Instructions to use pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF",
	filename="Qwen3-4B-Hindi-v2.Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-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 pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-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 pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-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": "pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M

Ollama
How to use pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF with Ollama:
```
ollama run hf.co/pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-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 pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-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 pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF to start chatting

How to use pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-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": "pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-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 pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M
```

Lemonade

How to use pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-4B-Hindi-Instruct-v2-GGUF-Q4_K_M

List all available models

lemonade list

🇮🇳 Qwen3-4B Hindi Instruct v2 — GGUF

GGUF quantizations of Qwen3-4B-Hindi-Instruct-v2 — a Hindi instruction-tuned Qwen3-4B model. These run locally on CPU or GPU with llama.cpp, Ollama, and LM Studio — no Python or heavy setup needed.

Part of the Hindi LLM Series, focused on bringing Indic-language models to local and edge devices.

Available Quants

File	Quant	Size	Recommended for
`Qwen3-4B-Hindi-v2.Q4_K_M.gguf`	Q4_K_M	2.5 GB	Best balance — start here
`Qwen3-4B-Hindi-v2.Q5_K_M.gguf`	Q5_K_M	2.9 GB	Higher quality, slightly larger
`Qwen3-4B-Hindi-v2.Q8_0.gguf`	Q8_0	4.3 GB	Near-lossless, maximum quality

If unsure, download Q4_K_M — it's the best size-to-quality tradeoff for most machines.

How to Run

Ollama

huggingface-cli download pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF Qwen3-4B-Hindi-v2.Q4_K_M.gguf --local-dir .
ollama create qwen3-hindi -f Modelfile
ollama run qwen3-hindi "भारत के बारे में एक रोचक तथ्य बताओ।"

llama.cpp

./llama-cli -m Qwen3-4B-Hindi-v2.Q4_K_M.gguf -p "भारत की राजधानी क्या है?" -cnv

LM Studio

Search for this repo in LM Studio, download the Q4_K_M file, and chat directly in the GUI.

About the Model

This is a Hindi instruction fine-tune of Qwen3-4B (LoRA via Unsloth, 10K Hindi instruction pairs), quantized to GGUF for efficient local inference. It handles both Hindi (Devanagari) and English.

For full model details and the original 16-bit weights, see the base model card.

License

Apache 2.0 — commercial use allowed.

Part of the 🇮🇳 Hindi LLM Series by pankajpandey-dev.

Downloads last month: 261

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF

Base model

Qwen/Qwen3-4B-Instruct-2507

Adapter

pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2

Quantized

(1)

this model

Collection including pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF

🇮🇳 Hindi LLM Series

Collection

5 items • Updated 6 days ago • 1