Beta

Explore
Marketplace
Neural Labs
Chat
Wallet
Docs

Beta

Explore Marketplace Neural Labs Wallet Docs

Build

Manage

My assets Manage wallet Organization

Community

Discord X (Twitter)Telegram

Docs Privacy Policy Terms and Conditions

Proud member of

Proud member of

Docs Cookie Policy

Privacy Policy Terms and Conditions

© 2026 AlphaNeural AI. All rights reserved.

Youliang_-_llama3-8b-instruct-derta-100step-8bits – AI Model by RichardErkhov | AlphaNeural AI

You can deploy this model and start earning money today!

RichardErkhov/
Youliang_-_llama3-8b-instruct-derta-100step-8bits

like

0

Views

No views yet

Quantization made by Richard Erkhov.

Request more models

llama3-8b-instruct-derta-100step - bnb 8bits

Model creator: https://huggingface.co/Youliang/
Original model: https://huggingface.co/Youliang/llama3-8b-instruct-derta-100step/

Original model description:

base_model: meta-llama/Meta-Llama-3-8B-Instruct tags:

generated_from_trainer model-index:
name: Meta-Llama-3-8B_derta results: [] license: apache-2.0

Meta-Llama-3-8B-Instruct_derta_100step

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the Evol-Instruct and BeaverTails dataset. The model is continued to train 100 steps with DeRTa on LLaMA3-8B-Instruct.

Model description

Please refer to the paper Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training and GitHub DeRTa.

Input format:

[INST] Your Instruction [\INST]

Intended uses & limitations

The model is trained with DeRTa, showing a high safety performance.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
weight_decay: 2e-5
eval_batch_size: 1
seed: 1
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 128
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 2.0

Training results

Framework versions

Transformers 4.40.0
Pytorch 2.2.0+cu118
Datasets 2.10.0
Tokenizers 0.19.1