library_name, model_name, tags, licence
| library_name | model_name | tags | licence | |||
|---|---|---|---|---|---|---|
| transformers | qwen3_0.6b_fft |
|
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
- Prerequisites
- 1. Dataset Generation
- 2. Full Fine-Tuning
- 3. Model Conversion to GGUF
- 4. Inference / Usage
- Directory Structure
- 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
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:
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
🌟 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:
{
"instruction": "Explain machine learning.",
"output": "Machine learning is a branch of AI that enables systems to learn from data."
}
🚀 Quick Start
Installation
pip install transformers torch accelerate
Load Model
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
User:
Write a Python function to reverse a string.
Assistant:
def reverse_string(text):
return text[::-1]
Educational Assistant
User:
What is Kubernetes?
Assistant:
Kubernetes is an open-source container orchestration platform...
Research Assistant
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