rahulholla1
/

model

@@ -73,8 +73,7 @@ Users should exercise caution and validate the model's predictions with addition
 Ensure that you have the `transformers` library installed. If not, you can install it via pip:
-```bash
-pip install transformers
 You can load the model using the provided pipeline or directly with the AutoTokenizer and AutoModelForCausalLM classes from the transformers library.
 Once the model is loaded, you can use it for text generation tasks. If you prefer a high-level interface, you can use the pipeline approach as well.
@@ -92,7 +91,7 @@ The model was trained on a dataset containing examples of stock option data pair
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
 The input data was preprocessed to tokenize and encode the text input before training.
@@ -138,27 +137,7 @@ The model demonstrated the ability to provide relevant and actionable trading in
 #### 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
@@ -166,42 +145,6 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ### 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]

 Ensure that you have the `transformers` library installed. If not, you can install it via pip:
+```pip install transformers```
 You can load the model using the provided pipeline or directly with the AutoTokenizer and AutoModelForCausalLM classes from the transformers library.
 Once the model is loaded, you can use it for text generation tasks. If you prefer a high-level interface, you can use the pipeline approach as well.
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing
 The input data was preprocessed to tokenize and encode the text input before training.
 #### Summary
+## Technical Specifications
 ### Model Architecture and Objective
 ### Compute Infrastructure
+1 x A100 GPU - 80GB VRAM
+    117 GB RAM
+    12 vCPU