README.md

---
license: llama2
language:
  - en
tags:
  - mistral
  - merge
library_name: transformers
pipeline_tag: text-generation
mergekit:
  - Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp
  - uukuguy/speechless-mistral-six-in-one-7b
datasets:
  - stingning/ultrachat
  - garage-bAInd/Open-Platypus
  - Open-Orca/OpenOrca
  - TIGER-Lab/MathInstruct
  - OpenAssistant/oasst_top1_2023-08-25
  - teknium/openhermes
  - meta-math/MetaMathQA
  - Open-Orca/SlimOrca

---

<p align="center">
  <img src="https://codeberg.org/aninokuma/DeydooAssistant/raw/branch/main/logo.webp" height="256px" alt="SynthIQ">
</p>

# SynthIQ

This is SynthIQ, rated 92.23/100 by GPT-4 across varied complex prompts. I used [mergekit](https://github.com/cg123/mergekit) to merge models.

Metrics from OpenLLM leaderboard:

| Model                                    | Average | ARC   | HellaSwag | MMLU  | TruthfulQA | Winogrande | GSM8K  |
| ---------------------------------------- | ------- | ----- | --------- | ----- | ---------- | ---------- | ------ |
| Weyaxi/OpenHermes-2.5_neural-chat-v3-3-openchat-5-1210-Slerp | 71.26   | 67.92 | 86.32     | 65.47 | 56.45      | 79.72      | 71.72  |
| sethuiyer/SynthIO-7b                    | 69.37   | 65.87 | 85.82     | 64.75 | 57         | 78.69      | 64.06  |
| uukuguy/speechless-mistral-six-in-one-7b | 60.76   | 62.97 | 84.6      | 63.29 | 57.77      | 77.51      | 18.42  |


# Yaml Config

```yaml

slices:
  - sources:
      - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp
        layer_range: [0, 32]
      - model: uukuguy/speechless-mistral-six-in-one-7b
        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
tokenizer_source: union

dtype: bfloat16

```

<!-- prompt-template start -->
## Prompt template: ChatML

```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

```

<!-- prompt-template end -->

SynthIQ's strengths can be succinctly summarized as follows:

1. **Advanced Natural Language Processing**: SynthIQ excels in understanding and generating natural language, making it highly effective for conversational AI applications.

2. **Strong Commonsense Reasoning**: It demonstrates a solid grasp of everyday scenarios and contexts, essential for practical and real-world applications.

3. **Creative and Engaging Content Generation**: SynthIQ has the capability to produce creative content, useful in fields like marketing, creative writing, and social media engagement.

4. **Adaptive User Interaction**: It can effectively adapt to various user personas, providing personalized experiences and recommendations.

5. **Multitasking Across Languages and Subjects**: SynthIQ is adept at handling tasks across different languages and subjects, showcasing its versatility in global and multifaceted settings.

6. **Analytical and Problem-Solving Skills**: The model shows proficiency in analytical reasoning and problem-solving, applicable in data-driven decision-making and complex scenario analysis.

7. **Cultural and Contextual Awareness**: SynthIQ's awareness of different cultural and social contexts makes it suitable for applications requiring cultural sensitivity.

8. **Empathetic and Human-Like Interactions**: The model can engage in empathetic and human-like dialogues, ideal for applications in mental health support, customer service, and education.


License is LLama2 license as uukuguy/speechless-mistral-six-in-one-7b is llama2 license.
# [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_sethuiyer__SynthIQ-7b)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 69.37   |
| ARC (25-shot)         | 65.87          |
| HellaSwag (10-shot)   | 85.82    |
| MMLU (5-shot)         | 64.75         |
| TruthfulQA (0-shot)   | 57.0   |
| Winogrande (5-shot)   | 78.69   |
| GSM8K (5-shot)        | 64.06        |