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--- |
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license: mit |
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base_model: microsoft/Phi-3-mini-4k-instruct |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: phi3-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: microsoft/Phi-3-mini-4k-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: false |
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strict: false |
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datasets: |
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- path: dataset.json |
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ds_type: json |
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type: completion |
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dataset_prepared_path: |
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val_set_size: 0.05 |
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output_dir: ./phi3-out |
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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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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: 1 |
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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: 0.0002 # 0.000003 #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: 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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eos_token: "<|end|>" |
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``` |
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</details><br> |
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# phi3-out |
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This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8809 |
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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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- 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: 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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| 0.4023 | 1.0 | 7628 | 1.4132 | |
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| 0.1342 | 2.0 | 15256 | 1.8809 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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