Instructions to use lone17k/Rooja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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

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.

temperature: 0.1 - 0.3
top_p: 0.8 - 0.95

For deterministic coding:

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