---
library_name: peft
base_model: google-t5/t5-base
license: apache-2.0
language:
- en
- ja
- ar
pipeline_tag: text2text-generation
---

# Model Card for Model ID

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## Model Details

### Model Description

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## Uses

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### Direct Use
```python
from peft import PeftModel

model_id = 'google-t5/t5-base'

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    load_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

original_model = AutoModelForSeq2SeqLM.from_pretrained(model_id,quantization_config=bnb_config,device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token

peft_model = PeftModel.from_pretrained(original_model, "bhuvanmdev/t5-base-news-describer")

generation_config = peft_model.generation_config
generation_config.do_sample = True
generation_config.max_new_tokens = 100   # maxium no of token in output will get
generation_config.temperature = 0.1
generation_config.top_p = 0.8
generation_config.num_return_sequences = 1
generation_config.pad_token_id = tokenizer.eos_token_id
generation_config.eos_token_id = tokenizer.eos_token_id
generation_config.use_cache = True

prompt = f"""Title: A big accidient occurs in luxemberg.""".strip()
encoding = tokenizer(prompt, return_tensors="pt").to(device)

with torch.inference_mode():
    outputs = peft_model.generate(
        input_ids=encoding.input_ids,
        attention_mask=encoding.attention_mask,
        generation_config=generation_config,
    )
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

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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

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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

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Results

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#### Summary



## Model Examination [optional]

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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).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Framework versions

- PEFT 0.8.2