Instructions to use InduwaraR/qwen-ai-research-qa-q4_k_m.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 InduwaraR/qwen-ai-research-qa-q4_k_m.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 InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf InduwaraR/qwen-ai-research-qa-q4_k_m.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 InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf InduwaraR/qwen-ai-research-qa-q4_k_m.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 InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf InduwaraR/qwen-ai-research-qa-q4_k_m.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 InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M

Use Docker

docker model run hf.co/InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use InduwaraR/qwen-ai-research-qa-q4_k_m.gguf with Ollama:
```
ollama run hf.co/InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M
```

Unsloth Studio

How to use InduwaraR/qwen-ai-research-qa-q4_k_m.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 InduwaraR/qwen-ai-research-qa-q4_k_m.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 InduwaraR/qwen-ai-research-qa-q4_k_m.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for InduwaraR/qwen-ai-research-qa-q4_k_m.gguf to start chatting

How to use InduwaraR/qwen-ai-research-qa-q4_k_m.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf InduwaraR/qwen-ai-research-qa-q4_k_m.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": "InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use InduwaraR/qwen-ai-research-qa-q4_k_m.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 InduwaraR/qwen-ai-research-qa-q4_k_m.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 InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use InduwaraR/qwen-ai-research-qa-q4_k_m.gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf InduwaraR/qwen-ai-research-qa-q4_k_m.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 "InduwaraR/qwen-ai-research-qa-q4_k_m.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"

Docker Model Runner
How to use InduwaraR/qwen-ai-research-qa-q4_k_m.gguf with Docker Model Runner:
```
docker model run hf.co/InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M
```

Lemonade

How to use InduwaraR/qwen-ai-research-qa-q4_k_m.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull InduwaraR/qwen-ai-research-qa-q4_k_m.gguf:Q4_K_M

Run and chat with the model

lemonade run user.qwen-ai-research-qa-q4_k_m.gguf-Q4_K_M

List all available models

lemonade list

Qwen AI Research QA Model (Q4_K_M GGUF)

Model Overview

The Qwen AI Research QA Model is designed for answering research-oriented AI questions with a focus on precision and depth. This model is optimized in the Q4_K_M format for efficient inference while maintaining high-quality responses.

How to Use

To use this model with llama-cpp-python, follow these steps:

Installation

Make sure you have llama-cpp-python installed:

pip install llama-cpp-python

Loading the Model

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="InduwaraR/qwen-ai-research-qa-q4_k_m.gguf",
    filename="qwen-ai-research-qa-q4_k_m.gguf",
)

Generating a Response

response = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "What are the latest advancements in AI research?"}
    ]
)
print(response)

Model Details

Model Name: Qwen AI Research QA
Format: GGUF (Q4_K_M Quantization)
Primary Use Case: AI research question answering
Inference Framework: llama-cpp-python
Optimized for: Running on local hardware with reduced memory usage

License

This model is open-source and available under the MIT License.

Acknowledgments

This model is hosted by InduwaraR on Hugging Face. Special thanks to the Qwen AI team for their contributions to AI research and development.

Downloads last month: 18

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for InduwaraR/qwen-ai-research-qa-q4_k_m.gguf

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Qwen/Qwen2.5-3B

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