Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+  - llama-3.2
+  - causal-lm
+  - code
+  - python
+  - peft
+  - qlora
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for llama32-1b-python-docstrings-qlora
 
+A parameter-efficiently fine-tuned adapter on top of `meta-llama/Llama-3.2-1B-Instruct` for generating concise one-line Python docstrings from function bodies.
 
 ## Model Details
 
 ### Model Description
 
-<!-- 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]
+- **Developed by:** Abdullah Al-Housni
+- **Model type:** Causal language model with LoRA/QLoRA adapters
+- **Language(s):** Python code as input, English docstrings as output
+- **License:** Same as `meta-llama/Llama-3.2-1B-Instruct` (Meta Llama 3.2 Community License)
+- **Finetuned from model:** `meta-llama/Llama-3.2-1B-Instruct`
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+The model is trained to take a Python function definition and generate a concise, one-line docstring describing what the function does.
 
 ## 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. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+- Automatically generate one-line Python docstrings for functions.
+- Improve or bootstrap documentation in Python codebases.
+- Educational use for learning how to summarize code behavior.
 
-[More Information Needed]
+Typical usage pattern:
+- Input: Python function body (source code).
+- Output: Single-sentence English description suitable as a docstring.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Generating full, multi-paragraph API documentation.
+- Security auditing or correctness guarantees for code.
+- Use outside Python (e.g., other programming languages) without additional fine-tuning.
+- Any safety-critical application where incorrect summaries could cause harm.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model can produce **incorrect or incomplete summaries**, especially for complex or ambiguous functions.
+- It may imitate noisy or low-quality patterns from the training data (e.g., overly short or cryptic docstrings).
+- It does **not** understand project-specific context, invariants, or business logic; outputs should be reviewed by a human developer.
 
 ### 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.
+- Use the model as an **assistive tool**, not an authoritative source.
+- Always review and edit generated docstrings before committing to production code.
+- For non-Python or highly domain-specific code, consider additional fine-tuning on in-domain examples.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Example with 🤗 Transformers and PEFT (LoRA adapter):
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
 
-## Training Details
+base_model_id = "meta-llama/Llama-3.2-1B-Instruct"
+adapter_id = "YOUR_USERNAME/llama32-1b-python-docstrings-qlora"
 
-### Training Data
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
+model = PeftModel.from_pretrained(model, adapter_id)
 
-<!-- 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. -->
+def make_prompt(code: str) -> str:
+    return (
+        "Write a one-line Python docstring for this function:\n\n"
+        f"{code}\n\n\"\"\""
+    )
 
-[More Information Needed]
+code = "def add(a, b):\n    return a + b"
+inputs = tokenizer(make_prompt(code), return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=32, do_sample=False)
+text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(text)
+```
 
-### Training Procedure
+## Training Details
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Data
 
-#### Preprocessing [optional]
+- Dataset: Python subset of CodeSearchNet (`Nan-Do/code-search-net-python`)
+- Inputs: `code` column (full Python function body)
+- Targets: First non-empty line of `docstring`
+- A filtered subset of ~1,000–2,000 examples was used for efficient QLoRA fine-tuning
 
-[More Information Needed]
+### Training Procedure
 
+- Objective: Causal language modeling (predict the docstring continuation)
+- Method: QLoRA (4-bit quantized base model with LoRA adapters)
+- Precision: 4-bit quantized weights, bf16 compute
+- Epochs: 1
+- Max sequence length: 256–512 tokens
 
 #### 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 -->
+- Learning rate: ~2e-4 (adapter weights only)
+- Epochs: 1
+- Optimizer: AdamW via Hugging Face `Trainer`
+- LoRA rank: 16
+- LoRA alpha: 32
+- LoRA dropout: 0.05
 
-#### 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]
+Held-out test split from the same CodeSearchNet Python dataset, using identical `code` → one-line docstring mapping.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+- Function size and complexity  
+- Variety in docstring writing styles  
+- Presence of short or noisy docstrings  
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- BLEU (sacreBLEU): strict n-gram overlap, sensitive to paraphrasing  
+- ROUGE (ROUGE-1 / ROUGE-2 / ROUGE-L): better for short summaries  
 
 ### Results
 
-[More Information Needed]
+Approximate performance on ~50 held-out samples:
+
+- BLEU: ~12.4  
+- ROUGE-1: ~0.78  
+- ROUGE-2: ~0.74  
+- ROUGE-L: ~0.78  
 
 #### Summary
 
+The model frequently reproduces or closely paraphrases the correct docstring. Occasional failures include echoing part of the prompt or returning an empty string. Strong performance for a 1B model trained briefly on a small dataset.
 
+---
 
-## Model Examination [optional]
+## Model Examination
 
-<!-- Relevant interpretability work for the model goes here -->
+Not applicable.
 
-[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: Google Colab GPU (T4/L4)  
+- Hours Used: ~0.5–1 hour total  
+- Cloud Provider: Google Colab  
+- Compute Region: US  
+- Carbon Emitted: Not estimated (very low due to minimal training time)
 
-- **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]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- Base model: Llama 3.2 1B Instruct  
+- Architecture: Decoder-only transformer  
+- Objective: Causal language modeling  
+- Parameter-efficient fine-tuning using LoRA (rank 16)
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+Single Google Colab GPU (T4 or L4)
 
 #### Software
 
-[More Information Needed]
+- Python  
+- PyTorch  
+- Hugging Face Transformers  
+- PEFT  
+- bitsandbytes  
+- Datasets  
 
-## 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. -->
+## Citation
 
-**BibTeX:**
+Not applicable.
 
-[More Information Needed]
+---
 
-**APA:**
+## Glossary
 
-[More Information Needed]
+Not applicable.
 
-## Glossary [optional]
+---
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## More Information
 
-[More Information Needed]
+See the Hugging Face model page for updates or usage examples.
 
-## More Information [optional]
+---
 
-[More Information Needed]
+## Model Card Authors
 
-## Model Card Authors [optional]
+Abdullah Al-Housni
 
-[More Information Needed]
+---
 
 ## Model Card Contact
 
-[More Information Needed]
+Available through the Hugging Face model repository.