---
library_name: transformers
tags:
- ipex
- intel
- gaudi
- alpaca
- PEFT
- optimum-habana
license: apache-2.0
datasets:
- tatsu-lab/alpaca
language:
- en
---

# Model Card for Model ID

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3-8B-Instruct) on [tatsu-lab/alpaca dataset](https://huggingface.co./datasets/tatsu-lab/alpaca).

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

## Model Details

### Model Description

This is a fine-tuned version of the [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3-8B-Instruct) model using Parameter Efficient Fine Tuning (PEFT) with Low Rank Adaptation (LoRA) on the Intel Gaudi 2 AI accelerator. This model can be used for various text generation tasks including chatbots, content creation, and other NLP applications.

- **Developed by:** Migara Amarasinghe
- **Model type:** LLM
- **Language(s) (NLP):** English
- **Finetuned from model:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3-8B-Instruct)


## Uses

### Direct Use

This model can be used for text generation tasks such as:
- Chatbots
- Automated content creation
- Text completion and augmentation

### Out-of-Scope Use

- Use in real-time applications where latency is critical
- Use in highly sensitive domains without thorough evaluation and testing


### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.


## Training Details

### Training Hyperparameters

<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
- Training regime: Mixed precision training using bf16
- Number of epochs: 3
- Learning rate: 1e-4
- Batch size: 16
- Seq length: 512


## Technical Specifications

### Compute Infrastructure

#### Hardware

- Intel Gaudi 2 AI Accelerator
- Intel(R) Xeon(R) Platinum 8380 CPU @ 2.30GHz

#### Software

- Transformers library
- Optimum Habana library


## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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:** Intel Gaudi 2 AI Accelerator
- **Hours used:** < 1 hour