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 : [email protected]
- Downloads last month
- 305
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.