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.ipynb_checkpoints/README-checkpoint.md
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---
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license: mit
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base_model: microsoft/phi-2
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tags:
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- generated_from_trainer
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model-index:
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- name: phi-sft-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: microsoft/phi-2
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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: false
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strict: false
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datasets:
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- path: Intel/orca_dpo_pairs
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type:
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system_prompt: ""
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field_system: system
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field_instruction: question
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field_output: rejected
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field_output: chosen
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dataset_prepared_path:
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val_set_size: 0.05
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output_dir: ./phi-sft-out
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sequence_len: 2048
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sample_packing: true
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pad_to_sequence_len: true
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adapter:
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lora_model_dir:
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lora_r:
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lora_alpha:
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lora_dropout:
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lora_target_linear:
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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: 1
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micro_batch_size: 2
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num_epochs: 2
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optimizer: adamw_torch
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adam_beta2: 0.95
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adam_epsilon: 0.00001
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max_grad_norm: 1.0
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lr_scheduler: cosine
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learning_rate: 0.000003
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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: true
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gradient_checkpointing_kwargs:
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use_reentrant: 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_steps: 100
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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: 0.1
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fsdp:
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fsdp_config:
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resize_token_embeddings_to_32x: true
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special_tokens:
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pad_token: "<|endoftext|>"
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```
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</details><br>
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# phi-sft-out
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This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2999
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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: 3e-06
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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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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 100
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.3053 | 0.0 | 1 | 1.3288 |
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| 1.2314 | 0.25 | 287 | 1.3183 |
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| 1.1664 | 0.5 | 574 | 1.3090 |
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| 1.4349 | 0.75 | 861 | 1.3034 |
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| 1.4875 | 1.0 | 1148 | 1.3012 |
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| 1.3461 | 1.23 | 1435 | 1.3006 |
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| 1.3247 | 1.48 | 1722 | 1.2998 |
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| 1.2906 | 1.73 | 2009 | 1.2999 |
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### Framework versions
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- Transformers 4.37.0
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- Pytorch 2.1.2+cu121
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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