Instructions to use ntegrals/NeuralMerge-9B-Ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ntegrals/NeuralMerge-9B-Ties with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ntegrals/NeuralMerge-9B-Ties", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: | |
| - OpenPipe/mistral-ft-optimized-1218 | |
| - mlabonne/NeuralHermes-2.5-Mistral-7B | |
| - mistralai/Mistral-7B-v0.1 | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| # merged | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) | |
| * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| base_model: mistralai/Mistral-7B-v0.1 | |
| dtype: float16 | |
| merge_method: ties | |
| modules: | |
| default: | |
| slices: | |
| - sources: | |
| - layer_range: [0, 32] | |
| model: mistralai/Mistral-7B-v0.1 | |
| - layer_range: [0, 32] | |
| model: OpenPipe/mistral-ft-optimized-1218 | |
| parameters: | |
| density: 0.5 | |
| weight: 0.5 | |
| - layer_range: [0, 32] | |
| model: mlabonne/NeuralHermes-2.5-Mistral-7B | |
| parameters: | |
| density: 0.5 | |
| weight: 0.3 | |
| parameters: | |
| normalize: 1.0 | |
| ``` | |