license, model-index, tags, base_model

license	model-index	tags	base_model
apache-2.0	name results OpenHermes-2.5-neural-chat-v3-3-Slerp task dataset metrics type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 68.09 normalized accuracy task dataset metrics type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 86.2 normalized accuracy task dataset metrics type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 64.26 accuracy task dataset metrics type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 62.78 task dataset metrics type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 79.16 accuracy task dataset metrics type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 67.78 accuracy	merge	teknium/OpenHermes-2.5-Mistral-7B Intel/neural-chat-7b-v3-3

OpenHermes-2.5-neural-chat-v3-3-Slerp

This is the model for OpenHermes-2.5-neural-chat-v3-3-Slerp. I used mergekit to merge models.

Prompt Templates

You can use these prompt templates, but I recommend using ChatML.

ChatML (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:

### System:
{system}
### User:
{user}
### Assistant:

Yaml Config to reproduce

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.

GPTQ

• TheBloke/OpenHermes-2.5-neural-chat-v3-3-Slerp-GPTQ

GGUF

• TheBloke/OpenHermes-2.5-neural-chat-v3-3-Slerp-GGUF

AWQ

• TheBloke/OpenHermes-2.5-neural-chat-v3-3-Slerp-AWQ

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

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

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