phi2-bunny

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>

<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

base_model: microsoft/phi-2
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: false
# trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: WhiteRabbitNeo/WRN-Chapter-1
    type:
      system_prompt: ""
      field_system: system
      field_instruction: instruction
      field_output: response
    prompt_style: chatml
  - path: WhiteRabbitNeo/WRN-Chapter-2
    type:
      system_prompt: ""
      field_system: system
      field_instruction: instruction
      field_output: response
    prompt_style: chatml

dataset_prepared_path: ./phi2-bunny/last-run-prepared
val_set_size: 0.05
output_dir: ./phi2-bunny/

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
  - embed_tokens
  - lm_head


hub_model_id: justinj92/phi2-bunny

wandb_project: phi2-bunny
wandb_entity: justinjoy-5
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 5
optimizer: paged_adamw_8bit
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 0.00001
max_grad_norm: 1000.0
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: true
bf16: true
fp16: false
tf32: true

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
chat_template: chatml

warmup_steps: 100
evals_per_epoch: 4
save_steps: 0.01
save_total_limit: 2
debug:
deepspeed:
weight_decay: 0.01
fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true
special_tokens:
  eos_token: "<|im_end|>"
  pad_token: "<|endoftext|>"
tokens:
  - "<|im_start|>"

</details><br>

Hardware

Azure 1xNC_H100 VM - 8 Hours Training Time

phi2-bunny

This model is a fine-tuned version of microsoft/phi-2 on the WhiteRabbit Cybersecurity dataset. It achieves the following results on the evaluation set:

• Loss: 0.5347

Model description

Phi-2 SLM

Intended uses & limitations

Research & Learning

ChatML Prompt

<|im_start|>system 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|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-05
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 5

Training results

Framework versions

• PEFT 0.8.1.dev0
• Transformers 4.37.0
• Pytorch 2.1.2+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0