Instructions to use Alsamir/Abjad_0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alsamir/Abjad_0.2 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("Alsamir/Abjad_0.2")
config = load_config("Alsamir/Abjad_0.2")

# 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 Alsamir/Abjad_0.2 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Alsamir/Abjad_0.2"

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": "Alsamir/Abjad_0.2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use Alsamir/Abjad_0.2 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 "Alsamir/Abjad_0.2"

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 Alsamir/Abjad_0.2

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Alsamir/Abjad_0.2 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Alsamir/Abjad_0.2"

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 "Alsamir/Abjad_0.2" \
  --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"

Abjad_0.2 / mlx_lora_entry.py

Alsamir

Add Hunyuan-style repository documentation and tooling

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#!/usr/bin/env python3
"""Make mlx-vlm's trainer accept an exact local JSONL file."""
from pathlib import Path
import runpy
import sys
import os

import datasets

_load_dataset = datasets.load_dataset


def load_dataset(path, name=None, split=None, **kwargs):
    candidate = Path(path)
    if candidate.exists() or str(path).endswith((".json", ".jsonl")):
        return _load_dataset("json", data_files=str(candidate), split=split or "train")
    return _load_dataset(path, name, split=split, **kwargs)


datasets.load_dataset = load_dataset

# Qwen3-VL uses a dynamic-resolution processor.  mlx-vlm's generic
# image-resize-shape option is intentionally ignored by that native path, so
# expose a safe pixel budget for local 16 GB Apple Silicon training.
try:
    from mlx_vlm import utils as _mlx_utils

    def _bounded_process_inputs(processor, prompts, images, audio, add_special_tokens=False, return_tensors="mlx", **kwargs):
        max_pixels = os.environ.get("ABJAD_MAX_PIXELS")
        if max_pixels:
            # Qwen3-VL exposes these through **kwargs, so the generic helper
            # cannot forward them by signature inspection.
            kwargs["min_pixels"] = 56 * 56
            kwargs["max_pixels"] = int(max_pixels)
        process_method = getattr(processor, "process", processor)
        call = {
            "text": prompts,
            "images": images,
            "padding": True,
            "return_tensors": return_tensors,
            "add_special_tokens": add_special_tokens,
            **kwargs,
        }
        return process_method(**call)

    _mlx_utils.process_inputs_with_fallback = _bounded_process_inputs
except Exception:
    pass

sys.argv[0] = "mlx_vlm.lora"
runpy.run_module("mlx_vlm.lora", run_name="__main__")

	#!/usr/bin/env python3
	"""Make mlx-vlm's trainer accept an exact local JSONL file."""
	from pathlib import Path
	import runpy
	import sys
	import os

	import datasets

	_load_dataset = datasets.load_dataset


	def load_dataset(path, name=None, split=None, **kwargs):
	candidate = Path(path)
	if candidate.exists() or str(path).endswith((".json", ".jsonl")):
	return _load_dataset("json", data_files=str(candidate), split=split or "train")
	return _load_dataset(path, name, split=split, **kwargs)


	datasets.load_dataset = load_dataset

	# Qwen3-VL uses a dynamic-resolution processor. mlx-vlm's generic
	# image-resize-shape option is intentionally ignored by that native path, so
	# expose a safe pixel budget for local 16 GB Apple Silicon training.
	try:
	from mlx_vlm import utils as _mlx_utils

	def _bounded_process_inputs(processor, prompts, images, audio, add_special_tokens=False, return_tensors="mlx", **kwargs):
	max_pixels = os.environ.get("ABJAD_MAX_PIXELS")
	if max_pixels:
	# Qwen3-VL exposes these through **kwargs, so the generic helper
	# cannot forward them by signature inspection.
	kwargs["min_pixels"] = 56 * 56
	kwargs["max_pixels"] = int(max_pixels)
	process_method = getattr(processor, "process", processor)
	call = {
	"text": prompts,
	"images": images,
	"padding": True,
	"return_tensors": return_tensors,
	"add_special_tokens": add_special_tokens,
	**kwargs,
	}
	return process_method(**call)

	_mlx_utils.process_inputs_with_fallback = _bounded_process_inputs
	except Exception:
	pass

	sys.argv[0] = "mlx_vlm.lora"
	runpy.run_module("mlx_vlm.lora", run_name="__main__")