Instructions to use ankitkushwaha90/tech3space3-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ankitkushwaha90/tech3space3-0.6B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ankitkushwaha90/tech3space3-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ankitkushwaha90/tech3space3-0.6B")
model = AutoModelForCausalLM.from_pretrained("ankitkushwaha90/tech3space3-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use ankitkushwaha90/tech3space3-0.6B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ankitkushwaha90/tech3space3-0.6B",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ankitkushwaha90/tech3space3-0.6B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ankitkushwaha90/tech3space3-0.6B
# Run inference directly in the terminal:
llama cli -hf ankitkushwaha90/tech3space3-0.6B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ankitkushwaha90/tech3space3-0.6B
# Run inference directly in the terminal:
llama cli -hf ankitkushwaha90/tech3space3-0.6B

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ankitkushwaha90/tech3space3-0.6B
# Run inference directly in the terminal:
./llama-cli -hf ankitkushwaha90/tech3space3-0.6B

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ankitkushwaha90/tech3space3-0.6B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ankitkushwaha90/tech3space3-0.6B

Use Docker

docker model run hf.co/ankitkushwaha90/tech3space3-0.6B

LM Studio
Jan

vLLM

How to use ankitkushwaha90/tech3space3-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ankitkushwaha90/tech3space3-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ankitkushwaha90/tech3space3-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ankitkushwaha90/tech3space3-0.6B

SGLang

How to use ankitkushwaha90/tech3space3-0.6B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ankitkushwaha90/tech3space3-0.6B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ankitkushwaha90/tech3space3-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ankitkushwaha90/tech3space3-0.6B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ankitkushwaha90/tech3space3-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ankitkushwaha90/tech3space3-0.6B with Ollama:
```
ollama run hf.co/ankitkushwaha90/tech3space3-0.6B
```

Unsloth Studio

How to use ankitkushwaha90/tech3space3-0.6B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ankitkushwaha90/tech3space3-0.6B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ankitkushwaha90/tech3space3-0.6B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ankitkushwaha90/tech3space3-0.6B to start chatting

How to use ankitkushwaha90/tech3space3-0.6B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ankitkushwaha90/tech3space3-0.6B

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ankitkushwaha90/tech3space3-0.6B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ankitkushwaha90/tech3space3-0.6B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ankitkushwaha90/tech3space3-0.6B

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ankitkushwaha90/tech3space3-0.6B

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ankitkushwaha90/tech3space3-0.6B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ankitkushwaha90/tech3space3-0.6B

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ankitkushwaha90/tech3space3-0.6B" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ankitkushwaha90/tech3space3-0.6B with Docker Model Runner:
```
docker model run hf.co/ankitkushwaha90/tech3space3-0.6B
```

Lemonade

How to use ankitkushwaha90/tech3space3-0.6B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ankitkushwaha90/tech3space3-0.6B

Run and chat with the model

lemonade run user.tech3space3-0.6B-{{QUANT_TAG}}

List all available models

lemonade list

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.

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

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Safetensors

Model size

0.6B params

Tensor type

BF16