Instructions to use salohcin714/granite-4.1-30b-2bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use salohcin714/granite-4.1-30b-2bit-mlx 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("salohcin714/granite-4.1-30b-2bit-mlx")

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 salohcin714/granite-4.1-30b-2bit-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "salohcin714/granite-4.1-30b-2bit-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "salohcin714/granite-4.1-30b-2bit-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use salohcin714/granite-4.1-30b-2bit-mlx 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 "salohcin714/granite-4.1-30b-2bit-mlx"

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 salohcin714/granite-4.1-30b-2bit-mlx

Run Hermes

hermes

OpenClaw new

How to use salohcin714/granite-4.1-30b-2bit-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "salohcin714/granite-4.1-30b-2bit-mlx"

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 "salohcin714/granite-4.1-30b-2bit-mlx" \
  --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"

MLX LM

How to use salohcin714/granite-4.1-30b-2bit-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "salohcin714/granite-4.1-30b-2bit-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "salohcin714/granite-4.1-30b-2bit-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "salohcin714/granite-4.1-30b-2bit-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

granite-4.1-30b-2bit-mlx

Provenance

Converted from ibm-granite/granite-4.1-30b using mlx-lm 0.31.3.

Usage

from mlx_lm import load, generate

model, tokenizer = load("salohcin714/granite-4.1-30b-2bit-mlx")
messages = [{"role": "user", "content": "Hello"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Modifications

Weights converted to MLX safetensors layout and quantized (2-bit affine quantization, group size 64, via round-to-nearest, no calibration). Redundant tied lm_head.weight dropped where the model ties input/output embeddings. No fine-tuning; no added training data.

License and attribution

Licensed under Apache 2.0. Original weights by the Granite Team, IBM. See the upstream model card and the included LICENSE file for the full text.

Disclaimer

This repository is not affiliated with or endorsed by IBM. "Granite" is an IBM trademark, used here descriptively to identify the origin of the base model. IBM's published benchmarks describe the original weights, not this quantized/converted artifact, and must not be read as claims about this repo.

Downloads last month: -

Safetensors

Model size

29B params

Tensor type

BF16

U32

MLX

Hardware compatibility

2-bit

Model tree for salohcin714/granite-4.1-30b-2bit-mlx

Base model

ibm-granite/granite-4.1-30b

Quantized

(41)

this model