Instructions to use ikppramesh/irx-2-pro-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 ikppramesh/irx-2-pro-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 ikppramesh/irx-2-pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ikppramesh/irx-2-pro-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 ikppramesh/irx-2-pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ikppramesh/irx-2-pro-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 ikppramesh/irx-2-pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ikppramesh/irx-2-pro-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 ikppramesh/irx-2-pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ikppramesh/irx-2-pro-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ikppramesh/irx-2-pro-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use ikppramesh/irx-2-pro-GGUF with Ollama:
```
ollama run hf.co/ikppramesh/irx-2-pro-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ikppramesh/irx-2-pro-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ikppramesh/irx-2-pro-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": "ikppramesh/irx-2-pro-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ikppramesh/irx-2-pro-GGUF with Docker Model Runner:
```
docker model run hf.co/ikppramesh/irx-2-pro-GGUF:Q4_K_M
```

Lemonade

How to use ikppramesh/irx-2-pro-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ikppramesh/irx-2-pro-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.irx-2-pro-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ikppramesh/irx-2-pro-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 ikppramesh/irx-2-pro-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 ikppramesh/irx-2-pro-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ikppramesh/irx-2-pro-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ikppramesh/irx-2-pro-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 "ikppramesh/irx-2-pro-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"

IRx-2 Pro (GGUF)

The largest edition of the IRx family: a private, offline AI chat assistant for long, detailed answers and extended conversations. Everything runs on your device — no internet connection, no account, and nothing you type ever leaves it. This is the build for phones, tablets and llama.cpp-based apps (PocketPal, LM Studio, llama.rn, Ollama).

Download: irx-2-pro-Q4_K_M.gguf (~5.3GB). Needs roughly 6GB of RAM while running, so use it on devices with 12GB+ RAM (recent Android flagships, foldables, Macs). On 8GB phones and tablets it may fail to load or be closed by the system — use ⭐ IRx-2 there instead.

The IRx family

All four run fully offline — nothing you type ever leaves your device.

Model	Download	Best for	Phones & tablets (GGUF)	Mac (MLX)
IRx-mini	~0.8GB	The smallest. Simple, quick answers on phones with just 4GB of RAM	irx-mini-GGUF	irx-mini
IRx-1	~1.2GB	Fast, quick everyday answers on almost any phone	irx-1-GGUF	irx-1
⭐ IRx-2 — Best Overall	~2.7GB	The best balance of answer quality and speed	irx-2-GGUF	irx-2
IRx-2 Pro	~5.3GB	The most detailed, long-form answers and long conversations	irx-2-pro-GGUF	irx-2-pro

Not sure which to pick? Start with ⭐ IRx-2. Choose IRx-mini for a 4GB-RAM phone, IRx-1 for fast everyday answers on most phones, and IRx-2 Pro for the richest answers on a device with 12GB+ RAM.

See them side by side: the IRx board compares all four models, shows their measured speed live, and has an animated step-by-step setup guide for PocketPal on iPhone and Android.

What IRx-2 Pro does best

IRx-2 Pro is the largest edition. It was trained with the longest context window of the family (so it learned from nearly all of the long conversations in the training data), which makes it the best choice for long, detailed answers and for working through a topic over many messages. It runs at about the same speed as IRx-2, but needs more memory: use it on devices with 12GB+ RAM, or on a Mac.

Who	Example things to ask
Software developers	"Help me design a small inventory app step by step — data model, screens and API." · "Walk me through refactoring this code over a few messages."
Farmers	"Help me write a full-season plan for my farm — sowing, watering, spraying and harvest — then let's refine it." · "Write a detailed proposal to sell my produce to a local supermarket."
Students & researchers	"Turn these notes into a structured report with headings and a summary." · "Explain this topic in depth, then quiz me on it."
Teachers	"Create a full week's unit on fractions: daily lessons, activities and a final test."
Business owners	"Draft a detailed marketing plan for my new bakery." · "Write a complete employee handbook outline."
