---
language:
- en
license: llama3.1
tags:
- medit-mesh
base_model:
- meta-llama/Llama-3.1-8B-Instruct
- arcee-ai/Llama-3.1-SuperNova-Lite
pipeline_tag: text-generation
model-index:
- name: Llama-3.1-MedIT-SUN-8B
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 78.37
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.1-MedIT-SUN-8B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 32.0
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.1-MedIT-SUN-8B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 20.02
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.1-MedIT-SUN-8B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 7.83
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.1-MedIT-SUN-8B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 9.64
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.1-MedIT-SUN-8B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 32.4
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.1-MedIT-SUN-8B
      name: Open LLM Leaderboard
---

# Llama-3.1-MedIT-SUN-8B

## Model Description

Llama-3.1-MedIT-SUN-8B is an experimental language model that leverages model merging techniques to combine the capabilities of multiple foundation models. This 8B parameter model is built upon the Llama-3.1-8B-Instruct architecture and represents an exploration in model fusion methodologies.

## Key Features

- **Base Architecture**: Meta's Llama-3.1-8B-Instruct
- **Parameter Count**: 8 billion
- **Development**: Created by MedIT Solutions
- **Merged Components**:
  - arcee-ai/Llama-3.1-SuperNova-Lite
  - meta-llama/Llama-3.1-8B-Instruct

## Technical Details

The model utilizes the proprietary MedIT-mesh technique for model merging, demonstrating an experimental approach to combining language models. This implementation serves as a proof of concept and testing ground for model fusion methodologies.

## Purpose

This model was developed primarily for testing and research purposes, exploring the potential of model merging techniques in language model development. It should be considered an experimental release rather than a production-ready model.

## Usage Notes

As this is a test model, it is recommended for research and experimental purposes only. Users should be aware of its experimental nature when considering it for any applications.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_meditsolutions__Llama-3.1-MedIT-SUN-8B)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |30.04|
|IFEval (0-Shot)    |78.37|
|BBH (3-Shot)       |32.00|
|MATH Lvl 5 (4-Shot)|20.02|
|GPQA (0-shot)      | 7.83|
|MuSR (0-shot)      | 9.64|
|MMLU-PRO (5-shot)  |32.40|