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---
license: apache-2.0
---
# OpenHermes-2.5-neural-chat-v3-3-Slerp
This is the model for OpenHermes-2.5-neural-chat-v3-3-Slerp. I used [mergekit](https://github.com/cg123/mergekit) to merge models.
# Prompt Templates
You can use these prompt templates, but I recommend using ChatML.
### ChatML [(OpenHermes-2.5-Mistral-7B)](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B):
```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
```
### [neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3):
```
### System:
{system}
### User:
{user}
### Assistant:
```
# Yaml Config to reproduce
```yaml
slices:
- sources:
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [0, 32]
- model: Intel/neural-chat-7b-v3-3
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-v0.1
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
dtype: bfloat16
```
# Quantizationed versions
Quantizationed versions of this model is available thanks to [TheBloke](https://hf.co/TheBloke).
##### GPTQ
- [TheBloke/OpenHermes-2.5-neural-chat-v3-3-Slerp-GPTQ](https://huggingface.co/TheBloke/OpenHermes-2.5-neural-chat-v3-3-Slerp-GPTQ)
##### GGUF
- [TheBloke/OpenHermes-2.5-neural-chat-v3-3-Slerp-GGUF](https://huggingface.co/TheBloke/OpenHermes-2.5-neural-chat-v3-3-Slerp-GGUF)
##### AWQ
- [TheBloke/OpenHermes-2.5-neural-chat-v3-3-Slerp-AWQ](https://huggingface.co/TheBloke/OpenHermes-2.5-neural-chat-v3-3-Slerp-AWQ)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-3-Slerp)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 71.38 |
| ARC (25-shot) | 68.09 |
| HellaSwag (10-shot) | 86.2 |
| MMLU (5-shot) | 64.26 |
| TruthfulQA (0-shot) | 62.78 |
| Winogrande (5-shot) | 79.16 |
| GSM8K (5-shot) | 67.78 |
|