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

# Model Card for Meta-Llama3-8B-Instruct-assessment 

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## Model Details

### Model Description

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This is the model card of a Meta-Llama3-8B-Instruct-assessment model that has been developed by fine-tuning Meta-Llama3-8B-Instruct. The model is finetuned using LoRA and the model was loaded in 16bit. Low-Rank Adaptation, also known as LoRA, makes fine-tuning LLMs easier by reducing the number of trainable parameters to produce lightweight and efficient models. LoRA was utilized by modifying matrix rank 'r' and alpha values.

- **Developed by:** xap
- **License:** llama3
- **Finetuned from model :** meta-llama/Meta-Llama-3-8B-Instruct
- **Finetuned using dataset :** [SelfCode2.0](https://zenodo.org/records/10912669)



## How to Get Started with the Model

The dataset or input for this model should be in the alpaca format.

```
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xap/Meta-Llama3-8B-Instruct-assessment")
base_model = AutoModelForCausalLM.from_pretrained("xap/Meta-Llama3-8B-Instruct-assessment")

lora_config = LoraConfig.from_pretrained("xap/Meta-Llama3-8B-Instruct-assessment")
model = PeftModel.from_pretrained(
    base_model,
    "xap/Meta-Llama3-8B-Instruct-assessment",
    lora_config=lora_config,
)