Instructions to use UX4567/Khushi-Business-AI-4bit 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/Khushi-Business-AI-4bit 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/Khushi-Business-AI-4bit:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf UX4567/Khushi-Business-AI-4bit: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/Khushi-Business-AI-4bit:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf UX4567/Khushi-Business-AI-4bit: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/Khushi-Business-AI-4bit:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf UX4567/Khushi-Business-AI-4bit: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/Khushi-Business-AI-4bit:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf UX4567/Khushi-Business-AI-4bit:Q4_K_M

Use Docker

docker model run hf.co/UX4567/Khushi-Business-AI-4bit:Q4_K_M

LM Studio
Jan

vLLM

How to use UX4567/Khushi-Business-AI-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UX4567/Khushi-Business-AI-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UX4567/Khushi-Business-AI-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UX4567/Khushi-Business-AI-4bit:Q4_K_M

Ollama
How to use UX4567/Khushi-Business-AI-4bit with Ollama:
```
ollama run hf.co/UX4567/Khushi-Business-AI-4bit:Q4_K_M
```
Unsloth Desktop

How to use UX4567/Khushi-Business-AI-4bit with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf UX4567/Khushi-Business-AI-4bit: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/Khushi-Business-AI-4bit:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use UX4567/Khushi-Business-AI-4bit with Docker Model Runner:
```
docker model run hf.co/UX4567/Khushi-Business-AI-4bit:Q4_K_M
```

Lemonade

How to use UX4567/Khushi-Business-AI-4bit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull UX4567/Khushi-Business-AI-4bit:Q4_K_M

Run and chat with the model

lemonade run user.Khushi-Business-AI-4bit-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use UX4567/Khushi-Business-AI-4bit 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/Khushi-Business-AI-4bit: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/Khushi-Business-AI-4bit:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use UX4567/Khushi-Business-AI-4bit with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf UX4567/Khushi-Business-AI-4bit: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/Khushi-Business-AI-4bit: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"

Khushi-Business-AI-4bit (7B)

A 4-bit quantized, business-focused conversational AI built on Mistral 7B using Unsloth. Optimized for low VRAM (runs on 6GB+ VRAM) and for Indian business use-cases.

Built with ❤️ by Kartik Sharma | UX4567 17+ models | 1200+ downloads | Low-VRAM Specialist

Why this model?

Made with Unsloth: 2x faster fine-tuning & quantization
7B params in 4-bit (BF16 base): ~4GB RAM me chal jayega
Business Tuned: Customer support, sales chat, lead handling
Bilingual: Hindi + English (Hinglish) samajhta hai
Two formats: safetensors + GGUF for Ollama / LM Studio

How to use (Transformers + Unsloth)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "UX4567/Khushi-Business-AI-4bit",
    max_seq_length = 2048,
    load_in_4bit = True,
)

prompt = "Ek customer bol raha hai: Mujhe refund chahiye, kya reply du?"
inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 200)
print(tokenizer.batch_decode(outputs)[0])

Downloads last month: 271

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for UX4567/Khushi-Business-AI-4bit

Base model

mistralai/Mistral-7B-Instruct-v0.2

Quantized

(111)

this model