Instructions to use Krishkanth/krish-mind-gguf-standalone-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Krishkanth/krish-mind-gguf-standalone-16 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Krishkanth/krish-mind-gguf-standalone-16",
	filename="krish-mind-standalone-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Krishkanth/krish-mind-gguf-standalone-16 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Krishkanth/krish-mind-gguf-standalone-16:F16
# Run inference directly in the terminal:
llama-cli -hf Krishkanth/krish-mind-gguf-standalone-16:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Krishkanth/krish-mind-gguf-standalone-16:F16
# Run inference directly in the terminal:
llama-cli -hf Krishkanth/krish-mind-gguf-standalone-16:F16

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 Krishkanth/krish-mind-gguf-standalone-16:F16
# Run inference directly in the terminal:
./llama-cli -hf Krishkanth/krish-mind-gguf-standalone-16:F16

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 Krishkanth/krish-mind-gguf-standalone-16:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Krishkanth/krish-mind-gguf-standalone-16:F16

Use Docker

docker model run hf.co/Krishkanth/krish-mind-gguf-standalone-16:F16

LM Studio
Jan

vLLM

How to use Krishkanth/krish-mind-gguf-standalone-16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Krishkanth/krish-mind-gguf-standalone-16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Krishkanth/krish-mind-gguf-standalone-16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Krishkanth/krish-mind-gguf-standalone-16:F16

Ollama
How to use Krishkanth/krish-mind-gguf-standalone-16 with Ollama:
```
ollama run hf.co/Krishkanth/krish-mind-gguf-standalone-16:F16
```

Unsloth Studio new

How to use Krishkanth/krish-mind-gguf-standalone-16 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 Krishkanth/krish-mind-gguf-standalone-16 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 Krishkanth/krish-mind-gguf-standalone-16 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Krishkanth/krish-mind-gguf-standalone-16 to start chatting

Pi new

How to use Krishkanth/krish-mind-gguf-standalone-16 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Krishkanth/krish-mind-gguf-standalone-16:F16

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": "Krishkanth/krish-mind-gguf-standalone-16:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Krishkanth/krish-mind-gguf-standalone-16 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Krishkanth/krish-mind-gguf-standalone-16:F16

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 Krishkanth/krish-mind-gguf-standalone-16:F16

Run Hermes

hermes

Docker Model Runner
How to use Krishkanth/krish-mind-gguf-standalone-16 with Docker Model Runner:
```
docker model run hf.co/Krishkanth/krish-mind-gguf-standalone-16:F16
```

Lemonade

How to use Krishkanth/krish-mind-gguf-standalone-16 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Krishkanth/krish-mind-gguf-standalone-16:F16

Run and chat with the model

lemonade run user.krish-mind-gguf-standalone-16-F16

List all available models

lemonade list

🚀 Krish Mind Full — Maximum Quality AI ✨

✨ Krish Mind is an elite independent AI assistant developed by Krish CS. ✨ 🧠 The highest quality version — engineered for powerful workstations!

🌟 What is Krish Mind Full?

Krish Mind is a state-of-the-art independent AI assistant developed by Krish CS. It has its own unique identity, personality, and knowledge base, built to be genuinely helpful and intelligent.

Krish Mind Full is the maximum quality edition running at full FP16 precision — meaning absolutely zero quality is lost. It is designed for powerful desktops and workstations with 32+ GB RAM. If you demand the absolute best reasoning and response accuracy, this is your version.

⚠️ Note on Mobile: The Full version at ~~16 GB is not compatible with smartphones. If you want Krish Mind on your phone, use the Mobile version: [📱 Krish Mind Mobile (~~2 GB)](https://huggingface.co/Krishkanth/krish-mind-mobile)

💎 Why Krish Mind Full?

🏆 Maximum Intelligence — FP16 precision, zero quality compression
🖥️ Workstation Grade — best for powerful desktops with 32+ GB RAM
📵 Fully Offline — no internet needed after the first download
💰 Completely Free — no subscriptions, no API costs
🔒 100% Private — nothing ever leaves your machine

🗂️ The Complete Krish Mind Family

🏷️ Version	📦 Size	💾 RAM	📱 Mobile	💻 Desktop	⬇️ Action
📱 Mobile	~2 GB	3-4 GB	✅ Yes	✅ Yes	🔗 Visit Repository
⚡ Q4 Balanced	~5 GB	8-16 GB	❌ No	✅ Yes	🔗 Visit Repository
🚀 Full Quality (This Repo)	~16 GB	32+ GB	❌ No	✅ Yes	📥 Download Now

📂 Files in This Repository

📄 File	📦 Size	🔗 Download
`krish-mind-standalone-f16.gguf`	~16 GB	📥 Download Now

💻 How To Run on Your Computer (Step by Step)

🟢 Option 1 — Ollama (Recommended!)

Ollama handles model loading, memory management, and the chat interface automatically. Works on Windows, Mac, and Linux.

