Update README.md

README.md

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 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
 
 
+**DeepAutoAI/d2nwg_Llama-3.1-8B-Instruct-v0.0** is a customized variant of
+the **VAGOsolutions/Llama-3.1-SauerkrautLM-8B-Instruct**, which is itself a spectrum fine-tuned version of
+ **Llama-2.1-8B-Instruct**. This customization is achieved by learning the distribution of all normalization
+ layer weights from both the original Llama model and its fine-tuned counterpart. 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 the normalization layer of
+  DeepAutoAI/d2nwg_Llama-3.1-8B-Instruct-v0.0
+
 
 ## 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.
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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]
+We trained a diffusion model to learn the distribution of the normalization layers to enable generation  weights
+that improve the performance.
 
-### 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]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+This is the model card of a 🤗 transformers model that has been pushed on the Hub.
+This model card has been automatically generated.
 
-### Recommendations
+- **Developed by:** DeepAuto.ai
+- **Shared by [optional]:** the model was shared by from deepauto.ai
+- **Model type:** DeepAutoAI/d2nwg_Llama-3.1-8B-Instruct-v0.0 is a customized model by generating diverse weights for
+Llama-3.1-SauerkrautLM-8b-Instruct model
+- **Language(s) (NLP):** the base model was fine-tuned on German, English. We only use the one provided on Huggingface
+- **License:** llama3.1
+Contact: DeepAuto.ai
 
-<!-- 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
+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.
 
 #### 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 -->
-
+- **Training regime:** The pretrained weights used for training are orriginally in bflot16.
 #### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
@@ -101,8 +62,7 @@ Use the code below to get started with the model.
 [More Information Needed]
 
 ## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
+We evaluate the reconstrution and sampling performance on Winogrande task  using lm_eval tools
 
 ### Testing Data, Factors & Metrics
 
@@ -112,88 +72,7 @@ Use the code below to get started with the model.
 
 [More Information Needed]
 
-#### 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:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+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.