lone17k

Rooja

README

License: apache-2.0

Model Details

Table with columns: Property, Value
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:

Table with columns: Metric, Result
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.

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Model Details

Model Provider

lone17k

Model Tree

Base

Qwen/Qwen2.5-7B-Instruct

Fine-tuned

this model

Input Modalities

Text

Output Modalities

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Supported Functionality

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README

License: apache-2.0

Model Details

Table with columns: Property, Value
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:

Table with columns: Metric, Result
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.