Update README.md

README.md

@@ -23,29 +23,31 @@ license: mit
 
 
 
+
+
 ## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
+This model focuses on fine-tuning the Llama-2 7B large language model for Python code generation. The project leverages Ludwig, an open-source toolkit, and a dataset of 500k Python code samples from Hugging Face. The model applies techniques such as prompt templating, zero-shot inference, and few-shot learning, enhancing the model's performance in generating Python code snippets efficiently.
 
-
-- **Developed by:** Kevin Geejo, Aniket Yadav, Rishab Pandey
-- **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]
+- **Developed by:** Kevin Geejo, Aniket Yadav, Rishab Pandey  
+- **Funded by [optional]:** No specific funding agency identified  
+- **Shared by [optional]:** No additional sharing information provided  
+- **Model type:** Fine-tuned Llama-2 7B for Python code generation  
+- **Language(s) (NLP):** Python (for code generation tasks)  
+- **License:** Not explicitly mentioned, but Llama-2 models are typically governed by Meta AI’s open-source licensing  
+- **Finetuned from model [optional]:** Llama-2 7B (Meta AI, 2023)  
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** Hugging Face (trained models uploaded, no specific link provided)  
+- **Paper [optional]:** Not explicitly mentioned  
+- **Demo [optional]:** No demo link provided  
 
 ## Uses
 
@@ -55,37 +57,48 @@ license: mit
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+- Python code generation for software development  
+- Automation of coding tasks  
+- Developer productivity enhancement  
 
 ### 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]
+- Code completion, bug fixing, and Python code translation  
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+- Non-Python programming tasks  
+- Generation of sensitive, legal, or medical content  
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+- Limited to Python programming tasks  
+- Dataset biases from Hugging Face's Python Code Dataset  
+- Environmental impact from computational costs during fine-tuning  
 
 ### 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.
+Users should be aware of computational efficiency trade-offs and potential limitations in generalizing to new Python tasks.  
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the code below to get started with the model:
+
+```python
+# Example setup (simplified)
+import ludwig
+from transformers import AutoModel
 
-[More Information Needed]
+model = AutoModel.from_pretrained("llama-2-7b-python")
+```  
 
 ## Training Details
 
@@ -93,26 +106,18 @@ Use the code below to get started with the model.
 
 <!-- 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]
+- 500k Python code samples sourced from Hugging Face  
 
 ### 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 -->
+- **Preprocessing [optional]:** Hugging Face Python Code Dataset  
+- **Training regime:** Parameter Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA)  
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- Not explicitly mentioned in the document  
 
 ## Evaluation
 
@@ -122,95 +127,90 @@ Use the code below to get started with the model.
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+- Derived from Python code datasets on Hugging Face  
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+- Python code generation tasks  
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- Code correctness and efficiency  
 
 ### Results
 
-[More Information Needed]
+- Fine-tuning improved Python code generation performance  
 
 #### Summary
 
-
+The fine-tuned model showed enhanced proficiency in generating Python code snippets, reflecting its adaptability to specific coding tasks.  
 
 ## Model Examination [optional]
 
 <!-- Relevant interpretability work for the model goes here -->
 
-[More Information Needed]
+[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).
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).  
 
-- **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]
+- **Hardware Type:** Not specified  
+- **Hours used:** Not specified  
+- **Cloud Provider:** Not specified  
+- **Compute Region:** Not specified  
+- **Carbon Emitted:** Not specified  
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- Llama-2 7B model architecture fine-tuned for Python code generation  
 
 ### Compute Infrastructure
 
-[More Information Needed]
+- Not explicitly mentioned  
 
 #### Hardware
 
-[More Information Needed]
+- Not specified  
 
 #### Software
 
-[More Information Needed]
-
+- Ludwig toolkit and Hugging Face integration  
 
-**BibTeX:**
+**BibTeX:**  
 
+[More Information Needed]  
 
-[More Information Needed]
+**APA:**  
 
-**APA:**
-
-[More Information Needed]
+[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]
+- **Llama-2:** Open-source large language model by Meta AI  
+- **LoRA (Low-Rank Adaptation):** Efficient fine-tuning method modifying fewer model parameters  
+- **PEFT:** Parameter-efficient fine-tuning technique  
 
 ## More Information [optional]
 
-[More Information Needed]
+[More Information Needed]  
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+Kevin Geejo, Aniket Yadav, Rishab Pandey  
 
 ## Model Card Contact
 
-[More Information Needed]
-
+[email protected], [email protected], [email protected]  
 
 ### Framework versions
 
-- PEFT 0.7.1
+- **Llama-2 version:** 7B  
+- **Ludwig version:** 0.8  
+- **Hugging Face integration:** Latest