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Model: ankitkushwaha90/tech3space3-0.6B Source: Original Platform
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README.md
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library_name: transformers
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model_name: qwen3_0.6b_fft
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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---
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# Qwen3-0.6B Fine-Tuning Project - Tech3Space + Spiritual Knowledge
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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**.
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## 📋 Table of Contents
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- [Project Overview](#project-overview)
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- [Prerequisites](#prerequisites)
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- [1. Dataset Generation](#1-dataset-generation)
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- [2. Full Fine-Tuning](#2-full-fine-tuning)
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- [3. Model Conversion to GGUF](#3-model-conversion-to-gguf)
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- [4. Inference / Usage](#4-inference--usage)
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- [Directory Structure](#directory-structure)
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- [Troubleshooting](#troubleshooting)
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---
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## Project Overview
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- **Base Model**: Qwen3-0.6B
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- **Knowledge Domains**:
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- AnkitKushwaha90 (Cybersecurity, AI Researcher)
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- Tech3Space Platform
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- Hugging Face Profile
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- **New**: In-depth Kundalini Energy, 7 Chakras, 27 Nakshatras, 12 Rashis, Hindi Months, Awakening Practices
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The model is trained to respond accurately about these topics using SFT (Supervised Fine-Tuning).
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---
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## Prerequisites
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```bash
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conda create -n safetensors python=3.11
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conda activate safetensors
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
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pip install transformers datasets trl accelerate
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pip install huggingface_hub
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```
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# For GGUF:
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```bash
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git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make clean && LLAMA_CUDA=1 make -j
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```
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# 🚀 Tech3Space-FT
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### Full Fine-Tuned Large Language Model by Tech3Space
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[]()
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[]()
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[]()
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[]()
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---
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## 🌟 Overview
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**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).
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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.
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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.
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---
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## ✨ Why This Model?
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Building an AI model is more than training weights.
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It is about:
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* Understanding Transformer architectures
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* Learning tokenization strategies
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* Managing datasets at scale
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* Optimizing GPU resources
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* Evaluating model behavior
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* Contributing to the open-source AI ecosystem
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Tech3Space-FT represents that journey.
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---
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## 🎯 Key Features
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✅ Full Fine-Tuning (All Parameters Updated)
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✅ Instruction Following
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✅ Natural Language Understanding
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✅ Code Generation Support
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✅ Research Assistance
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✅ Educational Use Cases
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✅ Hugging Face Transformers Compatible
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✅ PyTorch Compatible
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---
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## 🏗 Training Method
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### Full Fine-Tuning (FFT)
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During training:
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* All model weights were updated.
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* No LoRA adapters were used.
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* No PEFT layers were used.
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* The complete model learned from the training dataset.
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This allows the model to:
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* Adapt more deeply to specialized domains.
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* Improve consistency.
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* Learn new patterns directly within base weights.
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* Achieve stronger domain-specific performance.
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---
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## 📊 Training Configuration
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| Parameter | Value |
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| --------------- | ------------------------ |
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| Training Method | Full Fine-Tuning |
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| Framework | PyTorch |
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| Trainer | TRL SFTTrainer |
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| Precision | BF16 / FP16 |
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| Architecture | Transformer |
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| Optimizer | AdamW |
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| Learning Rate | Custom |
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| Dataset Format | JSON Instruction Dataset |
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---
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## 📚 Dataset
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The model was trained on carefully curated instruction-response examples.
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Dataset characteristics:
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* Instruction Tuning
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* Question Answering
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* Educational Content
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* Technical Discussions
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* Coding Tasks
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* Reasoning Tasks
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Example:
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```json
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{
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"instruction": "Explain machine learning.",
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"output": "Machine learning is a branch of AI that enables systems to learn from data."
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}
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```
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---
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## 🚀 Quick Start
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### Installation
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```bash
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pip install transformers torch accelerate
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```
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### Load Model
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_path = "Tech3Space/tech3space3-0.6B"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype="auto",
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device_map="auto"
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)
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prompt = "Explain neural networks."
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=256
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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## 💻 Example Usage
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### Coding Assistant
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```text
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User:
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Write a Python function to reverse a string.
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Assistant:
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def reverse_string(text):
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return text[::-1]
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```
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### Educational Assistant
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```text
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User:
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What is Kubernetes?
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Assistant:
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Kubernetes is an open-source container orchestration platform...
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```
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### Research Assistant
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```text
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User:
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Explain transformer architecture.
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Assistant:
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Transformers use self-attention mechanisms to process sequences...
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```
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---
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## 📈 Potential Applications
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* AI Research
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* Education
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* Coding Assistance
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* Knowledge Retrieval
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* Chatbots
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* Automation
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* Experimentation
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* Fine-Tuning Research
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---
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## ⚠️ Limitations
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Like all language models:
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* Responses may contain inaccuracies.
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* Generated content should be verified.
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* Performance depends on training data quality.
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* Not intended for high-risk decision making.
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---
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## 🤝 Contributing
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Contributions are welcome.
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Ways to contribute:
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* Improve datasets
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* Enhance evaluation methods
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* Report issues
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* Submit pull requests
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* Share benchmarks
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Together we can build stronger open-source AI systems.
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---
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## 🔬 Research Philosophy
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We believe that understanding AI comes from building it.
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Every experiment, dataset, training run, and failure contributes to deeper knowledge.
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Tech3Space-FT is part of that learning journey.
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If this project helps you start your own LLM research, then it has already achieved its purpose.
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---
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## 🙏 Acknowledgements
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Special thanks to:
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* Hugging Face
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* PyTorch Community
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* TRL
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* Transformers Library
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* Open Source AI Researchers
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for making modern AI development accessible to everyone.
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---
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## 📜 License
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Released under the Apache 2.0 License.
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Please verify the license before commercial deployment.
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---
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# ⭐ Support the Project
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If you find this model useful:
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* Star the repository
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* Share your results
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* Contribute improvements
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* Build something amazing
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### "The future of AI belongs to those who learn by building."
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— Tech3Space
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