README.md

---
inference: false
base_model:
- SanjiWatsuki/Silicon-Maid-7B
- sethuiyer/Aika-7B
- sethuiyer/Nandine-7b
- mlabonne/AlphaMonarch-7B
library_name: transformers
tags:
- mergekit
- merge
- not-for-all-audiences
license: cc
model-index:
- name: sethuiyer/Diana-7B
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 68.34
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Diana-7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 86.73
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Diana-7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 64.58
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Diana-7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 60.55
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Diana-7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 80.19
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Diana-7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 63.23
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Diana-7B
      name: Open LLM Leaderboard
language:
- en
pipeline_tag: conversational
---
# Diana-7B

<p align="center">
  <img src="https://huggingface.co/sethuiyer/Diana-7B/resolve/main/diana.webp" height="128px" alt="Diana">
</p>

This is Diana-7b, rated **93.56/100** by GPT-4 on a collection of 30 synthetic prompts generated by GPT-4.

It is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):

1. [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B): This model has impressive conversational abilities, formal and sophisticated style, and strong reasoning skills.
2. [sethuiyer/Aika-7b](https://huggingface.co/sethuiyer/Aika-7B): A merge of SanjiWatsuki/Silicon-Maid-7B, Guilherme34/Samantha-v2, jan-hq/stealth-v1.3, and senseable/WestLake-7B-v2, Aika-7b is designed for natural and human-like interactions, accurate information delivery, comprehensive analysis, emotional intelligence, clarity, and structure.
3. [SanjiWatsuki/Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B): This model is known for its excellent multi-turn conversational skills and logical coherence.
4. [sethuiyer/Nandine-7b](https://huggingface.co/sethuiyer/Nandine-7b): A merge of senseable/Westlake-7B, Guilherme34/Samantha-v2, and uukuguy/speechless-mistral-six-in-one-7b, Nandine-7b excels in narrative skill, empathetic interaction, intellectual depth, and eloquent communication.

By combining these models, Diana-7B offers a balanced blend of capabilities, making it suitable for various tasks and providing a comprehensive AI companion for creative writing, thoughtful discussions, problem-solving, and general assistance. 

## OpenLLM Benchmark

| Model                          | Average ⬆️ | ARC    | HellaSwag | MMLU  | TruthfulQA | Winogrande | GSM8K | 
|--------------------------------|------------|-------|-----------|-------|------------|------------|-------| 
| sethuiyer/Diana-7B 📑       | 70.6      | 68.34 | 86.73     | 64.58 | 60.55       | 80.19      | 63.23  | 


## Nous Benchmark
|                          Model                          |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|---------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Diana-7B](https://huggingface.co/sethuiyer/Diana-7B)|  44.38 |  75.1|     60.55|   44.58|  56.09|



### Configuration

The following YAML configuration was used to produce this model:

```yaml

base_model: mlabonne/AlphaMonarch-7B
dtype: bfloat16
merge_method: dare_ties
models:
- model: mlabonne/AlphaMonarch-7B
- model: sethuiyer/Aika-7B
  parameters:
    density: 0.85
    weight: 0.30
- model: SanjiWatsuki/Silicon-Maid-7B
  parameters:
    density: 0.85
    weight: 0.50
- model: sethuiyer/Nandine-7b
  parameters:
    density: 0.85
    weight: 0.30
parameters:
  int8_mask: true

```

## Prompt Template

```text
{bos}user
{ .Prompt }{eos}
{bos}assistant
```

## GGUF
GGUF files are available at [Diana-7B-GGUF](https://huggingface.co/sethuiyer/Diana-7B-GGUF/tree/main)

## Ollama
Diana is now available on Ollama. You can use it by running the command ```ollama run stuehieyr/diana``` in your 
terminal. If you have limited computing resources, check out this [video](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on 
a Google Colab backend.