Update README.md

README.md

@@ -6,202 +6,224 @@ tags:
 - base_model:adapter:Qwen/Qwen1.5-1.8B
 - lora
 - transformers
+- code-generation
+- python
+- reasoning
+- synthetic-data
+language:
+- en
+license: apache-2.0
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Qwen 1.5 1.8B - Python Code Generation with Step-by-Step Reasoning
 
+A fine-tuned version of Qwen 1.5 1.8B that generates Python code with detailed step-by-step reasoning explanations. This model teaches users how to solve programming problems by explaining its thought process before writing code.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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 is fine-tuned using QLoRA on a synthetic dataset of 1,000 Python programming problems enriched with step-by-step reasoning. The model learns to explain its problem-solving approach before generating code, making it ideal for educational purposes and transparent code generation.
 
-### Model Sources [optional]
+- **Developed by:** [Your Name/Organization]
+- **Model type:** Causal Language Model (Fine-tuned with LoRA adapters)
+- **Language(s):** English (code generation in Python)
+- **License:** Apache 2.0
+- **Finetuned from model:** Qwen/Qwen1.5-1.8B
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Base Model:** [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B)
+- **Training Data:** Synthetic dataset generated from MBPP and CodeAlpaca using Llama 3.1 8B
 
 ## 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 is designed for:
+- **Educational code generation**: Teaching programming concepts through explained solutions
+- **Transparent AI coding assistants**: Understanding how the model approaches problems
+- **Code explanation**: Generating step-by-step breakdowns of problem-solving strategies
+- **Learning tool**: Helping beginners understand algorithmic thinking
 
-[More Information Needed]
+### Example Usage
 
-### Downstream Use [optional]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+# Load base model and tokenizer
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen1.5-1.8B",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-1.8B")
 
-[More Information Needed]
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "[YOUR_MODEL_PATH]")
 
-### Out-of-Scope Use
+# Generate code with reasoning
+prompt = "Write a Python function to find the longest common prefix in a list of strings."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+### Out-of-Scope Use
 
-[More Information Needed]
+- **Production-critical systems**: This model is fine-tuned on a limited dataset and should not be used for safety-critical applications
+- **Non-Python languages**: The model is specifically trained on Python problems
+- **Complex software architecture**: Best suited for algorithm-level problems, not large-scale system design
+- **Security-sensitive code**: Should not be used for generating cryptographic or security-critical code without expert review
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Limitations
 
-[More Information Needed]
+1. **Dataset size**: Trained on only 1,000 examples, may not generalize to all problem types
+2. **Teacher model quality**: Synthetic data generated by Llama 3.1 8B may contain errors
+3. **Small test set**: Evaluated on only 7 problems, true generalization unknown
+4. **Potential overfitting**: High accuracy on test set may indicate memorization rather than true learning
+5. **No code validation**: Training data was not validated for correctness before 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- Always review and test generated code before using in production
+- Use as a learning tool rather than a replacement for human expertise
+- Validate outputs against test cases and edge cases
+- Consider the model's explanations as one perspective, not absolute truth
 
 ## 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]
+- **Source datasets**: MBPP (Mostly Basic Programming Problems) and CodeAlpaca
+- **Dataset size**: 1,000 Python programming problems
+- **Data generation**: Synthetic step-by-step reasoning generated using Llama 3.1 8B Instant via Groq API
+- **Data structure**: Each example contains:
+  - Original programming problem
+  - Step-by-step reasoning (problem understanding, algorithm design, implementation strategy)
+  - Python 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. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+#### Fine-tuning Method
 
+- **Technique**: QLoRA (Quantized Low-Rank Adaptation)
+- **Quantization**: 4-bit quantization for memory efficiency
+- **LoRA Configuration**:
+  - Rank (r): 8
+  - Alpha: 16
+  - Target modules: q_proj, k_proj, v_proj, o_proj (attention layers)
+  - Dropout: 0.05
 
