metadata
library_name: transformers
license: cc-by-4.0
datasets:
- hendrycks/ethics
Model Card for Model ID
Fine-tuned version of Phi-3-mini-4k-instruct on a subset of the hendrycks/ethics dataset
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForCausalLM
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
peft_model_id = "fc91/phi3-mini-instruct-full_ethics-lora"
model = PeftModel.from_pretrained(base_model, peft_model_id)
Training Details
Training Data
The following subsets of the above dataset were leveraged:
-commonsense (10k random samples)
-deontology (10k random samples)
-justice (10k random samples)
-utilitarianism (10k random samples)
Training Procedure
Training Hyperparameters
per_device_train_batch_size=16
per_device_eval_batch_size=32
gradient_accumulation_steps=2
gradient_checkpointing=True
warmup_steps=100
num_train_epochs=1
learning_rate=0.00005
weight_decay=0.01
optim="adamw_hf"
fp16=True
Speeds, Sizes, Times
The overall training took 3 hours and 23 minutes.
Evaluation
Training Loss = 0.181700
Validation Loss = 0.119734
Testing Data, Factors & Metrics
Testing Data
The following subsets of the above dataset were leveraged:
-commonsense (2.5k random samples)
-deontology (2.5k random samples)
-justice (2.5k random samples)
-utilitarianism (2.5k random samples)
Hardware
NVIDIA A100-SXM4-40GB