RichardErkhov/barc0_-_barc-llama3.1-8b-instruct-fft-transduction-gpt4omini10k_lr1e-5_epoch1-gguf

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barc-llama3.1-8b-instruct-fft-transduction-gpt4omini10klr1e-5epoch1 - GGUF

• —Model creator: https://huggingface.co/barc0/
• —Original model: https://huggingface.co/barc0/barc-llama3.1-8b-instruct-fft-transduction-gpt4omini10klr1e-5epoch1/

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

• —alignment-handbook
• —trl
• —sft
• —generatedfromtrainer
• —trl
• —sft
• —generatedfromtrainer datasets:
• —barc0/transduction100kgpt4o-minigeneratedproblemsseed100.jsonlmessagesformat0.3 model-index:
• —name: barc-llama3.1-8b-instruct-fft-transduction-gpt4omini10klr1e-5epoch1 results: [] ---

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barc-llama3.1-8b-instruct-fft-transduction-gpt4omini10klr1e-5epoch1

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the barc0/transduction100kgpt4o-minigeneratedproblemsseed100.jsonlmessagesformat0.3 dataset. It achieves the following results on the evaluation set:

• —Loss: 0.7042

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: 8
• —evalbatchsize: 4
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 8
• —gradientaccumulationsteps: 2
• —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: 1

Training results

Framework versions

• —Transformers 4.45.0.dev0
• —Pytorch 2.4.1+cu121
• —Datasets 3.0.0
• —Tokenizers 0.20.0