mahmoudalrefaey
/

llama3-codeweaver-lora

@@ -1,199 +1,103 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- llama-3
+- code-generation
+- qlora
+- peft
+- colab
+license: llama3
+datasets:
+- codeparrot/conala-mined-curated
+language:
+- en
+base_model:
+- meta-llama/Meta-Llama-3-8B
+pipeline_tag: text-generation
 ---
+# Model Card for llama3-codeweaver-lora
 ## Model Details
+- **Model name:** llama3-codeweaver-lora
+- **Developed by:** [mahmoudalrefaey](https://huggingface.co/mahmoudalrefaey)
+- **Funded by:** None (personal project)
+- **Finetuned from:** [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
+- **License:** LLaMA 3 license
+This is a **LLaMA-3 8B model fine-tuned with QLoRA** on the [CoNaLa mined-curated dataset](https://huggingface.co/datasets/codeparrot/conala-mined-curated) for **code generation tasks**.
+The adapter was trained on **Google Colab T4 (16GB)** using **fp16 mixed precision** with QLoRA for efficiency.
+---
 ## Uses
 ### Direct Use
+- Intended for **code generation assistant tasks** such as transforming natural language instructions into Python snippets.
+- Educational use for learning about LLM fine-tuning with LoRA adapters.
+### Downstream Use
+- Can be further fine-tuned on specialized coding datasets (e.g. SQL, JS).
+- Integration into coding assistants and research projects.
 ### Out-of-Scope Use
+- Not intended for production-critical code security auditing.
+- Not guaranteed to generate safe or fully optimized code.
+- Should not be used in environments where code execution safety is critical without sandboxing.
+---
 ## Training Details
 ### Training Data
+- Dataset: [CoNaLa mined-curated](https://huggingface.co/datasets/codeparrot/conala-mined-curated)
+- Dataset size used: ~7,000 samples
 ### Training Procedure
+- **Method:** QLoRA fine-tuning with 4-bit quantization
+- **Precision:** fp16 mixed precision
+- **Hardware:** Google Colab T4 (16GB GPU)
+- **Batch size:** 2 → effective batch 4 with accumulation
+- **Epochs:** 3
+- **Training time:** ~1h 30m
+---
 ## Evaluation
+### Testing Data
+- Held-out validation split (10% of dataset)
+### Metrics
+- **Validation Loss** decreased steadily across epochs
+- **Qualitative Evaluation:** Generated Python snippets from validation prompts
+- Example outputs matched reference solutions for common coding tasks
+### Example Prompt & Output
+```
+Prompt:
+### Instruction:
+Write code to convert integer num to list
+### Code:
+Generated:
+[int(x) for x in str(num)]
+```
 ## Environmental Impact
+- Hardware: NVIDIA T4 (16 GB VRAM)
+- Cloud Provider: Google Colab
+- Compute Region: Unknown
+- Training Duration: ~1.5 hours
+## Citation
+@misc{llama3-codeweaver-lora,
+  author = {Mahmoud Alrefaey},
+  title = {llama3-codeweaver-lora: A QLoRA fine-tuned LLaMA-3 model for code generation},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/mahmoudalrefaey/llama3-codeweaver-lora}},
+}