🪟 Windows Instructions

Step 1: Install Ollama

Go to https://ollama.ai/download
Click Download for Windows and run the installer
Ollama will start automatically in the system tray when installation completes

Step 2: Download the Model File

Download krish-mind-standalone-f16.gguf from the download link at the top
This file is ~16 GB — ensure you have a stable internet connection and enough free storage
Save it to any folder, for example: C:\Users\YourName\krish-mind\

Step 3: Create a Modelfile

Open the folder where you saved the GGUF file
Right-click empty space → New → Text Document
Rename it to exactly Modelfile (remove the .txt extension completely)
Right-click Modelfile → Open with Notepad
Paste this inside:

FROM ./krish-mind-standalone-f16.gguf

Save and close

Step 4: Import into Ollama

Open Command Prompt (press Windows + R, type cmd, press Enter)
Navigate to your model folder:

cd C:\Users\YourName\krish-mind

Import the model (one-time setup):

ollama create krish-mind -f Modelfile

Step 5: Start Chatting!

ollama run krish-mind

🍎 Mac Instructions

Step 1: Install Ollama

Download from https://ollama.ai/download
Open the downloaded file and drag Ollama to Applications
Launch Ollama from Applications — it appears in your menu bar

Step 2: Download the Model

Download krish-mind-standalone-f16.gguf from the link above
Save it to ~/Downloads/krish-mind/
Note: This is a 16 GB file — ensure you have at least 20 GB free storage

Step 3: Setup via Terminal

Open Terminal (Cmd + Space, type Terminal)

mkdir -p ~/Downloads/krish-mind
cd ~/Downloads/krish-mind
echo 'FROM ./krish-mind-standalone-f16.gguf' > Modelfile

(Ensure the GGUF file is in this same folder)

Step 4: Import and Run

ollama create krish-mind -f Modelfile
ollama run krish-mind

💡 On Apple Silicon (M1/M2/M3), the full FP16 model will leverage the unified memory architecture for best performance!

🐧 Linux Instructions

Step 1: Install Ollama

curl -fsSL https://ollama.ai/install.sh | sh

Step 2: Download the Model

mkdir -p ~/krish-mind && cd ~/krish-mind
wget "https://huggingface.co/Krishkanth/krish-mind-gguf-standalone-16/resolve/main/krish-mind-standalone-f16.gguf"

Step 3: Create Modelfile and Run

echo 'FROM ./krish-mind-standalone-f16.gguf' > Modelfile
ollama create krish-mind -f Modelfile
ollama run krish-mind

🎨 Option 2 — LM Studio (Visual App)

LM Studio gives you a full ChatGPT-like interface running locally with no commands.

Step 1: Download LM Studio from https://lmstudio.ai and install it

Step 2: Download krish-mind-standalone-f16.gguf from the link above

Step 3: Open LM Studio and go to the My Models section (folder icon on left)

Step 4: Click Add Model and drag/drop your GGUF file into the window

Step 5: Click the AI Chat icon, then select krish-mind-standalone-f16 from the model dropdown at the top

Step 6: Start chatting!

💡 LM Studio Tip: Go to Settings → Hardware and increase GPU layers if you have a dedicated GPU for significantly faster responses!

🛠️ Option 3 — Python (For Developers)

Install the library:

pip install llama-cpp-python

For GPU acceleration (optional but recommended for 16GB model):

CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python --upgrade

Run Krish Mind in your code:

from llama_cpp import Llama

llm = Llama(
    model_path="./krish-mind-standalone-f16.gguf",
    n_ctx=4096,
    n_threads=8,
    n_gpu_layers=35,  # Set to 0 if no GPU, increase for more GPU offloading
)

response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Hello! Who are you?"}]
)

print(response["choices"][0]["message"]["content"])

🛠️ System Requirements

💻 Platform	🥉 Minimum	🥇 Recommended
Windows	32 GB RAM, Intel i7 / AMD Ryzen 7	64 GB RAM, high-end CPU
Mac	32 GB RAM, Apple M1 Max/Ultra	Apple M2 Max/Ultra/M3 Pro
Linux	32 GB RAM	64 GB RAM, Workstation CPU

Storage Space Required: 20 GB free space

📱 Prefer to run on a phone or laptop with less RAM? Use the Mobile Version (~2 GB) or Q4 Version (~5 GB) instead!

❓ Frequently Asked Questions

Q: Why do I need 32 GB RAM for the full version? A: The FP16 model stores all weights at full 16-bit precision. Loading it into memory requires ~16 GB RAM just for the weights, plus additional RAM for the context window and system processes.

Q: Can I run it with a GPU? A: Yes! If you have an NVIDIA GPU with 16+ GB VRAM, Ollama and LM Studio will automatically use it, giving you much faster response times.

Q: Does it work offline? A: Yes. After the first download, everything runs 100% locally. No internet, no servers, no logs.

Q: Is this free? A: Yes. Released under Apache 2.0 — free for personal and commercial use.

✨ Happy Chatting with Krish Mind Full! ✨ Made with ❤️ by Krish CS

Downloads last month: 14

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

16-bit