README.md

---
library_name: transformers
model-index:
- name: Explore_Llama-3.2-1B-Inst_v1
  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: 49.99
      name: strict accuracy
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1
      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: 4.26
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1
      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: 1.28
      name: exact match
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1
      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: 0
      name: acc_norm
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1
      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: 5.2
      name: acc_norm
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1
      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: 2.99
      name: accuracy
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1
      name: Open LLM Leaderboard
license: apache-2.0
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.2-1B
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Overview


**DeepAutoAI/Explore_Llama-3.2-1B-Inst** is developed by **deepAuto.ai** by learning the distribution of llama-3.2-1B-instruct.
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. specifically , this model is based on learning the distrinution of transformer layers from 16 to 31. 

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/Explore_Llama-3.1-1B-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


## Model Details


<!-- Provide a longer summary of what this model is. -->

We trained a diffusion model to learn the distribution of subset of llama to enable generation weights that improve the performance.
We generate task specific weights on winogrande and arc_challenge then transfer the best model for leaderboard benchmarking.

- **Developed by:** DeepAuto.ai
- **Funded by [optional]:** DeepAuto.ai
- **Shared by [optional]:** DeepAuto.ai
- **Model type:** llama-3.2-1B
- **Language(s) (NLP):** English
- **License:** 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
- **Finetuned from model [optional]:** No fine-tuning

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** Under construction
- **Paper [optional]:** To be announce


## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->


<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

The direct use case of our work is o improve existing model performance as well as generating task specific weights with no training.


<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
Performance improvement of existing large models with limited compute

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

No fine-tuning or architecture generalization

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Using a generative model to produce weights can potentially lead to unintended or undesirable outputs. However, the generated content 
will still fall within the range of what the base model is inherently capable of producing.

## How to Get Started with the Model
The work is under progress

## Training Details
We employed a latent diffusion process on pretrained model weights, unlocking the ability to generate diverse, previously unseen neural networks. 
Remarkably, even within the constraints of one-shot learning, our approach consistently produces a wide range of weight variations, each offering 
distinct performance characteristics. These generated weights not only open opportunities for weight averaging and model merging but also have the
potential to significantly enhance model performance. Moreover, they enable the creation of task-specific weights, tailored to optimize performance 
for specialized applications

### Training Data
The training data used to produced the current model is the base pretrained weights

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->


### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

- We selected a set of layers and combined their pretrained weights, then trained a Variational Autoencoder (VAE) to encode these weights into the layer dimension.
- We conditionally trained a diffusion model on this set of weights, allowing individual sampling of layer-specific weights.
- All selected layers were encoded into a 1024-dimensional space. This model exclusively contained the sampled weights for layer normalization."


<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->


## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->



Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** Nvidia-A100-40Gb
- **Hours used:**  VAE is trained for 4 hour and diffusion process 4 hours
- **Compute Region:** South Korea
- **Carbon Emitted:** 0.96kg

## Technical Specifications [optional]

### Model Architecture and Objective

We used Latent diffusion for weights generation, and llama3-2-1B as target architectures.

The primary objective of this weight generation process was to demonstrate that by learning only the distribution
of few layers weights (normlaization layers in this case) in an 1-billion-parameter model, it is possible to significantly enhance the
 model's capabilities. Notably, this is achieved using a fraction of the computational resources and without the
 need for fine-tuning, showcasing the efficiency and potential of this approach.

### Compute Infrastructure

Nvidia-A100 cluster

#### Hardware

A single Nvidia-A100

#### Software

Model is tested using lm-harness tool version 0.4.3

## Model Card Contact
deepauto.ai