Update README.md

README.md

@@ -14,18 +14,7 @@ tags:
 - gemma
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
+# Code-Gemma-7b
 
 
 - **Developed by:** UnityAI Projects
@@ -107,72 +96,20 @@ While Code-GEMMA-7B is a robust and versatile AI model designed to assist in a v
 
 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. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### 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
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+```python
+from transformers import AutoModelWithHeads, AdapterType
 
-#### Factors
+# Load the model from your repository
+model = AutoModelWithHeads.from_pretrained("shapermindai/code-gemma-7b")
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+# Add an adapter to the model
+model.load_adapter("gemmadapter")
 
-[More Information Needed]
+# Set the adapter type
+model.set_active_adapters(AdapterType.text_task, "gemmadapter")
+```
 
-#### 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
 
@@ -182,54 +119,20 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 - **Hardware Type:** A10 24 GB x1
 - **Hours used:** 10h 22m 21s
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
+- **Cloud Provider:** Predibase
+- **Compute Region:** US
 - **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]
+Experiments were conducted using Google Cloud Platform in region northamerica-northeast1, which has a carbon efficiency of 0.03 kgCO$_2$eq/kWh. A cumulative of 10.5 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W).
 
-## More Information [optional]
+Total emissions are estimated to be 0.08 kgCO$_2$eq of which 100 percents were directly offset by the cloud provider.
 
-[More Information Needed]
+Estimations were conducted using the \href{https://mlco2.github.io/impact#compute}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}.
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+Perplexity AI, UnityAI Projects, Alex Scott
 
 ## Model Card Contact
 
-[More Information Needed]
+[email protected]