Update README.md

README.md

@@ -36,9 +36,7 @@ The input to the model is structred as follows:
 - **Language(s) (NLP):** English
 - **Finetuned from model [optional]:** meta-llama/Llama-2-7b-hf
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
 - **Repository:** https://github.com/BodaSadalla98/llm-optimized-fintuning
 
@@ -55,11 +53,6 @@ The model is the result of our AI project. If you intend to use it, please, refe
 
 For improving stories generation, you can play parameters: temeperature, top_p/top_k, repetition_penalty, etc.
 
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
 
 ## Training Details
 
@@ -69,20 +62,6 @@ Use the code below to get started with the model.
 
 Github for the dataset: https://github.com/kevalnagda/StoryGeneration
 
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-
-#### 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
 
@@ -90,9 +69,6 @@ Github for the dataset: https://github.com/kevalnagda/StoryGeneration
 
 ### Testing Data, Factors & Metrics
 
-#### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
 
 Test split of the same dataset.
 
@@ -108,29 +84,6 @@ Perplexity: 8.0546
 
 BERTScore: 80.11
 
-#### 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]
-
-
 ## Training procedure