README.md

---
library_name: transformers
tags:
- unsloth
- qlora
- lora
- llama-3.2
- instruction-tuned
- bf16
- 4bit
---

# Model Card: Sai2076/LLLMA_FINETUNED_PROJEN

A **LLaMA-3.2** based instruction-tuned model fine-tuned with **Unsloth + QLoRA** using 🤗 **Transformers**.  
This model is part of the **ProjGen project**, aimed at enhancing developer productivity through automated project generation and structured code scaffolding.

---

## Model Details

### Model Description
- **Base model:** `meta-llama/Llama-3.2-<SIZE>-Instruct` <!-- replace SIZE with e.g. 8B/70B -->
- **Finetuning method:** Unsloth + QLoRA (LoRA adapters)
- **Precision (train):** 4-bit NF4 quantization (bitsandbytes) + bf16 compute
- **Context length:** 4096
- **Task(s):** Instruction following & project/code generation
- **License:** Inherits from Meta’s LLaMA-3.2 license
- **Developed by:** Sai Praneeth (UAB, ProjGen Project)
- **Finetuned from:** `meta-llama/Llama-3.2-<SIZE>-Instruct`
- **Shared by:** [Sai2076](https://huggingface.co/Sai2076)

### Model Sources
- **Repository:** [Sai2076/LLLMA_FINETUNED_PROJEN](https://huggingface.co/Sai2076/LLLMA_FINETUNED_PROJEN)
- **Project Paper:** ProjGen – Enhanced Developer Productivity for Flask Project Generation with a RAG-Enhanced Fine-Tuned Local LLM
- **Demo (optional):** [link to demo if available]

---

## Intended Uses & Limitations

### Direct Use
- Generating Flask/Django/Streamlit project structures automatically.
- Instruction-following tasks related to software engineering and code generation.

### Downstream Use
- Further fine-tuning on domain-specific datasets (e.g., medical imaging, finance, etc.).
- Integration into developer assistants and productivity tools.

### Out-of-Scope / Limitations
- Not suitable for medical, legal, or financial decision-making without human review.
- May hallucinate or produce insecure/inefficient code if not monitored.

---

## Bias, Risks, and Limitations
The model inherits risks from the base **LLaMA-3.2** model:
- Possible hallucinations and factual inaccuracies.
- Dataset/domain biases reflected in responses.
- Outputs should be validated before production deployment.

**Recommendation:** Always pair outputs with testing, validation, and human oversight.

---

## Getting Started

### Inference (PEFT adapter form)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model_id = "Sai2076/LLLMA_FINETUNED_PROJEN"

tok = AutoTokenizer.from_pretrained(model_id)

bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb,
    device_map="auto",
    torch_dtype="auto"
)

prompt = "Generate a Flask project with login, dashboard, and reports."
inputs = tok(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tok.decode(outputs[0], skip_special_tokens=True))
```

---

## Training Details

### Data
- **Dataset:** Custom **ProjGen dataset** built from structured Flask/Django/Streamlit projects and instructions.
- **Size:** [Fill in #samples / tokens]
- **Preprocessing:** Chat-style instruction formatting (system/user/assistant), deduplication, truncation at 4096 tokens.

### Training Procedure
- **Quantization:** 4-bit NF4 + double quantization (bitsandbytes)
- **LoRA Config:**  
  - `r`: 16  
  - `alpha`: 32  
  - `dropout`: 0.05  
  - Target modules: q_proj, k_proj, v_proj, o_proj, gate_up_proj, down_proj
- **Optimizer:** Paged AdamW (32-bit)  
- **LR / Schedule:** 2e-4 with cosine decay + warmup  
- **Batch size:** [fill in effective batch size]  
- **Epochs/Steps:** [fill in from ipynb]  
- **Precision:** bf16 mixed precision  
- **Grad checkpointing:** Enabled  
- **Flash attention:** Enabled (Unsloth optimization)

### Training Hardware
- **GPU:** RTX 4070 (12GB VRAM) [replace with actual if different]
- **Training time:** [fill in hours]
- **Checkpoint size:** ~ (adapter size: ~200MB; merged model size depends on base LLaMA size)

---

## Evaluation

### Data & Metrics
- **Validation set:** Held-out portion of ProjGen dataset
- **Metrics:**  
  - Instruction Following: Exact Match, ROUGE-L  
  - Code Generation: Pass@k (via unit test evaluation)

### Results
| Metric                | Value  | Notes                 |
|-----------------------|--------|-----------------------|
| Validation Loss       | ___    | From training logs    |
| Exact Match / F1      | ___    |                       |
| ROUGE-L / BLEU        | ___    |                       |
| Pass@1                | ___    |                       |

---

## Environmental Impact (estimate)
- **Hardware:** RTX 4070 (12GB VRAM) [replace with actual]  
- **Hours:** [fill in H]  
- **Region/Provider:** [cloud/on-prem]  
- **Estimated CO₂e:** Use [ML CO₂ Impact](https://mlco2.github.io/impact#compute)

---

## Citation

If you use this model, please cite the base model and this project:

**BibTeX (base, example):**
```bibtex
@article{touvron2023llama,
  title={LLaMA: Open and Efficient Foundation Language Models},
  author={Touvron, Hugo and others},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2023}
}
```

**Your work (fill in):**
```bibtex
@misc{projgen2025,
  title = {ProjGen: Enhanced Developer Productivity for Flask Project Generation with a RAG-Enhanced Fine-Tuned Local LLM},
  author = {Sai Praneeth, Renduchinthala},
  year = {2025},
  howpublished = {\url{https://huggingface.co/Sai2076/LLLMA_FINETUNED_PROJEN}}
}
```

---

## Contact
- **Author:** Sai Praneeth Kumar (UAB)  
- **HF Profile:** [Sai2076](https://huggingface.co/Sai2076)