| | --- |
| | base_model: Qwen/Qwen1.5-1.8B |
| | library_name: peft |
| | pipeline_tag: text-generation |
| | tags: |
| | - base_model:adapter:Qwen/Qwen1.5-1.8B |
| | - lora |
| | - transformers |
| | - code-generation |
| | - python |
| | - reasoning |
| | - synthetic-data |
| | language: |
| | - en |
| | license: apache-2.0 |
| | --- |
| | |
| | # 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 |
| |
|
| | 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. |
| |
|
| | - **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 |
| |
|
| | ### Model Sources |
| |
|
| | - **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 |
| |
|
| | ### Direct Use |
| |
|
| | 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 |
| |
|
| | ### Example Usage |
| |
|
| | ```python |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | from peft import PeftModel |
| | |
| | # 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") |
| | |
| | # Load LoRA adapter |
| | model = PeftModel.from_pretrained(base_model, "[YOUR_MODEL_PATH]") |
| | |
| | # 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)) |
| | ``` |
| |
|
| | ### Out-of-Scope Use |
| |
|
| | - **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 |
| |
|
| | ### Limitations |
| |
|
| | 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 |
| |
|
| | - 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 |
| |
|
| | - **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 |
| |
|
| | #### 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 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 |
| |
|
| | #### Training Time |
| |
|
| | - Approximately [X hours] on Google Colab T4 GPU |
| |
|
| | ## Evaluation |
| |
|
| | ### Testing Data & Metrics |
| |
|
| | #### Testing Data |
| |
|
| | - **Test set size**: 7 diverse Python programming problems |
| | - **Problem types**: Mix of algorithmic challenges from the training distribution |
| |
|
| | #### Metrics |
| |
|
| | - **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 |
| |
|
| | | Metric | Base Model (Qwen 1.5 1.8B) | Fine-tuned Model | |
| | |--------|---------------------------|------------------| |
| | | Pass@1 | 75% | 100% | |
| | | Reasoning Structure | Inconsistent | 100% | |
| |
|
| | **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 |
| |
|
| | **Important Note**: Results are based on a small test set (7 examples). Larger-scale evaluation needed to confirm generalization. |
| |
|
| | ## Environmental Impact |
| |
|
| | - **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 |
| |
|
| | Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). |
| |
|
| | ## Technical Specifications |
| |
|
| | ### Model Architecture |
| |
|
| | - **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 |
| |
|
| | #### Hardware |
| |
|
| | - GPU: NVIDIA T4 (16GB VRAM) |
| | - Platform: Google Colab (free tier) |
| |
|
| | #### Software |
| |
|
| | - PEFT 0.17.1 |
| | - Transformers |
| | - bitsandbytes (for 4-bit quantization) |
| | - PyTorch |
| | - Groq API (for synthetic data generation) |
| |
|
| | ## Project Insights |
| |
|
| | ### What Worked Well |
| |
|
| | - 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 |
| |
|
| | ### Future Improvements |
| |
|
| | 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 |
| |
|
| | ## Citation |
| |
|
| | If you use this model, please cite: |
| |
|
| | ```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 Authors |
| |
|
| | [Rachit Verma] |
| |
|
| |
|
