
# Model Description
This is an experiment to test merging 14 models using DARE TIES 🦙

We first merge 14 models to produce [EmbeddedLLM/Mistral-7B-Merge-14-v0.3](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.3),
which is then merged again with [Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp) using Gradient SLERP.
The result is a model that performs quite well but may require further instruction fine-tuning.

## Open LLM Leaderboard

| Average    | 71.19 |
|------------|-------|
| ARC        | 66.81 |
| HellaSwag  | 86.15 |
| MMLU       | 65.10 |
| TruthfulQA | 58.25 |
| Winogrande | 80.03 |
| GSM8K      | 70.81  |

## Chat Template

Either ChatML or Llama-2 chat template.

## Merge Configuration

The merge config file for this model is here:

```yaml
slices:
  - sources:
      - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp
        layer_range: [0, 32]
      - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.3
        layer_range: [0, 32]

merge_method: slerp
base_model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp

parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5 # fallback for rest of tensors
tokenizer_source: base
embed_slerp: true

dtype: bfloat16
```
{"language": ["en"], "license": "apache-2.0", "tags": ["merge"], "base_model": ["Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp", "EmbeddedLLM/Mistral-7B-Merge-14-v0.3"]}
transformers safetensors mistral text-generation merge en base_model:Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp base_model:EmbeddedLLM/Mistral-7B-Merge-14-v0.3 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us