 #### 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 epochs**: 3
+- **Learning rate**: 2e-4
+- **Optimizer**: paged_adamw_8bit
+- **Batch size**: [Specify if known]
+- **Training regime**: Mixed precision (4-bit quantization)
+- **Hardware**: Google Colab T4 GPU (free tier)
+- **Framework**: PEFT 0.17.1, Transformers, bitsandbytes
 
-#### Speeds, Sizes, Times [optional]
+#### Training Time
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- Approximately [X hours] on Google Colab T4 GPU
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
+### Testing Data & Metrics
 
 #### Testing Data
 
-<!-- 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]
+- **Test set size**: 7 diverse Python programming problems
+- **Problem types**: Mix of algorithmic challenges from the training distribution
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Primary metric**: Pass@1 (functional correctness - does the generated code execute correctly?)
+- **Secondary metric**: Reasoning structure presence (does output include step-by-step explanation?)
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
+| Metric | Base Model (Qwen 1.5 1.8B) | Fine-tuned Model |
+|--------|---------------------------|------------------|
+| Pass@1 | 75% | 100% |
+| Reasoning Structure | Inconsistent | 100% |
 
-## Model Examination [optional]
+**Key Findings**:
+- **+25 percentage point improvement** in functional correctness
+- **100% of outputs** now include structured step-by-step reasoning
+- All 7 test cases passed successfully
 
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+**Important Note**: Results are based on a small test set (7 examples). Larger-scale evaluation needed to confirm generalization.
 
 ## 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**: NVIDIA T4 GPU (Google Colab)
+- **Hours used**: ~[X hours for fine-tuning]
+- **Cloud Provider**: Google Cloud Platform
+- **Compute Region**: [Specify if known]
+- **Carbon Emitted**: Minimal due to use of QLoRA on single T4 GPU
 
-- **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]
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
 
-## Technical Specifications [optional]
+## Technical Specifications
 
-### Model Architecture and Objective
+### Model Architecture
 
-[More Information Needed]
+- **Base architecture**: Qwen 1.5 1.8B (Transformer decoder)
+- **Fine-tuning method**: LoRA adapters on attention layers
+- **Total parameters**: 1.8B (base) + ~4.7M (LoRA adapters)
+- **Trainable parameters**: ~4.7M (0.26% of total)
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- GPU: NVIDIA T4 (16GB VRAM)
+- Platform: Google Colab (free tier)
 
 #### 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:**
+- PEFT 0.17.1
+- Transformers
+- bitsandbytes (for 4-bit quantization)
+- PyTorch
+- Groq API (for synthetic data generation)
 
-[More Information Needed]
+## Project Insights
 
-## Glossary [optional]
+### What Worked Well
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- Cross-model knowledge distillation (8B teacher → 1.8B student)
+- QLoRA enabled fine-tuning on free-tier GPU
+- Structured prompts for synthetic data generation
+- Teaching reasoning process alongside code generation
 
-[More Information Needed]
+### Future Improvements
 
-## More Information [optional]
+1. **Better teacher model**: Use Llama 3.1 70B for higher-quality synthetic data
+2. **Data validation**: Verify all generated code executes correctly before training
+3. **Larger dataset**: Scale to 5,000-10,000 examples
+4. **Robust evaluation**: Test on 50-100 problems from benchmarks like HumanEval
+5. **Higher LoRA rank**: Experiment with rank 16 or 32 for more capacity
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+If you use this model, please cite:
 
-[More Information Needed]
+```bibtex
+@misc{qwen15-code-reasoning,
+  author = {[Rachit Verma]},
+  title = {Qwen 1.5 1.8B Fine-tuned for Python Code Generation with Reasoning},
+  year = {2025},
+  publisher = {HuggingFace},
+}
+```
 
-## Model Card Contact
+## Model Card Authors
 
-[More Information Needed]
-### Framework versions
+[Rachit Verma]
 
-- PEFT 0.17.1