Update README.md

README.md

@@ -6,6 +6,11 @@ tags: []
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
+DeepAutoAI/Explore_Llama-3.1-8B-Inst is a customized variant of Llama-2.1-8B-Instruct. This customization is achieved by learning 
+the distribution of all normalization layer weights followed by the distribution of the last transformer block, 30, and 24th FFN layers of 
+ the original Llama model. 
+A layer-conditional diffusion based weights generation model that enables sampling for performance enhancement by leveraging 
+the learned distributions to optimize the merging process is used to generate newly diverse weights
 
 
 
@@ -15,51 +20,50 @@ tags: []
 
 <!-- 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-3.1-8B
+- **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]
+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.
 
 ### Recommendations
 
@@ -74,31 +78,29 @@ Use the code below to get started with the model.
 [More Information Needed]
 
 ## 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]
-
-
-#### Training Hyperparameters
+- 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 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 +108,10 @@ 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 +143,37 @@ 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-A100-80Gb
+- **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 llama3-1-8B as target architectures.
+
+The primary objective of this weight generation process was to demonstrate that by learning only the distribution
+of few layers weights9normlaization layers in this case) in an 8-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]
 
@@ -196,4 +203,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Contact
 
-[More Information Needed]
+For any question contact deepauto.ai