README.md

README.md

@@ -3,199 +3,202 @@ library_name: transformers
 tags:
 - trl
 - sft
+- code
+- competitive-programming
+- codeforces
+- lora
+license: mit
+datasets:
+- open-r1/codeforces-cots
+base_model:
+- google/gemma-2-2b-it
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Gemma-2-2b Fine-tuned for Competitive Programming
 
+This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) on the [open-r1/codeforces-cots](https://huggingface.co/datasets/open-r1/codeforces-cots) dataset for competitive programming problem solving.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-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:** [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]
+This model has been fine-tuned using LoRA (Low-Rank Adaptation) on competitive programming problems from Codeforces. It's designed to help generate solutions for algorithmic and data structure problems commonly found in competitive programming contests.
 
-### Model Sources [optional]
+- **Developed by:** Aswith77
+- **Model type:** Causal Language Model (Code Generation)
+- **Language(s):** Python, C++, Java (primarily Python)
+- **License:** MIT
+- **Finetuned from model:** google/gemma-2-2b-it
+- **Fine-tuning method:** LoRA (Low-Rank Adaptation)
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Hugging Face Model](https://huggingface.co/Aswith77/gemma-2-2b-it-finetune-codeforces-cots)
+- **Base Model:** [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it)
+- **Dataset:** [open-r1/codeforces-cots](https://huggingface.co/datasets/open-r1/codeforces-cots)
 
 ## 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. -->
-
-[More Information Needed]
+This model is intended for generating solutions to competitive programming problems, particularly those similar to Codeforces problems. It can:
 
-### Downstream Use [optional]
+- Generate algorithmic solutions for given problem statements
+- Help with code completion for competitive programming
+- Assist in learning algorithmic problem-solving patterns
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+The model can be further fine-tuned on:
+- Specific programming languages
+- Domain-specific algorithmic problems
+- Educational coding platforms
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[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.
+This model should not be used for:
+- Production code without thorough testing
+- Security-critical applications
+- General-purpose software development without validation
+- Problems requiring real-world system design
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2-2b-it",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load the fine-tuned LoRA adapters
+model = PeftModel.from_pretrained(
+    base_model,
+    "Aswith77/gemma-2-2b-it-finetune-codeforces-cots"
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it")
+
+# Generate code for a problem
+problem = """
+Given an array of integers, find the maximum sum of a contiguous subarray.
+Input: [-2,1,-3,4,-1,2,1,-5,4]
+Output: 6 (subarray [4,-1,2,1])
+"""
+
+inputs = tokenizer(problem, return_tensors="pt")
+outputs = model.generate(
+    **inputs,
+    max_length=512,
+    temperature=0.7,
+    do_sample=True,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+solution = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(solution)
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- 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 model was trained on the [open-r1/codeforces-cots](https://huggingface.co/datasets/open-r1/codeforces-cots) dataset, specifically using 1,000 competitive programming problems and their solutions from Codeforces.
 
 ### 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 -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Training regime:** fp16 mixed precision
+- **Learning rate:** 2e-4
+- **Batch size:** 1 (per device)
+- **Gradient accumulation steps:** 2
+- **Max steps:** 100
+- **Warmup steps:** 5
+- **Optimizer:** AdamW 8-bit
+- **Weight decay:** 0.01
+- **LoRA rank (r):** 16
+- **LoRA alpha:** 32
+- **LoRA dropout:** 0.1
+- **Target modules:** q_proj, v_proj, k_proj, o_proj, gate_proj, up_proj, down_proj
+
+#### Speeds, Sizes, Times
+
+- **Training time:** ~20 minutes
+- **Hardware:** Tesla T4 GPU (16GB)
+- **Model size:** ~30MB (LoRA adapters only)
+- **Final training loss:** 0.3715
+- **Training samples per second:** 0.338
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
+The model achieved a training loss of 0.3715, showing good convergence from an initial loss of 0.9303. The loss curve demonstrated steady improvement throughout training without signs of overfitting.
 
-#### Testing Data
+### Training Loss Progression
 
-<!-- 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]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
+- Initial loss: 0.9303
+- Final loss: 0.3715
+- Loss reduction: ~60%
 
+## Bias, Risks, and Limitations
 
+### Limitations
 
-## Model Examination [optional]
+- **Dataset bias:** Trained primarily on Codeforces problems, may not generalize well to other competitive programming platforms
+- **Language bias:** Solutions may favor certain programming patterns common in the training data
+- **Size limitations:** Being a 2B parameter model, it may struggle with very complex algorithmic problems
+- **Code correctness:** Generated code should always be tested and validated before use
 
-<!-- Relevant interpretability work for the model goes here -->
+### Recommendations
 
-[More Information Needed]
+- Always test generated solutions with multiple test cases
+- Use the model as a starting point, not a final solution
+- Verify algorithmic correctness and time complexity
+- Consider the model's suggestions as one approach among many possible solutions
 
 ## 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).
+Training was conducted on a single Tesla T4 GPU for approximately 20 minutes, resulting in minimal environmental impact compared to larger scale training runs.
 
-- **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:** Tesla T4 GPU
+- **Hours used:** 0.33 hours
+- **Cloud Provider:** Kaggle
+- **Compute Region:** Not specified
+- **Carbon Emitted:** Minimal (estimated < 0.1 kg CO2eq)
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- **Base Architecture:** Gemma-2 (2B parameters)
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+- **Objective:** Causal language modeling with supervised fine-tuning
+- **Quantization:** 4-bit quantization during training
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- **GPU:** Tesla T4 (16GB VRAM)
+- **Platform:** Kaggle Notebooks
 
 #### 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]
+- **Framework:** PyTorch, Transformers, PEFT, TRL
+- **Quantization:** bitsandbytes 4-bit
+- **Training:** Supervised Fine-Tuning (SFT)
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+Created by Aswith77 during fine-tuning experiments with competitive programming datasets.
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or issues regarding this model, please open an issue in the model repository.