Update README.md

README.md

@@ -2,199 +2,174 @@
 library_name: transformers
 tags:
 - unsloth
+- qlora
+- lora
+- llama-3.2
+- instruction-tuned
+- bf16
+- 4bit
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# 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]
 
-<!-- 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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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. -->
+## Intended Uses & Limitations
 
 ### Direct Use
+- Generating Flask/Django/Streamlit project structures automatically.
+- Instruction-following tasks related to software engineering and code generation.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+### Downstream Use
+- Further fine-tuning on domain-specific datasets (e.g., medical imaging, finance, etc.).
+- Integration into developer assistants and productivity tools.
 
-[More Information Needed]
+### 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.
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### 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
+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.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+**Recommendation:** Always pair outputs with testing, validation, and human oversight.
 
-### Recommendations
+---
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## Getting Started
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+### Inference (PEFT adapter form)
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
 
-## How to Get Started with the Model
+model_id = "Sai2076/LLLMA_FINETUNED_PROJEN"
 
-Use the code below to get started with the model.
+tok = AutoTokenizer.from_pretrained(model_id)
 
-[More Information Needed]
+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"
+)
 
-## Training Details
+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 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. -->
+## Training Details
 
-[More Information Needed]
+### 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)
 
-<!-- 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]
+---
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & 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]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+### 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                | ___    |                       |
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## 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:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### 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]
+## 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)
 
-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+## 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}}
+}
+```
 
-## Model Card Contact
+---
 
-[More Information Needed]
+## Contact
+- **Author:** Sai Praneeth Kumar (UAB)  
+- **HF Profile:** [Sai2076](https://huggingface.co/Sai2076)