---
base_model: codellama/CodeLlama-7b-Instruct-hf
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:codellama/CodeLlama-7b-Instruct-hf
- lora
- transformers
- luau
- roblox
license: apache-2.0
language:
- en
---

# Model Card for CodeLlama-7B-Instruct-Luau

Fine-tuned version of `codellama/CodeLlama-7b-Instruct-hf` targeted toward the **Luau** programming language, Roblox’s Lua-derived scripting language.

This model is distributed as a **LoRA adapter** and is intended to improve the base model’s performance on Roblox-specific scripting tasks.

---

## Model Details

### Model Description

This model is a parameter-efficient fine-tuning (LoRA) of CodeLlama 7B Instruct, specialized for generating, explaining, and refactoring **Luau** code.

The fine-tuning focuses on Roblox development patterns, including common services, APIs, gameplay scripting idioms, and client/server logic. The model is designed to assist developers during prototyping, learning, and general scripting workflows.

- **Developed by:** darwinkernelpanic  
- **Funded by:** Not applicable  
- **Shared by:** darwinkernelpanic  
- **Model type:** Causal Language Model (decoder-only, LoRA adapter)  
- **Language(s) (NLP):** English  
- **License:** Apache-2.0  
- **Finetuned from model:** codellama/CodeLlama-7b-Instruct-hf  

### Model Sources

- **Repository:** https://huggingface.co/darwinkernelpanic/CodeLlama-7b-Instruct-hf-luau  
- **Paper:** *Code Llama: Large Language Models for Code* (Meta AI)  
- **Demo:** Not available  

---

## Uses

### Direct Use

This model can be used directly for:

- Writing Luau scripts for Roblox
- Explaining Roblox APIs and services
- Refactoring or debugging Luau code
- Prototyping gameplay systems and utilities
- Learning Luau and Roblox scripting concepts

The model is intended as a **developer assistant**, not an autonomous system.

### Downstream Use

Potential downstream uses include:

- Further fine-tuning on proprietary Roblox frameworks
- Integration into IDEs or editor tooling
- Chat-based assistants for Roblox development
- Educational or documentation tooling

### Out-of-Scope Use

This model should **not** be used for:

- Safety-critical or production-critical systems
- Legal, medical, or financial advice
- Malware, exploit, or cheat development
- Fully automated code deployment without review

---

## Bias, Risks, and Limitations

- Inherits biases and limitations from the base CodeLlama model
- May hallucinate Roblox APIs or outdated behaviors
- Does not validate code at runtime
- Output correctness depends on prompt quality

### Recommendations

Users should:

- Review all generated code manually
- Test scripts in Roblox Studio
- Cross-check with official Roblox documentation
- Treat outputs as suggestions rather than authoritative solutions

---

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = "codellama/CodeLlama-7b-Instruct-hf"
adapter_model = "darwinkernelpanic/CodeLlama-7b-Instruct-hf-luau"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_model)

prompt = "Write a Luau function that creates a Part and parents it to Workspace."
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=300,
    temperature=0.7,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
````

---

## Training Details

### Training Data

The model was fine-tuned on a curated mixture of:

* Luau scripts
* Roblox API usage examples
* Open-source Roblox projects
* Synthetic instruction-style prompts

All data was filtered to avoid private, proprietary, or sensitive content.

### Training Procedure

The model was trained using parameter-efficient fine-tuning with LoRA while keeping the base model weights frozen.

#### Preprocessing

* Code formatting normalization
* Instruction-style prompt structuring
* Removal of low-quality or irrelevant samples

#### Training Hyperparameters

* **Training regime:** fp16 mixed precision

#### Speeds, Sizes, Times

* **Base model size:** ~7B parameters
* **Trainable parameters:** <1% (LoRA adapters only)
* **Adapter checkpoint size:** ~100–200 MB

---

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

* Hand-written Luau prompts
* Roblox-specific scripting scenarios

#### Factors

* Luau syntax correctness
* Roblox API familiarity
* Instruction-following behavior

#### Metrics

* Qualitative human evaluation
* Manual code review and comparison with base model

### Results

The LoRA adapter demonstrates improved performance over the base model in:

* Generating idiomatic Luau
* Correct Roblox service usage
* Following game-development-oriented instructions

#### Summary

The model performs best when used as a Roblox development assistant and is not intended for general-purpose natural language tasks.

---

## Model Examination

No formal interpretability or probing analysis was conducted.

---

## Environmental Impact

Carbon emissions were not formally measured.

* **Hardware Type:** Consumer-grade GPU
* **Hours used:** < 24 hours
* **Cloud Provider:** None (local training)
* **Compute Region:** Not applicable
* **Carbon Emitted:** Not estimated

---

## Technical Specifications

### Model Architecture and Objective

* Decoder-only Transformer
* Next-token prediction objective
* LoRA adapters applied to attention layers

### Compute Infrastructure

#### Hardware

* Single consumer-grade GPU

#### Software

* PyTorch
* Transformers
* PEFT

---

## Citation

**BibTeX:**

```bibtex
@misc{darwinkernelpanic2025luau,
  title={CodeLlama 7B Instruct Luau LoRA},
  author={darwinkernelpanic},
  year={2025},
  howpublished={Hugging Face},
  note={LoRA fine-tuned for Luau / Roblox scripting}
}
```

**APA:**

darwinkernelpanic. (2025). *CodeLlama 7B Instruct Luau LoRA*. Hugging Face.

---

## Model Card Authors

darwinkernelpanic

## Model Card Contact

Use the Hugging Face repository issues or the author’s profile.

---

### Framework versions

* PEFT 0.18.0