
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- PEFT 0.14.0
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- Transformers 4.48.1
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- Pytorch 2.5.1+cu124
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- Datasets 3.2.0
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- Tokenizers 0.21.0 |
