初始化项目，由ModelHub XC社区提供模型
Model: DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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model-index:
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- name: ldm_soup_Llama-3.1-8B-Inst
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 80.33
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name: strict accuracy
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source:
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 31.1
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name: normalized accuracy
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source:
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 11.56
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name: exact match
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source:
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 5.26
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name: acc_norm
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source:
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 11.52
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name: acc_norm
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source:
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 32.07
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
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name: Open LLM Leaderboard
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license: apache-2.0
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language:
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- en
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base_model:
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- meta-llama/Llama-3.1-8B-Instruct
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---
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# Model Card for DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in
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- compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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## Overview
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**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.
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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**.
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This approach has led to improved performance on previously unseen leaderboard tasks, all without any additional task-specific training.
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The work is currently in progress
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## References
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<a href="https://arxiv.org/abs/2402.18153" target="_blank">Diffusion-Based Neural Network Weights Generation</a>
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## Evaluation
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### Results
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_DeepAutoAI__ldm_soup_Llama-3.1-8B-Inst)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |28.64|
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|IFEval (0-Shot) |80.33|
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|BBH (3-Shot) |31.10|
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|MATH Lvl 5 (4-Shot)|11.56|
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|GPQA (0-shot) | 5.26|
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|MuSR (0-shot) |11.52|
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|MMLU-PRO (5-shot) |32.07|
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