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--- |
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library_name: transformers |
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datasets: |
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- lavita/ChatDoctor-HealthCareMagic-100k |
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base_model: |
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- google/gemma-2-2b-it |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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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. |
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- **Developed by:** Arash Nicoomanesh |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** google/gemma-2b-it |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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### Training Procedure |
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model = Gemma2ForCausalLM.from_pretrained( # Changed here |
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base_model, |
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quantization_config=bnb_config, |
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device_map="auto", |
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attn_implementation=attn_implementation |
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) |
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tokenizer = GemmaTokenizerFast.from_pretrained(base_model, padding_side="right", |
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truncation_side="right", trust_remote_code=True) |
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#### Preprocessing [optional] |
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dataset = load_dataset(dataset_name, split="all", cache_dir="./cache") |
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dataset = dataset.shuffle(seed=42).select(range(3000)) # Use 3k samples for a better demo |
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# Define a cleaning function to remove unwanted artifacts |
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def clean_text(text): |
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# Remove URLs and any "Chat Doctor" or similar phrases |
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text = re.sub(r'\b(?:www\.[^\s]+|http\S+)', '', text) # Remove URLs |
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text = re.sub(r'\b(?:Chat Doctor(?:.com)?(?:.in)?|www\.(?:google|yahoo)\S*)', '', text) # Remove site names |
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text = re.sub(r'\s+', ' ', text) # Collapse multiple spaces |
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return text.strip() |
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#### Training Hyperparameters |
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training_args = TrainingArguments( |
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output_dir=new_model, |
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per_device_train_batch_size=1, |
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per_device_eval_batch_size=1, |
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gradient_accumulation_steps=2, |
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optim="paged_adamw_32bit", |
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num_train_epochs=1, |
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eval_strategy="steps", |
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eval_steps=200, |
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save_steps=500, # Keep save_steps as 500 |
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logging_steps=1, |
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warmup_steps=10, |
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logging_strategy="steps", |
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learning_rate=2e-4, |
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fp16=True, |
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bf16=False, |
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group_by_length=True, |
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report_to="wandb", |
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load_best_model_at_end=False # Disable loading best model at the end |
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) |
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# Trainer with early stopping callback |
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trainer = SFTTrainer( |
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model=model, |
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train_dataset=dataset["train"], |
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eval_dataset=dataset["test"], |
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peft_config=peft_config, |
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max_seq_length=512, |
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dataset_text_field="text", # Specify the text field in your dataset |
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tokenizer=tokenizer, |
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args=training_args, |
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packing=False, |
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) |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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View run noble-hill-29 at: https://wandb.ai/anicomanesh/Fine-tune%20Gemma-2-2b-it%20on%20Medical%20Dataset/runs/06xd9vvz |
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wandb: ⭐️ View project at: https://wandb.ai/anicomanesh/Fine-tune%20Gemma-2-2b-it%20on%20Medical%20Dat |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
