README.md

---
library_name: transformers
tags: []
model-index:
- name: ldm_soup_Llama-3.1-8B-Inst
  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: 80.33
      name: strict accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
      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: 31.1
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
      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: 11.56
      name: exact match
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
      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: 5.26
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
      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: 11.52
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
      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: 32.07
      name: accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
      name: Open LLM Leaderboard
---

# Model Card for DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in
- compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

## Overview


**DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst** is developed by **deepAuto.ai** and builds upon the **VAGOsolutions/Llama-3.1-SauerkrautLM-8B-Instruct** model. Our approach leverages the base model’s pretrained weights and optimizes them for the **Winogrande** and **ARC-Challenge** datasets by training a latent diffusion model on the pretrained weights.

Through this process, we learn the distribution of the base model's weight space, enabling us to explore optimal configurations. We then sample multiple sets of weights, using the **model-soup averaging technique** to identify the best-performing weights for both datasets. These weights are merged using linear interpolation to create the final model weights for **DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst**.

This approach has led to improved performance on previously unseen leaderboard tasks, all without any additional task-specific training.

The work is currently in progress



## Evaluation



### Results


# [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_DeepAutoAI__ldm_soup_Llama-3.1-8B-Inst)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |28.64|
|IFEval (0-Shot)    |80.33|
|BBH (3-Shot)       |31.10|
|MATH Lvl 5 (4-Shot)|11.56|
|GPQA (0-shot)      | 5.26|
|MuSR (0-shot)      |11.52|
|MMLU-PRO (5-shot)  |32.07|