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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+Explore_Llama-3.2-1B-Inst_v1.1 - AWQ
+- Model creator: https://huggingface.co/DeepAutoAI/
+- Original model: https://huggingface.co/DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1.1/
+
+
+
+
+Original model description:
+---
+library_name: transformers
+model-index:
+- name: Explore_Llama-3.2-1B-Inst_v1.1
+  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: 48.13
+      name: strict accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1.1
+      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: 5.19
+      name: normalized accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1.1
+      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: 1.36
+      name: exact match
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1.1
+      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: 2.35
+      name: acc_norm
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1.1
+      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: 4.05
+      name: acc_norm
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1.1
+      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: 3.05
+      name: accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/Explore_Llama-3.2-1B-Inst_v1.1
+      name: Open LLM Leaderboard
+license: apache-2.0
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+![Model Exploration](./d2nwg2.webp)
+
+
+
+## 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 the top 2 layer of layer in feed forward
+or attention layers based on spectrum based optimum layer selection. 
+
+
+We directly transfer the weights of the best model on both winogrande and arc-challenge 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. -->
+
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+
+<!-- This should link to a Dataset Card if possible. -->
+
+We test our method on Winogrande and arc_challenge, and hellaswag
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## 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
+[email protected]
+
+## References
+<a href="https://arxiv.org/abs/2402.18153" target="_blank">Diffusion-Based Neural Network Weights Generation</a>
+
+
+# [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__Explore_Llama-3.2-1B-Inst_v1.1)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |14.12|
+|IFEval (0-Shot)    |58.44|
+|BBH (3-Shot)       | 8.82|
+|MATH Lvl 5 (4-Shot)| 6.04|
+|GPQA (0-shot)      | 1.68|
+|MuSR (0-shot)      | 0.66|
+|MMLU-PRO (5-shot)  | 9.09|
+