TXB-Qwen2.5-0.5B-LoRA-v1

Model Name

TXB-Qwen2.5-0.5B-LoRA-v1

Developed By

TensoraMax Studio

Base Model

Qwen/Qwen2.5-0.5B

Model Type

LoRA Adapter

Language

English

Version

v1

Status

Experimental Release

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About TXB

TXB (TensoraMax Knowledge Base) is a long-term initiative within TensoraMax Studio focused on creating a structured AI knowledge ecosystem.

The project aims to:

• Organize technical knowledge
• Assist development workflows
• Support project documentation
• Provide planning assistance
• Preserve ecosystem knowledge
• Enable future AI-powered development tools

Future TXB releases are expected to expand dataset size, improve reasoning quality, and integrate deeper ecosystem knowledge.

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Intended Uses

Direct Use

This model can be used for:

• Question answering
• Documentation assistance
• Development planning
• Technical explanations
• Knowledge retrieval
• AI assistant experimentation

Example Questions

• What is TensoraMax Studio?
• How should I structure a project roadmap?
• Explain LoRA fine-tuning.
• Help create project documentation.
• Generate development ideas.

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Out-of-Scope Uses

This model is not intended for:

• Medical advice
• Legal advice
• Financial advice
• Safety-critical systems
• Autonomous decision making
• High-risk professional environments

Outputs should always be reviewed by a human before use in important situations.

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Training Details

Training Dataset

Custom TXB dataset created for the TensoraMax Studio ecosystem.

Dataset characteristics include:

• Question and answer pairs
• Documentation-style content
• Development workflows
• Technical explanations
• Knowledge base entries
• Project planning examples

Dataset Size

Approximately 680 training samples.

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Training Method

Parameter Efficient Fine-Tuning (PEFT)

LoRA Configuration:

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Rank (r): 16
Alpha: 32
Dropout: 0.05
Task Type: CAUSAL_LM

Target Modules:

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q_proj
k_proj
v_proj
o_proj

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Training Hyperparameters

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Epochs: 3
Learning Rate: 2e-4
Batch Size: 2
Gradient Accumulation Steps: 4
FP16 Training: Enabled

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Training Infrastructure

Hardware

Tesla T4 GPU

Platform

Kaggle Notebooks

Frameworks

• Transformers
• PEFT
• TRL
• Datasets
• PyTorch

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Training Results

Training completed successfully.

Final training statistics:

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Global Steps: 255
Epochs Completed: 3
Training Loss: ~2.24
Mean Token Accuracy: ~58%

These metrics should be considered preliminary due to the small dataset size and experimental nature of the project.

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Model Architecture

This repository contains a LoRA adapter trained on top of:

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Qwen/Qwen2.5-0.5B

The adapter weights modify a small subset of model parameters while preserving the original base model.

Users must load the adapter together with the original Qwen2.5-0.5B model.

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Usage

Install Dependencies

bash
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pip install transformers peft torch accelerate

Load Model

python
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from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_name = "Qwen/Qwen2.5-0.5B"
adapter_name = "YuRiVeRTi/TXB-Qwen2.5-0.5B-LoRA-v1"

tokenizer = AutoTokenizer.from_pretrained(base_model_name)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto"
)

model = PeftModel.from_pretrained(
    base_model,
    adapter_name
)

Example Inference

python
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prompt = "What is TXB?"

inputs = tokenizer(
    prompt,
    return_tensors="pt"
)

outputs = model.generate(
    **inputs,
    max_new_tokens=128
)

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

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Limitations

Current limitations include:

• Small training dataset
• Limited domain coverage
• Experimental training setup
• Potential hallucinations
• Limited reasoning depth
• Early-stage alignment

Performance will vary depending on prompt quality and topic relevance.

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Future Roadmap

Planned future improvements:

• Larger datasets
• Better instruction tuning
• Improved reasoning capabilities
• Expanded ecosystem knowledge
• TXB multi-domain support
• TXB Copilot integration
• TensoraMax Studio ecosystem integration

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Acknowledgements

Special thanks to:

• Qwen Team
• Hugging Face
• Kaggle
• Open-source AI community

for providing the tools and infrastructure that made this project possible.

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Author

TensoraMax Studio

Project Lead: Yuri

Repository:

https://github.com/Yuri-code-dot/tensoramax-knowledge-base

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Version History

TXB-Qwen2.5-0.5B-LoRA-v1

• Initial public release
• First TXB experimental model
• Trained on custom TXB dataset
• Published on Hugging Face

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Disclaimer

This model is provided as-is for research, experimentation, and educational purposes.

No guarantees are made regarding correctness, reliability, safety, or fitness for any particular purpose.

Users assume full responsibility for any outputs generated by the model.