Instructions to use ikppramesh/irx-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ikppramesh/irx-2 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("ikppramesh/irx-2")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use ikppramesh/irx-2 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ikppramesh/irx-2"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ikppramesh/irx-2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use ikppramesh/irx-2 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "ikppramesh/irx-2"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "ikppramesh/irx-2"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "ikppramesh/irx-2",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use ikppramesh/irx-2 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ikppramesh/irx-2"

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ikppramesh/irx-2 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ikppramesh/irx-2"

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" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

IRx-2 — ⭐ Best Overall

The recommended model of the IRx family: a private, offline AI chat assistant with the best balance of answer quality and speed. Everything runs on your device — no internet connection, no account, and nothing you type ever leaves your phone, tablet or laptop.

The IRx family

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

Model	Download	Best for	Phones & tablets (GGUF)	Mac (MLX)
IRx-1	~1.2GB	The fastest. 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-1 for speed or an older / smaller phone, and IRx-2 Pro for the richest answers on a device with 12GB+ RAM.

What IRx-2 does best — ⭐ Best Overall

IRx-2 is the recommended model for most people. It is about twice the size of IRx-1, which shows in clearer reasoning, better-structured answers, more reliable writing and stronger coding help — while still running comfortably on phones and tablets with 8GB+ RAM.

Who	Example things to ask
Software developers	"Explain this error and how to fix it: …" · "Write a function that validates an email address, with tests." · "Why is this SQL query slow?"
Farmers	"Plan a monthly budget for a 2-acre vegetable farm." · "Compare drip and flood irrigation: pros and cons." · "Write a loan application letter to my bank."
Students	"Make a one-week study plan for my exams." · "Explain Newton's laws with everyday examples."
Teachers	"Create a 40-minute lesson plan on the water cycle, with a short quiz."
Shop & small business owners	"Outline a simple business plan for a tea stall." · "Write a polite reply to a customer complaint."
Writers & creators	"Outline a 5-minute YouTube script about saving money." · "Make this paragraph sound more professional."
Job seekers	"Improve these resume bullet points." · "Give me 10 likely interview questions for a sales role."
Everyday life	"Plan a family weekend on a budget." · "Help me write a birthday message for my father."

Measured on the same Mac CPU: IRx-2 generates about 26 tokens/s vs IRx-1's 54 — roughly half the speed, for noticeably better answers.

Usage (Mac, MLX)

pip install mlx-lm

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load("ikppramesh/irx-2")
messages = [{"role": "user", "content": "How do I convert Celsius to Fahrenheit?"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
print(generate(model, tokenizer, prompt=prompt, max_tokens=1024,
               sampler=make_sampler(temp=0.7, top_p=0.95)))

No system prompt is required: the built-in chat template supplies the IRx-2 one when none is given, and always keeps thinking mode off. Sample at a non-zero temperature (e.g. temp=0.7). On a phone or tablet, use the GGUF build.

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.
Not current-events aware — its knowledge is fixed at training time.
Don't enable native tool/function-calling in chat apps — never trained; plain chat is reliable.

Changelog

2026-09-30 — First release. Built with the IRx pipeline's fixes: fine-tune merged into the full-precision base and quantized once with an importance matrix (no repeating/looping replies), thinking mode always off, identity built in. 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 is a derivative fine-tuned model — full Apache 2.0 terms apply as with any work under this license.

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Safetensors

Model size

4B params

Tensor type

U32

BF16

F32

MLX

Hardware compatibility

4-bit