Instructions to use jefflaporte/chandra-ocr-2-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jefflaporte/chandra-ocr-2-MLX-8bit with MLX:

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

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("jefflaporte/chandra-ocr-2-MLX-8bit")
config = load_config("jefflaporte/chandra-ocr-2-MLX-8bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use jefflaporte/chandra-ocr-2-MLX-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jefflaporte/chandra-ocr-2-MLX-8bit"

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": "jefflaporte/chandra-ocr-2-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use jefflaporte/chandra-ocr-2-MLX-8bit 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 "jefflaporte/chandra-ocr-2-MLX-8bit"

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 jefflaporte/chandra-ocr-2-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jefflaporte/chandra-ocr-2-MLX-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jefflaporte/chandra-ocr-2-MLX-8bit"

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 "jefflaporte/chandra-ocr-2-MLX-8bit" \
  --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"

chandra-ocr-2-MLX-8bit

File size: 780 Bytes

b9501e2

{
  "recipe": "8bit",
  "source": {
    "repo": "datalab-to/chandra-ocr-2",
    "revision": "af93b47dba1b47b6640c86ccf487ed2260ab9a09"
  },
  "base_model_license": {
    "repo": "Qwen/Qwen3.5-4B",
    "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a"
  },
  "quantization": {
    "q_bits": 8,
    "q_group_size": 64,
    "quant_predicate": null,
    "method": "rtn",
    "calibration": null,
    "default_bits": 8,
    "group_size": 64,
    "modules_by_bits": {}
  },
  "tools": {
    "mlx": "0.32.2",
    "mlx-metal": "0.32.2",
    "mlx-vlm": "0.7.6",
    "transformers": "5.21.0"
  },
  "processor_files_restored_from_source": [
    "preprocessor_config.json",
    "processor_config.json",
    "video_preprocessor_config.json"
  ],
  "built": "2026-10-26T20:15:41+00:00"
}