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Apel-sin/
gemma-2-ifable-9b-exl2

Name: gemma-2-ifable-9b-exl2
Availability: InStock

transformers

jondurbin/gutenberg-dpo-v0.1

gemma

endpoints_compatible

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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
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen	Sft Loss
1.4444	0.9807	35	1.0163	-21.6822	-47.8754	0.9167	26.1931	-4.7875	-2.1682	-17.0475	-12.0041	0.0184

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

Apel-sin/gemma-2-ifable-9b-exl2