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Model Description
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
Model Sources [optional]
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Uses
Direct Use
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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
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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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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