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--- |
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license: mit |
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library_name: transformers |
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tags: |
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- axolotl |
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- generated_from_trainer |
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base_model: microsoft/phi-2 |
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model-index: |
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- name: phi2-bunny |
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results: [] |
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datasets: |
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- WhiteRabbitNeo/WRN-Chapter-1 |
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- WhiteRabbitNeo/WRN-Chapter-2 |
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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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is_llama_derived_model: false |
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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: WhiteRabbitNeo/WRN-Chapter-1 |
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type: |
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system_prompt: "" |
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field_system: system |
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field_instruction: instruction |
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field_output: response |
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prompt_style: chatml |
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- path: WhiteRabbitNeo/WRN-Chapter-2 |
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type: |
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system_prompt: "" |
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field_system: system |
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field_instruction: instruction |
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field_output: response |
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prompt_style: chatml |
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dataset_prepared_path: ./phi2-bunny/last-run-prepared |
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val_set_size: 0.05 |
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output_dir: ./phi2-bunny/ |
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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: lora |
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lora_model_dir: |
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lora_r: 64 |
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lora_alpha: 32 |
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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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lora_modules_to_save: |
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- embed_tokens |
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- lm_head |
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hub_model_id: justinj92/phi2-bunny |
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wandb_project: phi2-bunny |
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wandb_entity: justinjoy-5 |
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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: 2 |
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num_epochs: 5 |
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optimizer: paged_adamw_8bit |
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adam_beta1: 0.9 |
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adam_beta2: 0.999 |
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adam_epsilon: 0.00001 |
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max_grad_norm: 1000.0 |
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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: true |
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bf16: true |
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fp16: false |
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tf32: true |
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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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auto_resume_from_checkpoints: |
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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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chat_template: chatml |
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warmup_steps: 100 |
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evals_per_epoch: 4 |
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save_steps: 0.01 |
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save_total_limit: 2 |
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debug: |
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deepspeed: |
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weight_decay: 0.01 |
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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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eos_token: "<|im_end|>" |
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pad_token: "<|endoftext|>" |
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tokens: |
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- "<|im_start|>" |
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``` |
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</details><br> |
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## Hardware |
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Azure 1xNC_H100 VM - 8 Hours Training Time |
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# phi2-bunny |
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This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the WhiteRabbit Cybersecurity dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5347 |
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## Model description |
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Phi-2 SLM |
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## Intended uses & limitations |
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Research & Learning |
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## ChatML Prompt |
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<|im_start|>system |
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You are Bunny, a helpful AI cyber researcher. Answer the Question in a logical, step-by-step manner that makes the reasoning process clear. Carefully analyze the question to identify the core issue or problem to be solved.<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) 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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.8645 | 0.0 | 1 | 0.7932 | |
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| 0.6246 | 0.25 | 228 | 0.6771 | |
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| 0.6449 | 0.5 | 456 | 0.6186 | |
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| 0.6658 | 0.75 | 684 | 0.6073 | |
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| 0.5419 | 1.0 | 912 | 0.5911 | |
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| 0.5477 | 1.24 | 1140 | 0.5878 | |
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| 0.612 | 1.49 | 1368 | 0.5715 | |
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| 0.6328 | 1.74 | 1596 | 0.5632 | |
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| 0.5082 | 1.99 | 1824 | 0.5534 | |
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| 0.5807 | 2.24 | 2052 | 0.5513 | |
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| 0.4775 | 2.49 | 2280 | 0.5448 | |
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| 0.514 | 2.74 | 2508 | 0.5430 | |
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| 0.4943 | 2.99 | 2736 | 0.5398 | |
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| 0.5012 | 3.22 | 2964 | 0.5396 | |
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| 0.5203 | 3.48 | 3192 | 0.5371 | |
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| 0.5112 | 3.73 | 3420 | 0.5356 | |
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| 0.4978 | 3.98 | 3648 | 0.5351 | |
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| 0.5642 | 4.22 | 3876 | 0.5348 | |
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| 0.5383 | 4.47 | 4104 | 0.5348 | |
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| 0.4679 | 4.72 | 4332 | 0.5347 | |
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### Framework versions |
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- PEFT 0.8.1.dev0 |
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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 |
