Skip to content

The Mistral-Nemo-12b model has been fine-tuned for text generation tasks. This fine-tuning was performed using the Unsloth optimization framework, which significantly accelerates the training process, achieving a 2x faster fine-tuning time compared to conventional methods.

Notifications You must be signed in to change notification settings

SkkJodhpur/Mistral-Nemo-12b-Unsloth-2x-faster-finetuning-by-skk

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

base_model language license tags
unsloth/mistral-nemo-base-2407-bnb-4bit
en
apache-2.0
text-generation-inference
transformers
unsloth
mistral
trl

Mistral-Nemo-12b-Unsloth-2x-Faster-Finetuning

Model Overview:

  • Developed by: skkjodhpur
  • License: Apache-2.0
  • Base Model: unsloth/mistral-nemo-base-2407-bnb-4bit
  • Libraries Used: Unsloth, Huggingface's TRL (Transformers Reinforcement Learning) library
  • Finetuned from model : unsloth/mistral-nemo-base-2407-bnb-4bit

Model Description The Mistral-Nemo-12b model has been fine-tuned for text generation tasks. This fine-tuning was performed using the Unsloth optimization framework, which significantly accelerates the training process, achieving a 2x faster fine-tuning time compared to conventional methods. The model leverages the robust capabilities of Huggingface's TRL library, enhancing its performance in generating high-quality text.

Features Language: English Capabilities: Text generation, transformers-based inference Fine-tuning Details: The fine-tuning process was focused on improving inference speed and maintaining or enhancing the quality of the generated text.

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

About

The Mistral-Nemo-12b model has been fine-tuned for text generation tasks. This fine-tuning was performed using the Unsloth optimization framework, which significantly accelerates the training process, achieving a 2x faster fine-tuning time compared to conventional methods.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages