---
library_name: transformers
model_name: qwen3_0.6b_fft
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
 
 

 # Qwen3-0.6B Fine-Tuning Project - Tech3Space + Spiritual Knowledge

This repository contains the complete pipeline for fine-tuning **Qwen3-0.6B** on https://ollama.com/ankitkushwahahacker9910921/tech3space-pro custom knowledge about https://tech3space.com/  **AnkitKushwaha90**, **Tech3Space**, and **Kundalini Spiritual Dataset**.

## 📋 Table of Contents
- [Project Overview](#project-overview)
- [Prerequisites](#prerequisites)
- [1. Dataset Generation](#1-dataset-generation)
- [2. Full Fine-Tuning](#2-full-fine-tuning)
- [3. Model Conversion to GGUF](#3-model-conversion-to-gguf)
- [4. Inference / Usage](#4-inference--usage)
- [Directory Structure](#directory-structure)
- [Troubleshooting](#troubleshooting)

---

## Project Overview

- **Base Model**: Qwen3-0.6B
- **Knowledge Domains**:
  - AnkitKushwaha90 (Cybersecurity, AI Researcher)
  - Tech3Space Platform
  - Hugging Face Profile
  - **New**: In-depth Kundalini Energy, 7 Chakras, 27 Nakshatras, 12 Rashis, Hindi Months, Awakening Practices

The model is trained to respond accurately about these topics using SFT (Supervised Fine-Tuning).

---

## Prerequisites

```bash
conda create -n safetensors python=3.11
conda activate safetensors

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install transformers datasets trl accelerate
pip install huggingface_hub
```
# For GGUF:
```bash
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make clean && LLAMA_CUDA=1 make -j
```

# 🚀 Tech3Space-FT

### Full Fine-Tuned Large Language Model by Tech3Space

[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)]()
[![Transformers](https://img.shields.io/badge/Transformers-Compatible-yellow)]()
[![PyTorch](https://img.shields.io/badge/PyTorch-2.x-red)]()
[![Model Type](https://img.shields.io/badge/Model-Full%20Fine--Tuned-green)]()

---

## 🌟 Overview

**Tech3Space-FT** is a fully fine-tuned Large Language Model designed to demonstrate the power of domain adaptation, instruction following, reasoning, coding assistance, and knowledge enhancement through Full Fine-Tuning (FFT).

Unlike parameter-efficient methods such as LoRA, this model has been trained by updating **all model parameters**, enabling deeper adaptation to the target dataset and learning objectives.

The goal of this project is not only to build a capable AI assistant but also to inspire researchers, students, developers, and AI enthusiasts to explore the complete lifecycle of modern LLM training.

---

## ✨ Why This Model?

Building an AI model is more than training weights.

It is about:

* Understanding Transformer architectures
* Learning tokenization strategies
* Managing datasets at scale
* Optimizing GPU resources
* Evaluating model behavior
* Contributing to the open-source AI ecosystem

Tech3Space-FT represents that journey.

---

## 🎯 Key Features

✅ Full Fine-Tuning (All Parameters Updated)

✅ Instruction Following

✅ Natural Language Understanding

✅ Code Generation Support

✅ Research Assistance

✅ Educational Use Cases

✅ Hugging Face Transformers Compatible

✅ PyTorch Compatible

---

## 🏗 Training Method

### Full Fine-Tuning (FFT)

During training:

* All model weights were updated.
* No LoRA adapters were used.
* No PEFT layers were used.
* The complete model learned from the training dataset.

This allows the model to:

* Adapt more deeply to specialized domains.
* Improve consistency.
* Learn new patterns directly within base weights.
* Achieve stronger domain-specific performance.

---

## 📊 Training Configuration

| Parameter       | Value                    |
| --------------- | ------------------------ |
| Training Method | Full Fine-Tuning         |
| Framework       | PyTorch                  |
| Trainer         | TRL SFTTrainer           |
| Precision       | BF16 / FP16              |
| Architecture    | Transformer              |
| Optimizer       | AdamW                    |
| Learning Rate   | Custom                   |
| Dataset Format  | JSON Instruction Dataset |

---

## 📚 Dataset

The model was trained on carefully curated instruction-response examples.

Dataset characteristics:

* Instruction Tuning
* Question Answering
* Educational Content
* Technical Discussions
* Coding Tasks
* Reasoning Tasks

Example:

```json
{
  "instruction": "Explain machine learning.",
  "output": "Machine learning is a branch of AI that enables systems to learn from data."
}
```

---

## 🚀 Quick Start

### Installation

```bash
pip install transformers torch accelerate
```

### Load Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = "Tech3Space/tech3space3-0.6B"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Explain neural networks."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256
)

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

---

## 💻 Example Usage

### Coding Assistant

```text
User:
Write a Python function to reverse a string.

Assistant:
def reverse_string(text):
    return text[::-1]
```

### Educational Assistant

```text
User:
What is Kubernetes?

Assistant:
Kubernetes is an open-source container orchestration platform...
```

### Research Assistant

```text
User:
Explain transformer architecture.

Assistant:
Transformers use self-attention mechanisms to process sequences...
```

---

## 📈 Potential Applications

* AI Research
* Education
* Coding Assistance
* Knowledge Retrieval
* Chatbots
* Automation
* Experimentation
* Fine-Tuning Research

---

## ⚠️ Limitations

Like all language models:

* Responses may contain inaccuracies.
* Generated content should be verified.
* Performance depends on training data quality.
* Not intended for high-risk decision making.

---

## 🤝 Contributing

Contributions are welcome.

Ways to contribute:

* Improve datasets
* Enhance evaluation methods
* Report issues
* Submit pull requests
* Share benchmarks

Together we can build stronger open-source AI systems.

---

## 🔬 Research Philosophy

We believe that understanding AI comes from building it.

Every experiment, dataset, training run, and failure contributes to deeper knowledge.

Tech3Space-FT is part of that learning journey.

If this project helps you start your own LLM research, then it has already achieved its purpose.

---

## 🙏 Acknowledgements

Special thanks to:

* Hugging Face
* PyTorch Community
* TRL
* Transformers Library
* Open Source AI Researchers

for making modern AI development accessible to everyone.

---

## 📜 License

Released under the Apache 2.0 License.

Please verify the license before commercial deployment.

---

# ⭐ Support the Project

If you find this model useful:

* Star the repository
* Share your results
* Contribute improvements
* Build something amazing

### "The future of AI belongs to those who learn by building."

— Tech3Space