Luminia-13B-v1-QLora

Nekochu/Luminia-13B-v1-QLora

Luminia-13B-v1-QLora is a text generation model from Nekochu. Use it when you need the model to write or continue text. It is set up for peft - PEFT 0.5.0. The card lists the license as mit.

QLoRA Reinforcement Learning (DPO) on Llama-2-13b-chat.

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Public

Updated Nov 8, 2023

Repo size

3.1 GB

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Public

Hugging Face

Repo makeup

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.pt1 GB · 66%

At a glance

Task: Text Generation
Library: peft - PEFT 0.5.0
License: mit
Access: Public
Created: Nov 8, 2023
Updated: Nov 8, 2023
SHA: 0635e686

Try a prompt

Base models

Nekochu/Luminia-13B-v1-Lora

Datasets

Model card

From the Hugging Face model README

Model Details

QLoRA Reinforcement Learning (DPO) on Llama-2-13b-chat.

Model Description

Train by: [Nekochu]
Model type: [Llama]
Finetuned from model Llama2

How to Get Started with the Model

<details> <summary>Output example tested In <i>text-generation-webui</i></summary>

Input	base llama-2-chat	QLoRa
Name 3 applications for artificial intelligence	Sure! Here are three potential applications of artificial intelligence (AI):<br><br>- Healthcare: AI can be used to analyze medical records and identify patterns that could lead to better diagnoses or treatments for diseases. Additionally, AI-powered robots can assist in surgeries and other medical procedures, allowing doctors to perform complex tasks with greater precision and accuracy.<br> - Finance: AI can be used to detect fraud and prevent financial losses by analyzing large amounts of data quickly and accurately. Additionally, AI-powered chatbots can help customers with their financial queries, freeing up human representatives to focus on more complex issues.<br> Education: AI can be used to personalize learning experiences for students, adapting to their individual needs and abilities. Additionally, AI-powered tools can help teachers with grading and providing feedback, reducing their workload and allowing them to focus on more important aspects of	Certainly! In terms of applications, there are countless possibilities, but here are three examples that come to mind: <br><br> 1) Virtual Assistants - These virtual assistants use natural language processing (NLP) capabilities to understand voice commands from users, perform tasks such as scheduling appointments or sending emails, and even make decisions based on user preferences.<br> 2) Predictive Maintenance - Artificial intelligence algorithms can analyze data from sensors in industrial machines and predict when maintenance will be required, reducing downtime and increasing productivity.<br> 3) Fraud Detection - Machine learning algorithms can analyze patterns in customer transactions to identify fraudulent activity, allowing businesses to take action before financial loss occurs.

</details>

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:

Training Details

main branch is 200k step. 3 epoch for a total of 400k step in 10 days

<details> <summary>Click to see details</summary>

Training and evaluation data

Luminia 13B v1 evaluation reward

Training procedure

<details open> <summary>Datasets</summary>

HH-RLHF (en)
Open Assistant (multilingual)
GPT-4 Generated Data (en&zh)

</details>

Training hyperparameters

The following hyperparameters were used during training:

finetuning_type: lora
quantization_bit: 4
stage: dpo
learning_rate: 5e-05
cutoff_len: 4096
num_train_epochs: 3.0
max_samples: 100000
warmup_steps: 0
train_batch_size: 1
distributed_type: single-GPU
num_devices: 1
warmup_steps: 0
rope_scaling: linear
lora_rank: 32
lora_dropout: 0.15
dpo_beta: 0.1
bnb_4bit_compute_dtype: bfloat16

</details>

Trainer

hiyouga/LLaMA-Efficient-Tuning

Task: Text Generation
Library: peft - PEFT 0.5.0
License: mit
Languages: en
Created: Nov 8, 2023
Updated: Nov 8, 2023