Arnic/Gemma-2-2b-it-chat-medicare
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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
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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.frompretrained( # Changed here basemodel, quantizationconfig=bnbconfig, devicemap="auto", attnimplementation=attn_implementation )
tokenizer = GemmaTokenizerFast.frompretrained(basemodel, paddingside="right", truncationside="right", trustremotecode=True)
Preprocessing [optional]
dataset = loaddataset(datasetname, 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
trainingargs = TrainingArguments( outputdir=newmodel, perdevicetrainbatchsize=1, perdeviceevalbatchsize=1, gradientaccumulationsteps=2, optim="pagedadamw32bit", numtrainepochs=1, evalstrategy="steps", evalsteps=200, savesteps=500, # Keep savesteps as 500 loggingsteps=1, warmupsteps=10, loggingstrategy="steps", learningrate=2e-4, fp16=True, bf16=False, groupbylength=True, reportto="wandb", loadbestmodelatend=False # Disable loading best model at the end )
Trainer with early stopping callback
trainer = SFTTrainer( model=model, traindataset=dataset["train"], evaldataset=dataset["test"], peftconfig=peftconfig, maxseqlength=512, datasettextfield="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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Glossary [optional]
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