Update README.md

README.md

@@ -1,70 +1,59 @@
 ---
 library_name: peft
 base_model: meta-llama/Llama-2-7b-hf
+license: mit
+language:
+- en
+metrics:
+- bertscore
+- perplexity
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+Fine-tuned using QLoRA for story generation task.
 
 
-
-## Model Details
-
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+We utilize "Hierarchical Neural Story Generation" dataset and fine-tune the model to generate stories.
 
+The input to the model is structred as follows:
 
+'''
 
-- **Developed by:** [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]
+\#\#\# Instruction:  Below is a story idea. Write a short story based on this context.
 
-### Model Sources [optional]
+\#\#\# Input: [story idea here]
 
-<!-- Provide the basic links for the model. -->
+\#\#\# Response:
 
-- **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
+- **Developed by:** Abdelrahman ’Boda’ Sadallah, Anastasiia Demidova, Daria Kotova
+- **Model type:** Causal LM
+- **Language(s) (NLP):** English
+- **Finetuned from model [optional]:** meta-llama/Llama-2-7b-hf
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### 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]
+### Model Sources [optional]
 
-### Out-of-Scope Use
+<!-- Provide the basic links for the model. -->
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- **Repository:** https://github.com/BodaSadalla98/llm-optimized-fintuning
 
-[More Information Needed]
+## Uses
 
-## Bias, Risks, and Limitations
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The model is the result of our AI project. If you intend to use it, please, refer to the repo.
 
-[More Information Needed]
 
 ### Recommendations
 
 <!-- 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.
+For improving stories generation, you can play parameters: temeperature, top_p/top_k, repetition_penalty, etc.
 
 ## How to Get Started with the Model
 
@@ -78,16 +67,12 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Data 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]
+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. -->
 
-#### Preprocessing [optional]
-
-[More Information Needed]
-
 
 #### Training Hyperparameters
 
@@ -109,23 +94,19 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Data Card if possible. -->
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Test split of the same dataset.
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+We are using perplexity and BERTScore.
 
 ### Results
 
-[More Information Needed]
+Perplexity: 8.0546
+
+BERTScore: 80.11
 
 #### Summary
 
@@ -149,54 +130,6 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 - **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]
-
 
 ## Training procedure
 
@@ -216,4 +149,4 @@ The following `bitsandbytes` quantization config was used during training:
 ### Framework versions
 
 
-- PEFT 0.6.0.dev0
+- PEFT 0.6.0.dev0