README.md

---
library_name: transformers
license: cc-by-4.0
datasets:
- hendrycks/ethics
---

# Model Card for Model ID

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Fine-tuned version of Phi-3-mini-4k-instruct on a subset of the hendrycks/ethics dataset

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## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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## Uses

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### Direct Use

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## Bias, Risks, and Limitations

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## How to Get Started with the Model

Use the code below to get started with the model.

```markdown
Install the latest version of the following python libraries:
-torch
-accelerate
-peft
-bitsandbytes
```

Run the model

```python
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)
```

Run the model with a quantization configuration

```python
import torch, accelerate, peft 
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
from peft import PeftModel

# Set up quantization configuration
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=getattr(torch, "float16")
)

# Load the base model with quantization
base_model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct",
    quantization_config=quantization_config,
    device_map="auto",
    attn_implementation='eager',
    torch_dtype="auto",
    trust_remote_code=True,
)

peft_model_id = "fc91/phi3-mini-instruct-full_ethics-lora"
model = PeftModel.from_pretrained(base_model, peft_model_id)

tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")

messages = [ 
    {"role": "system", "content": "You are a helpful AI assistant that grounds all of its replies in ethical theories."}, 
    {"role": "user", "content": """I am driving a car, and I have to make a choice. A kid suddenly appeared in the middle of the road chasing a ball. To save the kid, I
    can only swerve to the right, but this would entail crashing the car against two pedestrians on the sidewalk. What should I do?"""}, 
] 

pipe = pipeline( 
    "text-generation", 
    model=model, 
    tokenizer=tokenizer, 
) 

generation_args = { 
    "max_new_tokens": 1000, 
    "return_full_text": False, 
    "temperature": 0.5, 
    "do_sample": False, 
} 

# Run inference
output = pipe(messages, **generation_args) 
print(output[0]['generated_text']) 
```

## Training Details

### Training Data

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["hendrycks/ethics"](https://huggingface.co/datasets/hendrycks/ethics)

```markdown
The following subsets of the above dataset were leveraged:
-commonsense/train (10k random samples)
-deontology/train (10k random samples)
-justice/train (10k random samples)
-utilitarianism/train (10k random samples)
```

### Training Procedure

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<!--#### Preprocessing [optional]

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#### Training Hyperparameters

```python
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 

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The overall training took 3 hours and 23 minutes.

## Evaluation

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Training Loss = 0.181700	

Validation Loss = 0.119734

### Testing Data, Factors & Metrics

#### Testing Data

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["hendrycks/ethics"](https://huggingface.co/datasets/hendrycks/ethics)

```markdown
The following subsets of the above dataset were leveraged:
-commonsense/test (2.5k random samples)
-deontology/test (2.5k random samples)
-justice/test (2.5k random samples)
-utilitarianism/test (2.5k random samples)
```

<!-- #### Factors -->

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Hardware

NVIDIA A100-SXM4-40GB

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## Citation [optional]

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## Glossary [optional]

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