Instructions to use toiar/Ri-Gemma-E2B-it-QAT-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 toiar/Ri-Gemma-E2B-it-QAT-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 toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf toiar/Ri-Gemma-E2B-it-QAT-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 toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf toiar/Ri-Gemma-E2B-it-QAT-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 toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf toiar/Ri-Gemma-E2B-it-QAT-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 toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M

Use Docker

docker model run hf.co/toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use toiar/Ri-Gemma-E2B-it-QAT-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "toiar/Ri-Gemma-E2B-it-QAT-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": "toiar/Ri-Gemma-E2B-it-QAT-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M

Ollama
How to use toiar/Ri-Gemma-E2B-it-QAT-GGUF with Ollama:
```
ollama run hf.co/toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M
```
Unsloth Desktop

How to use toiar/Ri-Gemma-E2B-it-QAT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf toiar/Ri-Gemma-E2B-it-QAT-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": "toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use toiar/Ri-Gemma-E2B-it-QAT-GGUF with Docker Model Runner:
```
docker model run hf.co/toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M
```

Lemonade

How to use toiar/Ri-Gemma-E2B-it-QAT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ri-Gemma-E2B-it-QAT-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use toiar/Ri-Gemma-E2B-it-QAT-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 toiar/Ri-Gemma-E2B-it-QAT-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 toiar/Ri-Gemma-E2B-it-QAT-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use toiar/Ri-Gemma-E2B-it-QAT-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf toiar/Ri-Gemma-E2B-it-QAT-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 "toiar/Ri-Gemma-E2B-it-QAT-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"

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Ri-Gemma-E2B-it-QAT-GGUF

Ri-Gemma-E2B-it-QAT-GGUF is the GGUF release of Ri-Gemma-E2B-IT-QAT-Khasi-Chatbot, optimized for efficient local inference with llama.cpp, Ollama, LM Studio, Jan, Open WebUI, and other GGUF-compatible applications.

The model provides fluent Khasi conversations, instruction following, translation, and reasoning while enabling deployment on consumer hardware through multiple quantization formats.

Quantizations

Quantization	Size	Recommended Use
Q4_K_M	3.43 GB	Best balance between quality and speed. Recommended for most users.
Q6_K	3.85 GB	Higher quality with moderate memory requirements.
Q8_0	4.97 GB	Near-lossless quantization with excellent response quality.
F16	9.31 GB	Full-precision model for maximum quality and research purposes.

Features

Native Khasi conversational AI
English ↔ Khasi translation
Instruction following
General knowledge
Logical reasoning
Writing assistance
Local offline inference
Compatible with popular GGUF runtimes

Recommended Software

This model can be used with:

llama.cpp
Ollama
LM Studio
Jan
GPT4All
Open WebUI
KoboldCpp

Training

This GGUF release is converted from:

Ri-Gemma-E2B-IT-QAT-Khasi-Chatbot

which was instruction-tuned using:

Dataset: toiar/khasi-instruction-response-v2

Dataset Statistics

Category	Count
Total Instruction-Response Pairs	77,810
Khasi-Specific Data	72,810
External English Reasoning & General Data	5,000

The dataset combines:

Khasi conversations
Instruction following
Translation
Cultural knowledge
Educational content
General knowledge
High-quality reasoning examples

Intended Uses

This model is suitable for:

Local AI assistants
Offline chatbots
Language learning
Translation
Educational applications
Research on Khasi NLP
Low-resource language development

Limitations

This model may:

Produce incorrect factual information.
Make reasoning mistakes on complex tasks.
Be sensitive to ambiguous prompts.
Reflect biases present in the training data.

Human verification is recommended for important decisions.

Citation

If you use this model in your work, please cite:

@misc{ri_gemma_e2b_it_qat_gguf,
  title        = {Ri-Gemma-E2B-it-QAT-GGUF},
  author       = {Toiar},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/toiar/Ri-Gemma-E2B-it-QAT-GGUF}}
}