RichardErkhov/ifable_-_gemma-2-Ifable-9B-gguf

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gemma-2-Ifable-9B - GGUF

• —Model creator: https://huggingface.co/ifable/
• —Original model: https://huggingface.co/ifable/gemma-2-Ifable-9B/

Original model description: --- license: gemma library_name: transformers datasets:

• —jondurbin/gutenberg-dpo-v0.1 --- <!-- 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. -->

ifable/gemma-2-Ifable-9B

This model ranked first on the Creative Writing Benchmark (https://eqbench.com/creative_writing.html) on September 10, 2024

Training and evaluation data

• —Gutenberg: https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1
• —Carefully curated proprietary creative writing dataset

Training procedure

Training method: SimPO (GitHub - princeton-nlp/SimPO: SimPO: Simple Preference Optimization with a Reference-Free Reward)

It achieves the following results on the evaluation set:

• —Loss: 1.0163
• —Rewards/chosen: -21.6822
• —Rewards/rejected: -47.8754
• —Rewards/accuracies: 0.9167
• —Rewards/margins: 26.1931
• —Logps/rejected: -4.7875
• —Logps/chosen: -2.1682
• —Logits/rejected: -17.0475
• —Logits/chosen: -12.0041

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 8e-07
• —trainbatchsize: 1
• —evalbatchsize: 1
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 8
• —gradientaccumulationsteps: 16
• —totaltrainbatch_size: 128
• —totalevalbatch_size: 8
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —lrschedulerwarmup_ratio: 0.1
• —num_epochs: 1.0

Training results

Framework versions

• —Transformers 4.43.4
• —Pytorch 2.3.0a0+ebedce2
• —Datasets 2.20.0
• —Tokenizers 0.19.1

We are looking for product manager and operations managers to build applications through our model, and also open for business cooperation, and also AI engineer to join us, contact with : contact@ifable.ai