Text Generation
Transformers
Safetensors
Luba-Lulua
qwen2
fivem
qbcore
gta-v
gta5
game-development
code-generation
code
qwen2.5
qwen-coder
fine-tuned
conversational
text-generation-inference
Instructions to use lone17k/Rooja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lone17k/Rooja with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lone17k/Rooja") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lone17k/Rooja") model = AutoModelForCausalLM.from_pretrained("lone17k/Rooja", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lone17k/Rooja with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lone17k/Rooja" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lone17k/Rooja", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lone17k/Rooja
- SGLang
How to use lone17k/Rooja with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "lone17k/Rooja" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lone17k/Rooja", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "lone17k/Rooja" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lone17k/Rooja", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use lone17k/Rooja with Docker Model Runner:
docker model run hf.co/lone17k/Rooja
| base_model: | |
| - Qwen/Qwen2.5-7B-Instruct | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| language: | |
| - lua | |
| license: apache-2.0 | |
| tags: | |
| - fivem | |
| - qbcore | |
| - lua | |
| - gta-v | |
| - gta5 | |
| - game-development | |
| - code-generation | |
| - code | |
| - qwen2 | |
| - qwen2.5 | |
| - qwen-coder | |
| - fine-tuned | |
| # Rooja | |
| **Rooja** is a fine-tuned version of | |
| **Qwen2.5-7B-Instruct**, specialized for **FiveM development, | |
| Lua scripting, QBCore, and GTA V server development**. | |
| Rooja is designed to act as a coding assistant for developers building | |
| and maintaining FiveM resources. | |
| ## Model Details | |
| | Property | Value | | |
| |---|---| | |
| | Model | Rooja | | |
| | Base Model | Qwen/Qwen2.5-7B-Instruct | | |
| | Parameters | ~7B | | |
| | Fine-tuning Method | QLoRA | | |
| | LoRA Rank | 64 | | |
| | LoRA Alpha | 128 | | |
| | LoRA Dropout | 0.05 | | |
| | Quantization During Training | 4-bit NF4 | | |
| | Double Quantization | Enabled | | |
| | Maximum Training Sequence Length | 8192 | | |
| | Training Epochs | 2 | | |
| | Learning Rate | 1e-4 | | |
| | Effective Batch Size | 16 | | |
| | Optimizer | paged_adamw_8bit | | |
| | Learning Rate Scheduler | cosine | | |
| | Gradient Checkpointing | Enabled | | |
| ## Training Dataset | |
| The training run contained: | |
| - **5,969 total examples** | |
| - **4,460 training examples** | |
| - **1,509 validation examples** | |
| The dataset was created for FiveM-oriented coding and development tasks. | |
| ## Training Results | |
| Final training results: | |
| | Metric | Result | | |
| |---|---:| | |
| | Final Training Loss | 0.4496 | | |
| | Final Training Token Accuracy | ~91.6% | | |
| | Final Validation Loss | 0.5274 | | |
| | Final Validation Token Accuracy | ~88.0% | | |
| | Epochs | 2 | | |
| The training run completed successfully after 2 epochs. | |
| ## What Rooja Is Designed For | |
| Rooja is intended to help with: | |
| - FiveM Lua development | |
| - QBCore scripting | |
| - GTA V server development | |
| - Client-side Lua | |
| - Server-side Lua | |
| - FiveM resources | |
| - `fxmanifest.lua` | |
| - QBCore events and callbacks | |
| - Player and character systems | |
| - Server/client communication | |
| - Configuration files | |
| - Debugging | |
| - Code explanation | |
| - Code generation | |
| - Resource architecture | |
| - FiveM development workflows | |
| ## Example Prompt | |
| ```text | |
| Create a QBCore FiveM server-side command that gives | |
| cash to another player. | |
| Validate the target player and amount and make sure the | |
| command cannot be abused with invalid values. | |
| ``` | |
| ## Usage | |
| ### Transformers | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| model_id = "lone17k/Rooja" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| torch_dtype="auto", | |
| device_map="auto" | |
| ) | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": "Create a basic FiveM QBCore server-side command." | |
| } | |
| ] | |
| text = tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True | |
| ) | |
| inputs = tokenizer( | |
| text, | |
| return_tensors="pt" | |
| ).to(model.device) | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=512, | |
| temperature=0.2 | |
| ) | |
| response = tokenizer.decode( | |
| outputs[0][inputs["input_ids"].shape[-1]:], | |
| skip_special_tokens=True | |
| ) | |
| print(response) | |
| ``` | |
| ## Recommended Generation Settings | |
| For code generation, a low temperature is recommended. | |
| ```text | |
| temperature: 0.1 - 0.3 | |
| top_p: 0.8 - 0.95 | |
| ``` | |
| For deterministic coding: | |
| ```text | |
| temperature: 0.2 | |
| ``` | |
| ## Limitations | |
| Rooja may generate incorrect, incomplete, or outdated FiveM and | |
| QBCore APIs. | |
| FiveM resources and frameworks can change over time. Generated code | |
| should therefore be reviewed and tested before being deployed to a | |
| production server. | |
| Rooja should be treated as a coding assistant and not as an | |
| authoritative source of FiveM documentation. | |
| ## Base Model | |
| Rooja is based on: | |
| **Qwen/Qwen/Qwen2.5-7B-Instruct** | |
| The original Qwen/Qwen2.5-7B-Instruct model contains approximately | |
| 14.7B parameters and supports long-context usage. Its Hugging Face | |
| model card currently identifies the model as Apache-2.0 licensed. | |
| For the original model and its license, see: | |
| https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct | |
| ## Creator | |
| Created and fine-tuned by **Lone17k**. | |
| Hugging Face: | |
| https://huggingface.co/lone17k | |
| Model: | |
| https://huggingface.co/lone17k/Rooja | |
| ## Disclaimer | |
| This project is an independent fine-tune and is not affiliated with | |
| Qwen, Alibaba Cloud, FiveM, or Rockstar Games. |