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

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->


# How to Get Started with the Model
To load and use this model with the Transformers library by Hugging Face, follow the steps outlined in the code snippet below. This code demonstrates how to configure the model, load it along with its tokenizer, and perform inference to generate text based on a given prompt.

## Code Format:
```python
from peft import PeftModel, PeftConfig

test_config = PeftConfig.from_pretrained("checkiejan/phi2-marking-test-full")
model_base = AutoModelForCausalLM.from_pretrained(
    test_config.base_model_name_or_path,
    device_map="auto",
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_quant_type="nf4",
    ),
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(test_config.base_model_name_or_path)

# Add/set tokens same tokens to base model before merging, like we did before starting training  
tokenizer.add_tokens(["<|im_start|>", "<PAD>"])
tokenizer.pad_token = "<PAD>"
tokenizer.add_special_tokens(dict(eos_token="<|im_end|>"))
model_base.resize_token_embeddings(
    new_num_tokens=len(tokenizer),
    pad_to_multiple_of=64)   # phi2 default is 64, see configuration_phi.py
model_base.config.eos_token_id = tokenizer.eos_token_id
lora_model = PeftModel.from_pretrained(model_base, "checkiejan/phi2-marking-test-full")

inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=True)
outputs = lora_model.generate(**inputs)
text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(''.join(text))
```
This code snippet sets up the model and tokenizer, configures the necessary parameters, and demonstrates how to generate text based on a given prompt. Ensure to replace "Your prompt here" with your actual input text.
## 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.

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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

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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#### Training Hyperparameters

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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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#### Hardware

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