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---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- openchat/openchat-3.5-0106
- machinists/Mistral-7B-SQL
model-index:
- name: haLLAwa3
  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: 67.83
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa3
      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: 87.02
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa3
      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: 64.23
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa3
      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: 63.71
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa3
      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: 80.51
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa3
      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: 64.75
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/haLLAwa3
      name: Open LLM Leaderboard
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65843bdfd9ea8286deed2619/Q_Fp9_F1ZJb9J7xuMnCjh.png)

# Hallawa3: The Fusion of Expertise and Precision for 7B Models"
Unveiling 'Hallawa', an AI marvel that embodies the perfect blend of expert knowledge and cutting-edge technology, tailored for 7B models where direct answers are paramount. This AI powerhouse excels in delivering precise responses, ideal for use cases that demand accuracy and immediacy.
Excelling in document understanding and prompts in its size.
With 'Hallawa', you tap into a repository of intelligence that's been acknowledged by over 1400 downloads on the OpenLLM leaderboard, boasting a remarkable score of 71. This model isn't just about quantity but quality, setting new benchmarks in the realm of language models.

Whether you're looking to enhance customer service, drive research, or accelerate decision-making, 'Hallawa' stands as your go-to solution, engineered to exceed expectations in scenarios where only the most accurate and immediate answers will suffice. Join the ranks of those leveraging 'Hallawa' for their most critical applications and witness the transformation it brings to your operations.
haLLAwa3 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)
* [machinists/Mistral-7B-SQL](https://huggingface.co/machinists/Mistral-7B-SQL)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: openchat/openchat-3.5-0106
        layer_range: [0, 32]
      - model: machinists/Mistral-7B-SQL
        layer_range: [0, 32]
merge_method: slerp
base_model: openchat/openchat-3.5-0106
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
dtype: bfloat16
```
# [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_AbacusResearch__haLLAwa3)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |71.34|
|AI2 Reasoning Challenge (25-Shot)|67.83|
|HellaSwag (10-Shot)              |87.02|
|MMLU (5-Shot)                    |64.23|
|TruthfulQA (0-shot)              |63.71|
|Winogrande (5-shot)              |80.51|
|GSM8k (5-shot)                   |64.75|