Gemma-2-2b-it-chat-medicare

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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
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• Finetuned from model [optional]: google/gemma-2b-it

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Uses

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Direct Use

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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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How 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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Results

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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