Writers & creators	"Write a 1,500-word blog post on healthy habits." · "Help me develop a short story over several messages."
Everyday life	"Plan a 7-day family trip with a day-by-day schedule and budget."

Measured on the same Mac CPU: IRx-2 Pro generates about 24 tokens/s, close to IRx-2's 26 — most of its extra size is lookup tables that cost little compute.

Recommended settings (PocketPal and similar apps)

Setting	Value
Context size	8192
Max new tokens	1024
Temperature	0.7
Top-P / Min-P	0.95 / 0.05
Repeat penalty	1.1
System prompt	optional — leave empty to use the built-in one

A repeat penalty of 1.1 is also embedded in the file as the default; apps that let you set your own sampling values (PocketPal does) need it set there too.

Updating from an older download: delete the old model in the app and download it again — apps don't replace a downloaded file on their own. If you set a custom chat template in the app, reset it so the one built into the file is used.

Built-in chat template: thinking off, identity on

Apps built on llama.cpp often switch on a hidden "thinking" mode by default, which can use up the whole conversation before any answer appears ("The conversation ran out of room"). The template embedded in this file always keeps thinking off — the app's reasoning toggle has no effect — and supplies the model's own system prompt when the app sends none.

Usage (command line)

llama-cli -m irx-2-pro-Q4_K_M.gguf --jinja -c 8192 -p "How do I convert Celsius to Fahrenheit?"

Limitations

Not a frontier-scale model — it won't match large hosted AI services on very hard multi-step reasoning, deep coding problems or breadth of world knowledge.
Facts can be wrong. Like any small offline model it can state things confidently that aren't true — double-check anything important, such as prices, medicines and doses, laws, and local farming advice (seed varieties, chemical quantities, weather).
Not a replacement for professionals — medical, legal and financial answers are general information only.
News up to 3 October 2026 only — it can't look anything up, and news details can be imprecise.
Don't enable native tool/function-calling in chat apps — never trained; plain chat is reliable.

Changelog

2026-10-03 — News refresh and quality update
- Built-in news digest (up to 3 October 2026). The model's built-in prompt now carries 15 of the day's top headlines across India, world, business, science and technology, AI and sports, so it can answer questions about them offline, in any app, with no internet. In testing, answers about digest headlines contained the real facts 47% of the time, against 0% for news learned through training alone.
- Knows its cutoff. Asked how current it is, it says its news goes up to 3 October 2026 and suggests checking a news source for anything later.
- News sources: 18. Times of India, The Hindu (National, Business, Sport, Sci-Tech), Indian Express, NDTV, LiveMint, BBC (World, Business, Science, Technology), Al Jazeera, ESPNcricinfo, NASA, TechCrunch, The Verge and MIT Technology Review. Recent headlines were also added to training.
- Cleaner training data. Removed answers that repeat themselves, refusals, answers copied from earlier in a chat and leftover chat-export text. Greetings no longer trigger a self-introduction, and identity answers never credit another AI company.
- Passed the pre-publish check: repeated 8-turn conversations with the repeat penalty off and the app's thinking request on — no loops, no copied answers, no hidden reasoning, correct identity.
- Keep in mind: news outside the digest is not reliably known, and small models can mix up names, numbers and dates. Double-check anything important. The digest adds a few seconds before the first reply of a new chat.
2026-09-30 — First release (published earlier the same day under a different repository name; old links redirect here). Trained with a 2048-token window, merged into the full-precision base and quantized once with an importance matrix. Passed the automated pre-publish check: repeated 8-turn conversations with the repeat penalty off and the app's thinking request on — no loops, no copied answers, no hidden reasoning, correct identity.

License

Apache 2.0. IRx-2 Pro is a derivative fine-tuned model — full Apache 2.0 terms apply as with any work under this license.

Downloads last month: 287

GGUF

Model size

7B params

Architecture

gemma4

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support