---
library_name: transformers
tags: []
---

# Model Card for Model ID

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## Model Details
class SFTConfig:
    sft_model_name: str = 'facebook/opt-350m'
    sft_dataset_path: str = 'train.csv'
    sft_model_cache_dir: str = 'cache'
    sft_output_dir: str = '.'
    hf_key: str = ''

    peft_config = LoraConfig(
        r=4, # TODO: play with this number 
        lora_alpha=8, # TODO: play with this number 
        target_modules=['q_proj', 'v_proj', 'k_proj'],
        lora_dropout=0.05,
        bias="none",
        task_type="CAUSAL_LM" # TODO: you need to figure this out. HINT https://github.com/huggingface/peft/blob/3d2bf9a8b261ed2960f26e61246cf0aa624a6115/src/peft/utils/peft_types.py#L67
    )
        
    training_args = TrainingArguments(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=2,
        gradient_checkpointing =False,
        max_grad_norm= 0.3,
        num_train_epochs=2, # TODO: play with this number 
        save_steps= 100,
        learning_rate=0.0001, # TODO: play with this number 
        bf16=True,
        save_total_limit=3,
        logging_steps=10,
        output_dir='./sft_models',
        optim="adamw_torch",
        lr_scheduler_type="cosine",
        warmup_ratio=0.05,
        remove_unused_columns=False,
        report_to="none",
    )
### 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.

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### Model Sources [optional]

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## Uses

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### Direct Use

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### Out-of-Scope Use

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### Recommendations

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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.

## How to Get Started with the Model

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## Training Details

### Training Data

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### Training Procedure

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

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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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

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## Glossary [optional]

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