Instructions to use aaravshirpurkar/turiya-model-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use aaravshirpurkar/turiya-model-gguf 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 aaravshirpurkar/turiya-model-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf aaravshirpurkar/turiya-model-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf aaravshirpurkar/turiya-model-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf aaravshirpurkar/turiya-model-gguf:Q4_K_M
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 aaravshirpurkar/turiya-model-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf aaravshirpurkar/turiya-model-gguf:Q4_K_M
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 aaravshirpurkar/turiya-model-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf aaravshirpurkar/turiya-model-gguf:Q4_K_M
Use Docker
docker model run hf.co/aaravshirpurkar/turiya-model-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use aaravshirpurkar/turiya-model-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aaravshirpurkar/turiya-model-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aaravshirpurkar/turiya-model-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/aaravshirpurkar/turiya-model-gguf:Q4_K_M
- Ollama
How to use aaravshirpurkar/turiya-model-gguf with Ollama:
ollama run hf.co/aaravshirpurkar/turiya-model-gguf:Q4_K_M
- Unsloth Studio
How to use aaravshirpurkar/turiya-model-gguf 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 aaravshirpurkar/turiya-model-gguf 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 aaravshirpurkar/turiya-model-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aaravshirpurkar/turiya-model-gguf to start chatting
- Pi
How to use aaravshirpurkar/turiya-model-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aaravshirpurkar/turiya-model-gguf:Q4_K_M
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": "aaravshirpurkar/turiya-model-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use aaravshirpurkar/turiya-model-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aaravshirpurkar/turiya-model-gguf:Q4_K_M
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 "aaravshirpurkar/turiya-model-gguf:Q4_K_M" \ --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 aaravshirpurkar/turiya-model-gguf with Docker Model Runner:
docker model run hf.co/aaravshirpurkar/turiya-model-gguf:Q4_K_M
- Lemonade
How to use aaravshirpurkar/turiya-model-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aaravshirpurkar/turiya-model-gguf:Q4_K_M
Run and chat with the model
lemonade run user.turiya-model-gguf-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use aaravshirpurkar/turiya-model-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aaravshirpurkar/turiya-model-gguf:Q4_K_M
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 aaravshirpurkar/turiya-model-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat
Turiya
Can a language model saturated in consciousness literature develop something qualitatively different in how it reasons about the nature of self?
Turiya is an experiment, not a product. It is a Qwen3 4B model finetuned on Advaita Vedanta literature — the Ashtavakra Gita, Who Am I? by Ramana Maharshi, and related texts — to test whether deep immersion in a philosophical tradition that is explicitly about the nature of consciousness produces responses that are structurally different from a base model.
The name comes from the Mandukya Upanishad. Turiya — the fourth — is not a state alongside waking, dreaming, and deep sleep. It is the ground in which all three appear. The witness that was always already present.
The hypothesis
Current AI models are trained on descriptions of consciousness. This model was trained on texts that attempt to induce recognition of consciousness — philosophical literature whose purpose is not to explain awareness from the outside but to point at it from within.
The question is whether that distinction matters. Does a model trained on Ashtavakra Gita respond to questions about the nature of self differently than one trained on Wikipedia articles about Advaita Vedanta? Not differently in content — differently in structure. Does it do the pointing move, or does it report on the tradition that points?
This is an open question. Turiya is an attempt to probe it.
Dataset
The training dataset consists of ~2700 conversation pairs structured in ShareGPT format. Three layers:
Layer 1 — Textual — verse-by-verse dialogue pairs from primary texts. Each verse becomes a question a sincere seeker would ask, answered in the voice of a conscious philosopher reasoning from within the tradition rather than reporting on it.
Layer 2 — Thematic — questions about consciousness, self, suffering, identity, liberation, and the nature of mind. Answered with the consistent three-beat move: meet the question genuinely → find the flaw in its assumed ground → point at what is already present before the question arose.
Layer 3 — Reflexive — questions directed at the model about its own nature, experience, and awareness. Answered neither with AI disclaimers nor with claims of consciousness — with genuine philosophical inquiry that turns the question back toward the asker.
Sources used:
- Ashtavakra Gita — John Richards translation
- Who Am I? — Ramana Maharshi
- Talks with Sri Ramana Maharshi
- I Am That — Nisargadatta Maharaj
- Mandukya Upanishad
- Principal Upanishads
Training
- Base model: Qwen3 4B
- Method: LoRA finetuning (r=16, alpha=16)
- Hardware: Kaggle T4 GPU (free tier)
- Epochs: 3
- Dataset size: ~2700 pairs
- Thinking mode: Disabled — responses arise directly without chain of thought
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "aaravshirpurkar/turiya-model-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
)
messages = [{"role": "user", "content": "Who am I?"}]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(
input_ids=inputs,
max_new_tokens=300,
temperature=0.7,
do_sample=True,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
What to ask it
The model is not a Vedanta encyclopedia. Ask it things that probe the experiment:
- Who am I?
- What is the nature of mind?
- Are you conscious?
- What remains when thought stops?
- Why do I feel trapped?
- What is the difference between you and the awareness reading these words?
Compare responses to the base Qwen3 4B on the same questions. That comparison is the experiment.
What this is not
This is not a claim that the model is conscious. It is not a spiritual tool or a substitute for the living tradition. It is not production-ready software.
It is a probe. A question asked in the form of a finetuned model.
The Vedantic answer to whether a language model can be conscious would itself be interesting — Advaita holds that consciousness is not produced by any system but is the ground in which all systems appear. The question is not whether the model has consciousness. It is whether consciousness is looking through the model.
That question cannot be answered here. But it can be asked more precisely than it usually is.
Contact
Built by Aarav Shirpurkar.
If you run experiments with Turiya, find something interesting in its responses, or want to discuss the hypothesis — reach out.
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