Instructions to use UX4567/Kartik-Kundli-AI-3B-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use UX4567/Kartik-Kundli-AI-3B-v2.0 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 UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf UX4567/Kartik-Kundli-AI-3B-v2.0: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 UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf UX4567/Kartik-Kundli-AI-3B-v2.0: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 UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M

Use Docker

docker model run hf.co/UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M

LM Studio
Jan
Ollama
How to use UX4567/Kartik-Kundli-AI-3B-v2.0 with Ollama:
```
ollama run hf.co/UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M
```
Unsloth Desktop

How to use UX4567/Kartik-Kundli-AI-3B-v2.0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use UX4567/Kartik-Kundli-AI-3B-v2.0 with Docker Model Runner:
```
docker model run hf.co/UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M
```

Lemonade

How to use UX4567/Kartik-Kundli-AI-3B-v2.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M

Run and chat with the model

lemonade run user.Kartik-Kundli-AI-3B-v2.0-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use UX4567/Kartik-Kundli-AI-3B-v2.0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf UX4567/Kartik-Kundli-AI-3B-v2.0: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 UX4567/Kartik-Kundli-AI-3B-v2.0:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use UX4567/Kartik-Kundli-AI-3B-v2.0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf UX4567/Kartik-Kundli-AI-3B-v2.0: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 "UX4567/Kartik-Kundli-AI-3B-v2.0: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"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

library_name: transformers tags: - kundli - astrology - hindi - vedic-astrology - qwen3 - fine-tuned - gguf - astrology-ai base_model: Qwen/Qwen2.5-3B-Instruct language: - hi - en license: mit

Kartik-Kundli-AI-3B-v2.0 🔮

India's Own Vedic Astrology AI - Ab aapka Kundli Expert aapke laptop pe!

Ye model Vedic Astrology, Kundli, Rashi, Nakshatra, Dasha pe specially fine-tune kiya gaya hai. 3B size ka hai isliye aapke laptop / mobile pe bhi tez chalega.

Model Details

Developed by: Kartik (UX4567)
Model Type: Qwen 3B Instruct Fine-Tuned
Finetuned from: Qwen/Qwen2.5-3B-Instruct
Language: Hindi + English (Hinglish Support)
Version: v2.0 - More Accurate Predictions
GGUF Available: Yes, for llama.cpp

Model Sources

GGUF Version: [Check Files -> GGUF]
Demo: Coming Soon

Uses

Direct Use

Aap isse puch sakte ho:

"Meri Mesh rashi ka aaj ka bhavishya kya hai?"
"Kundli me Mangal Dosh kya hota hai?"
"Mere janm nakshatra ka matlab kya hai?"
"Shani ki sade sati kab khatam hogi?"

Best For

Vedic Astrology Q&A
Kundli Explanation in simple Hindi
Daily Horoscope / Rashi Predictions
Offline Astrology App (via GGUF)

Out-of-Scope Use

Ye model Medical, Legal ya Financial advice ke liye nahi hai. Ye sirf educational astrology purpose ke liye hai.

How to Get Started

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "UX4567/Kartik-Kundli-AI-3B-v2.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "Meri rashi Kumbh hai, mera

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Safetensors

Model size

3B params

Tensor type

F16

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