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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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- Language(s) (NLP): [More Information Needed]
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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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Downstream Use [optional]
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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
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]
BibTeX:
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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