Update README.md

README.md

@@ -9,96 +9,99 @@ tags: []
 
 
 
+## 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
 
-### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **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:** NA
+- **Finetuned from model [optional]:** No fine-tuning
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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. -->
 
-### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+The direct use case of our work is o improve existing model performance as well as generating task specific weights with no training.
 
-### Downstream Use [optional]
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+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. -->
 
-[More Information Needed]
+No fine-tuning or architecture generalization
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+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
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+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. -->
 
-[More Information Needed]
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
-
-[More Information Needed]
+- 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."
 
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
 
 ## Evaluation
 
@@ -106,11 +109,10 @@ Use the code below to get started with the model.
 
 ### Testing Data, Factors & Metrics
 
-#### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
+We test our method on Winogrande and arc_challenge, and hellaswag
 
 #### Factors
 
@@ -142,31 +144,37 @@ Use the code below to get started with the model.
 
 <!-- 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:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **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
 
-[More Information Needed]
+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
 
-[More Information Needed]
+Nvidia-A100 cluster
 
 #### Hardware
 
-[More Information Needed]
+A single Nvidia-A100
 
 #### Software
 
-[More Information Needed]
+Model is tested using lm-harness tool version 0.4.3
 
 ## Citation [optional]