RichardErkhov/Jimmy19991222_-_llama-3-8b-instruct-gapo-v2-rougeL-beta2-he-scale-gamma0.3-lr2.0e-6-gguf

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llama-3-8b-instruct-gapo-v2-rougeL-beta2-he-scale-gamma0.3-lr2.0e-6 - GGUF

• —Model creator: https://huggingface.co/Jimmy19991222/
• —Original model: https://huggingface.co/Jimmy19991222/llama-3-8b-instruct-gapo-v2-rougeL-beta2-he-scale-gamma0.3-lr2.0e-6/

Original model description: --- libraryname: transformers license: llama3 basemodel: meta-llama/Meta-Llama-3-8B-Instruct tags:

• —alignment-handbook
• —generatedfromtrainer datasets:
• —princeton-nlp/llama3-ultrafeedback-armorm model-index:
• —name: llama-3-8b-instruct-gapo-v2-rougeL-beta2-he-scale-gamma0.3-lr2.0e-6 results: [] ---

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llama-3-8b-instruct-gapo-v2-rougeL-beta2-he-scale-gamma0.3-lr2.0e-6

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the princeton-nlp/llama3-ultrafeedback-armorm dataset. It achieves the following results on the evaluation set:

• —Loss: 0.3499
• —Rewards/chosen: -11.9406
• —Rewards/rejected: -16.3570
• —Rewards/accuracies: 0.9004
• —Rewards/margins: 4.4164
• —Logps/rejected: -8.1785
• —Logps/chosen: -5.9703
• —Logits/rejected: -1.3845
• —Logits/chosen: -1.3878

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: 2e-06
• —trainbatchsize: 2
• —evalbatchsize: 4
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 4
• —gradientaccumulationsteps: 16
• —totaltrainbatch_size: 128
• —totalevalbatch_size: 16
• —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 4.44.2
• —Pytorch 2.2.0
• —Datasets 2.21.0
• —Tokenizers 0.19.1