---
library_name: transformers
datasets:
- lavita/ChatDoctor-HealthCareMagic-100k
base_model:
- google/gemma-2-2b-it
---

# Model Card for Model ID

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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.

- **Developed by:** Arash Nicoomanesh
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** google/gemma-2b-it

### Model Sources [optional]

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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

model = Gemma2ForCausalLM.from_pretrained(  # Changed here
    base_model,
    quantization_config=bnb_config,
    device_map="auto",
    attn_implementation=attn_implementation
)

tokenizer = GemmaTokenizerFast.from_pretrained(base_model, padding_side="right",
    truncation_side="right", trust_remote_code=True)

#### Preprocessing [optional]

dataset = load_dataset(dataset_name, split="all", cache_dir="./cache")
dataset = dataset.shuffle(seed=42).select(range(3000))  # Use 3k samples for a better demo

# Define a cleaning function to remove unwanted artifacts
def clean_text(text):
    # Remove URLs and any "Chat Doctor" or similar phrases
    text = re.sub(r'\b(?:www\.[^\s]+|http\S+)', '', text)                   # Remove URLs
    text = re.sub(r'\b(?:Chat Doctor(?:.com)?(?:.in)?|www\.(?:google|yahoo)\S*)', '', text)  # Remove site names
    text = re.sub(r'\s+', ' ', text)                                        # Collapse multiple spaces
    return text.strip()


#### Training Hyperparameters

training_args = TrainingArguments(
    output_dir=new_model,
    per_device_train_batch_size=1,
    per_device_eval_batch_size=1,
    gradient_accumulation_steps=2,
    optim="paged_adamw_32bit",
    num_train_epochs=1,
    eval_strategy="steps",
    eval_steps=200, 
    save_steps=500,  # Keep save_steps as 500 
    logging_steps=1,
    warmup_steps=10,
    logging_strategy="steps",
    learning_rate=2e-4,
    fp16=True,
    bf16=False,
    group_by_length=True,
    report_to="wandb",
    load_best_model_at_end=False  # Disable loading best model at the end
)


# Trainer with early stopping callback
trainer = SFTTrainer(
    model=model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["test"],
    peft_config=peft_config,
    max_seq_length=512,
    dataset_text_field="text",  # Specify the text field in your dataset
    tokenizer=tokenizer,
    args=training_args,
    packing=False,
)
#### Speeds, Sizes, Times [optional]

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## Evaluation

View run noble-hill-29 at: https://wandb.ai/anicomanesh/Fine-tune%20Gemma-2-2b-it%20on%20Medical%20Dataset/runs/06xd9vvz
wandb: ⭐️ View project at: https://wandb.ai/anicomanesh/Fine-tune%20Gemma-2-2b-it%20on%20Medical%20Dat

### Testing Data, Factors & Metrics

#### Testing Data

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#### 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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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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