Instructions to use pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use pankajpandey-dev/qwen3.5-9b-hindi-instruct-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 pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pankajpandey-dev/qwen3.5-9b-hindi-instruct-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 pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pankajpandey-dev/qwen3.5-9b-hindi-instruct-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/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pankajpandey-dev/qwen3.5-9b-hindi-instruct-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/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pankajpandey-dev/qwen3.5-9b-hindi-instruct-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/qwen3.5-9b-hindi-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M

Ollama
How to use pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF with Ollama:
```
ollama run hf.co/pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF: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": "pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M
```

Lemonade

How to use pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.qwen3.5-9b-hindi-instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use pankajpandey-dev/qwen3.5-9b-hindi-instruct-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 pankajpandey-dev/qwen3.5-9b-hindi-instruct-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/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pankajpandey-dev/qwen3.5-9b-hindi-instruct-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 "pankajpandey-dev/qwen3.5-9b-hindi-instruct-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"

qwen3.5-9b-hindi-instruct-GGUF / README.md

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metadata

license: apache-2.0
language:
  - hi
  - en
base_model: pankajpandey-dev/qwen3.5-9b-hindi-instruct
base_model_relation: quantized
pipeline_tag: text-generation
tags:
  - hindi
  - qwen3.5
  - gguf
  - llama.cpp
  - lmstudio
  - quantized
  - india

Qwen3.5-9B Hindi Instruct — GGUF 🇮🇳

Native-Hindi Qwen3.5-9B for your laptop. Quantized from pankajpandey-dev/qwen3.5-9b-hindi-instruct. Answers directly in Devanagari — no English thinking, no code-switching.

Which file?

File	Size	RAM needed	Use when
Q4_K_M	~5.7 GB	~8 GB	Recommended — best size/quality on CPU
Q5_K_M	~6.7 GB	~9 GB	More quality headroom
Q8_0	~10 GB	~12 GB	Near-lossless

Run it

llama.cpp (build from ~March 2026 or newer for Qwen3.5 support):

llama-cli -m qwen3.5-9b-hindi-Q4_K_M.gguf --jinja -cnv --repeat-penalty 1.1

LM Studio: search this repo name, download Q4_K_M, set repeat penalty 1.1, chat. Python (llama-cpp-python 0.3.32 or newer):

from llama_cpp import Llama
llm = Llama(model_path="qwen3.5-9b-hindi-Q4_K_M.gguf", n_ctx=4096)
r = llm.create_chat_completion(
    messages=[{"role": "user", "content": "योग के चार लाभ बताइए।"}],
    repeat_penalty=1.1)
print(r["choices"][0]["message"]["content"])

Ollama: ollama run hf.co/pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M — needs an Ollama build with Qwen3.5 support; if it errors, use llama.cpp or LM Studio. Tip: use repeat penalty 1.1 — long letter-style outputs can loop without it.

🇮🇳 About the Hindi LLM Series

Weekly open releases making small LLMs speak fluent, native Hindi — trained on free/low-cost GPUs, shipped as GGUF for laptops and edge devices. Built by pankajpandey-dev (contact links on profile).

This release: Model · GGUF · LoRA · Series: 🇮🇳 Hindi LLM Collection