RichardErkhov/argilla-warehouse_-_Llama-3.1-8B-MagPie-Ultra-500k-gguf

Quantization made by Richard Erkhov.

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Llama-3.1-8B-MagPie-Ultra-500k - GGUF

• —Model creator: https://huggingface.co/argilla-warehouse/
• —Original model: https://huggingface.co/argilla-warehouse/Llama-3.1-8B-MagPie-Ultra-500k/

Original model description: --- libraryname: transformers license: llama3.1 basemodel: meta-llama/Llama-3.1-8B tags:

• —alignment-handbook
• —trl
• —sft
• —generatedfromtrainer
• —trl
• —sft
• —generatedfromtrainer datasets:
• —argilla-warehouse/magpie-ultra-v1.0 model-index:
• —name: Llama-3.1-8B-MagPie-Ultra-500k results: [] ---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

Llama-3.1-8B-MagPie-Ultra-500k

This model is a fine-tuned version of meta-llama/Llama-3.1-8B on the argilla-warehouse/magpie-ultra-v1.0 dataset. It achieves the following results on the evaluation set:

• —Loss: 0.5953

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 1e-05
• —trainbatchsize: 4
• —evalbatchsize: 4
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 8
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 128
• —totalevalbatch_size: 32
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —lrschedulerwarmup_ratio: 0.1
• —num_epochs: 3

Training results

Framework versions

• —Transformers 4.45.0
• —Pytorch 2.4.1+cu121
• —Datasets 3.0.1
• —Tokenizers 0.20.0