Update README.md

README.md

@@ -1,6 +1,14 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- code
+license: mit
+base_model:
+- distilbert/distilgpt2
+datasets:
+- teven/code_contests
+language:
+- en
 ---
 
 # Model Card for Model ID
@@ -13,65 +21,84 @@ tags: []
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model is a LoRA fine-tuned version of distilgpt2, optimized for generating programming solutions in a style similar to competitive programming platforms such as LeetCode and Codeforces. It was trained on a custom dataset of ~5000 coding questions and answers and designed to be deployed with low-resource hardware (4GB VRAM GPU RTX 3050). The model is part of a larger project that incorporates Retrieval-Augmented Generation (RAG) to personalize outputs according to a user's historical coding patterns.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** https://github.com/Srinidhi-Yoganand
+- **Funded by :** Self-funded 
+- **Shared by :** sriniidhi
+- **Model type:** Causal Language Model (Decoder-only)
+- **Language(s) (NLP):** English (programming-focused)
+- **License:** MIT 
+- **Finetuned from model :** distilgpt2
 
-- **Developed by:** [More Information Needed]
-- **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]
-
-### Model Sources [optional]
+### Model Sources 
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Link]
+- **Demo:** [Link]
 
 ## 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
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model can be used for:
+
+  -  Auto-completing coding problems with competitive programming-style answers
 
-[More Information Needed]
+  -  Assisting in learning algorithms by showing step-by-step code solutions
 
-### Downstream Use [optional]
+  -  Experimenting with personalized coding assistants
+
+### Downstream Use 
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
+It can be plugged into systems using RAG to personalize answers by analyzing a user’s prior code submissions, or integrated into IDE plugins or chat-based tutoring systems.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+  - Generating natural language responses outside of programming tasks
+
+  - Mission-critical code generation (e.g., medical, legal, or financial systems)
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+- May hallucinate code or logic for uncommon problems.
+
+- Not robust to complex multi-language code interactions or frameworks.
 
 ### 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 (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
+
+- Use in combination with RAG for best personalization results.
+
+- Validate generated code before execution.
+
+- Avoid relying solely on this model for production-critical code generation.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("sriniidhi/gpt2-coding")
+tokenizer = AutoTokenizer.from_pretrained("sriniidhi/gpt2-coding")
+
+prompt = "def two_sum(nums, target):"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## Training Details
 
@@ -79,30 +106,49 @@ 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]
+The dataset consists of 5000+ competitive programming Q&A-style examples extracted and formatted from LeetCode, Codeforces, and similar platforms. Each entry includes a problem prompt and a sample Python, Java, Cpp solution.
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+LoRA fine-tuning using peft and transformers on top of distilgpt2.
 
 #### Preprocessing [optional]
 
-[More Information Needed]
+- Tokenized using GPT2TokenizerFast
+
+- Prompt-style formatting with problem + solution pairs
+
+- All code lowercased for consistency
 
 
 #### 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 -->
+- **Training regime:**
+- Epochs: 3
+
+- Batch size: 2
+
+- Learning rate: 5e-5
 
-#### Speeds, Sizes, Times [optional]
+- LoRA rank: 8
+
+- Precision: fp16
+
+- Max length: 512
+
+- Optimizer: AdamW
+  
+#### Speeds, Sizes, Times 
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- Fine-tuned on Google Colab and locally on RTX 3050 4GB
 
-[More Information Needed]
+- Training duration: ~30 hours
 
-## Evaluation
+- LoRA-adapted weights: ~75MB
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+## Evaluation
 
 ### Testing Data, Factors & Metrics
 
@@ -110,33 +156,27 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Evaluation done on 10000 held-out samples not used during training.
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+Manual evaluation of logical correctness and style similarity
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
+- Approx. 80% logical match on simple algorithm questions
 
+- Maintains coding style reasonably for most basic prompts
 
+- Some struggles with complex nested logic
 
-## Model Examination [optional]
+## Model Examination 
 
-<!-- Relevant interpretability work for the model goes here -->
+- Focused on learning indentation, loop constructs, and simple algorithm templates
 
-[More Information Needed]
+- No external code memory or global context unless paired with RAG
 
 ## Environmental Impact
 
@@ -144,56 +184,45 @@ Use the code below to get started with the model.
 
 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]
+- **Hardware Type:** NVIDIA RTX 3050 (4GB VRAM)
+- **Hours used:** ~30 hrs 
+- **Cloud Provider:** Google Colab (partial)
+- **Compute Region:** India
+- **Carbon Emitted:** ~2.15 kg CO₂ eq (estimated)
 
-## Technical Specifications [optional]
+## Technical Specifications 
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+Decoder-only Transformer (distilgpt2, 6-layer GPT-2)
 
 ### Compute Infrastructure
 
-[More Information Needed]
+- Colab + Local
 
-#### Hardware
-
-[More Information Needed]
+- PyTorch, transformers, peft, bitsandbytes
 
-#### Software
+#### Hardware
 
-[More Information Needed]
+- Ryzen 7
+- RTX 3050
 
-## Citation [optional]
+## Citation 
 
 <!-- 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]
+```
+@misc{gpt2-coding,
+  author       = {Srinidhi},
+  title        = {LoRA Fine-tuned distilgpt2 for Code Generation},
+  year         = {2025},
+  url          = {https://huggingface.co/sriniidhi/gpt2-coding}
+}
+```
 
 ## Model Card Contact
 
-[More Information Needed]
+GitHub: https://github.com/Srinidhi-Yoganand
+
+Hugging Face: https://huggingface.co/sriniidhi