Instructions to use preferredev/Roblox-Coder-v2_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use preferredev/Roblox-Coder-v2_gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="preferredev/Roblox-Coder-v2_gguf", filename="Qwen3.5-4B.F16-mmproj.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use preferredev/Roblox-Coder-v2_gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M
Use Docker
docker model run hf.co/preferredev/Roblox-Coder-v2_gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use preferredev/Roblox-Coder-v2_gguf with Ollama:
ollama run hf.co/preferredev/Roblox-Coder-v2_gguf:Q4_K_M
- Unsloth Studio
How to use preferredev/Roblox-Coder-v2_gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for preferredev/Roblox-Coder-v2_gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for preferredev/Roblox-Coder-v2_gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for preferredev/Roblox-Coder-v2_gguf to start chatting
- Pi
How to use preferredev/Roblox-Coder-v2_gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "preferredev/Roblox-Coder-v2_gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use preferredev/Roblox-Coder-v2_gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default preferredev/Roblox-Coder-v2_gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use preferredev/Roblox-Coder-v2_gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf preferredev/Roblox-Coder-v2_gguf:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "preferredev/Roblox-Coder-v2_gguf:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use preferredev/Roblox-Coder-v2_gguf with Docker Model Runner:
docker model run hf.co/preferredev/Roblox-Coder-v2_gguf:Q4_K_M
- Lemonade
How to use preferredev/Roblox-Coder-v2_gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull preferredev/Roblox-Coder-v2_gguf:Q4_K_M
Run and chat with the model
lemonade run user.Roblox-Coder-v2_gguf-Q4_K_M
List all available models
lemonade list
| tags: | |
| - gguf | |
| - llama.cpp | |
| - unsloth | |
| - roblox | |
| - lua | |
| - luau | |
| license: mit | |
| language: | |
| - en | |
| base_model: | |
| - unsloth/Qwen3.5-4B-GGUF | |
| **/ Roblox_Coder V2** | |
| <aside> | |
| **Roblox-Coder V2** is a noticable improvement over the v1, focused on early-stage Roblox Luau coding model focused on secure, server-authoritative game systems, backend architecture, and production-style Roblox Studio workflows. | |
| </aside> | |
| ## / Model Overview | |
| **Roblox_Coder V2** is a fine-tuned version of **Qwen 3.5 4B**, trained specifically for **Roblox Studio development and Luau programming**. | |
| It is designed to act as a **Roblox backend + systems development assistant**, capable of generating structured, server-authoritative, and production-style Luau code for real Roblox game development workflows. | |
| | **Developed by** | preferredev | | |
| | --- | --- | | |
| | **Base model** | Qwen 3.5 4B Instruct | | |
| | **Fine-tuning method** | QLoRA, 4-bit nf4, double quantization | | |
| | **License** | Apache 2.0 | | |
| | **Language(s)** | English, Luau — Roblox-specific fork of Lua | | |
| | **Dataset size** | 3,016 instruction examples | | |
| | **Train / validation split** | 2,666 train / 295 validation | | |
| | **Dataset focus** | Server-authoritative Roblox systems, secure networking, DataStores, gameplay systems, and production-style Luau | | |
| ## / Model Capabilities | |
| This model is optimized for **Roblox systems engineering** and performs best on backend-heavy development tasks. | |
| The model was trained on an instruction dataset focused on high-quality Roblox systems design, covering backend architecture, secure scripting patterns, scalable game development workflows, and common Roblox services. | |
| - Server-authoritative game architecture | |
| - DataStore systems and persistence | |
| - RemoteEvent / RemoteFunction networking | |
| - Secure combat and anti-exploit patterns | |
| - NPC AI and Pathfinding systems | |
| - Inventory, shop, economy, and progression systems | |
| - UI frameworks and Roblox client systems | |
| - Performance-aware Luau scripting patterns | |
| - ModuleScript APIs, service-style architecture, and bootstrap loaders | |
| - Debugging and refactoring insecure or outdated Roblox code | |
| ## / Core Strengths | |
| - Generates structured Luau systems, including services, modules, classes, and framework-style code | |
| - Strong understanding of **client-server separation** | |
| - Produces secure **server-authoritative gameplay logic** | |
| - Implements **DataStore-backed progression systems** with `pcall`, retry/backoff, and save-on-leave patterns | |
| - Builds scalable **inventory, shop, currency, and economy systems** | |
| - Designs **NPC AI with PathfindingService** and simple finite-state-machine behavior | |
| - Handles **RemoteEvent validation, RemoteFunction safety, rate limiting, and anti-exploit logic** | |
| - Creates modular Roblox architecture patterns, including service-based design and shared network wrappers | |
| - Avoids deprecated Roblox/Luau patterns such as `wait`, `spawn`, `delay`, `:remove()`, and direct `game.Workspace` usage where modern alternatives are better | |
| ## / Advanced Behavior | |
| - Enforces anti-exploit security by default in generated code | |
| - Prefers scalable architecture over quick one-off scripts | |
| - Uses strict typing patterns where applicable, including `--!strict` | |
| - Encourages server-side validation for all critical gameplay, economy, combat, and inventory logic | |
| - Produces production-style Luau structure rather than beginner-only scripts | |
| - Tends to include Roblox service setup, validation helpers, cooldowns, debounces, and safe persistence patterns | |
| - Refactors insecure client-trust patterns into server-authoritative implementations | |
| ## / Known Limitations | |
| <aside> | |
| ⚠️ | |
| This is an early v1 fine-tune. Outputs should be reviewed, tested in Roblox Studio, and validated with real Luau tooling before production use. | |
| </aside> | |
| - Not optimized for animation, VFX, or art-heavy systems | |
| - Limited coverage of Studio UI/UX design workflows | |
| - May over-engineer simple tasks into full systems | |
| - Dataset is mostly synthetic/template-generated, so outputs may sometimes share a similar style or structure | |
| - May add security, validation, or persistence boilerplate even when a simpler snippet would be enough | |
| - Not guaranteed to understand newer or less-common Roblox APIs outside the training coverage | |
| - Weaker on large multi-file architecture, complex typed Luau generics, and long debugging tasks requiring state tracing across many files | |
| - Not a replacement for real testing, code review, exploit testing, or Roblox Studio validation | |
| ## / System Requirements | |
| This model can run locally in **GGUF** or other quantized formats using compatible runtimes. | |
| Supported local runtimes include: | |
| - llama.cpp | |
| - LM Studio | |
| - Ollama | |
| - KoboldCpp | |
| - Other GGUF-compatible inference backends | |
| ## / Model Sizes | |
| | Quantization | Size | Recommended Use | | |
| | --- | --- | --- | | |
| | Q4_K_M | ~2.78 GB | Fast inference for low-VRAM or CPU-based systems | | |
| | Q5_K_M | ~3.16 GB | Balanced quality and performance | | |
| | Q8_0 | ~4.61 GB | Higher-quality inference for stronger local hardware | | |
| ## / Hardware Requirements | |
| ### Minimum | |
| - 8 GB system RAM | |
| - CPU inference supported | |
| - GGUF runtime such as llama.cpp, LM Studio, or Ollama | |
| ### Recommended | |
| - 12–16 GB RAM or VRAM | |
| - GPU acceleration, NVIDIA preferred | |
| - Fast SSD for model loading | |
| ### Optimal | |
| - 8 GB+ VRAM GPU for smooth Q8 inference | |
| - CUDA-enabled inference backend | |
| - Enough memory headroom for longer prompts and larger Roblox code generations | |
| ## / Model & Links | |
| - Hugging Face: https://huggingface.co/preferredev/Roblox-Coder-v2_gguf | |
| - GitHub: https://github.com/preferredev/Roblox-Coder-v2 | |
| ## / Intended Use | |
| This model is intended for: | |
| - Roblox Studio developers | |
| - Luau backend system design | |
| - Secure Roblox architecture learning | |
| - Rapid prototyping of Roblox game systems | |
| - Server-side gameplay systems | |
| - DataStore-backed progression systems | |
| - Combat, inventory, shop, NPC, and economy logic | |
| - Refactoring insecure Roblox scripts into safer server-authoritative code | |
| ## / Example Prompts | |
| ### Roblox backend systems | |
| - “Create a secure server-side shop system where players can buy tools using coins.” | |
| - “Write a DataStore-backed player profile system with retries and save-on-leave.” | |
| - “Build a server-authoritative inventory module with add, remove, has, and capacity limits.” | |
| ### Networking and anti-exploit | |
| - “Wire up a RemoteEvent for buying an item, but validate the price and ownership on the server.” | |
| - “Add a rate limit so a player can only fire AttackRemote 5 times per second.” | |
| - “Fix this OnServerEvent that trusts a client-supplied coin amount.” | |
| ### NPCs and gameplay | |
| - “Create an NPC that chases the nearest player within 40 studs using PathfindingService.” | |
| - “Write a server-side melee weapon with range checks, cooldowns, and validated hit detection.” | |
| - “Build a raycast gun with server-side hit validation and ammo tracking.” | |
| ## / Evaluation Guidance | |
| Recommended evaluation before production use: | |
| - Run generated code through a real Luau checker such as `luau-analyze` or `selene` | |
| - Test generated systems inside Roblox Studio | |
| - Scan for deprecated or unsafe patterns such as `wait(`, `spawn(`, `delay(`, `:remove()`, and insecure client-trust logic | |
| - Red-team networking prompts to ensure the model does not trust client-supplied currency, inventory, damage, or ownership state | |
| - Compare outputs against the base model and earlier fine-tune versions for parse validity, security, and task success | |
| ## / Safety & Security Notes | |
| Roblox_Coder is designed to prefer secure, server-authoritative implementations. However, generated code should still be reviewed carefully. | |
| For production Roblox games: | |
| - Never trust client-supplied economy, combat, inventory, or ownership data | |
| - Validate all RemoteEvent and RemoteFunction arguments on the server | |
| - Rate-limit sensitive remotes | |
| - Wrap DataStore calls in `pcall` | |
| - Add retry/backoff behavior for persistence | |
| - Test edge cases, exploit attempts, and failure modes manually | |
| ## / Notes | |
| This is the **v2 fine-tune**, | |
| Future versions may expand dataset coverage to animation systems, tooling, UI frameworks, full game development pipelines, typed Luau, multi-file project architecture, and more diverse real-world Roblox code examples. | |
| <aside> | |
| 📌 | |
| Bottom line: **Roblox_Coder V2** should be treated as a strong Roblox backend and systems-coding assistant for common server-authoritative Luau tasks, but all generated code should be tested, reviewed, and validated before production use. | |
| </aside> |