Image-Text-to-Text
MLX
Safetensors
inkling_mm_model
Mixture of Experts
multimodal
inkling
thinking-machines
conversational
Instructions to use pipenetwork/Inkling-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use pipenetwork/Inkling-MLX-4bit 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("pipenetwork/Inkling-MLX-4bit") config = load_config("pipenetwork/Inkling-MLX-4bit") # 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
- Pi
How to use pipenetwork/Inkling-MLX-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "pipenetwork/Inkling-MLX-4bit"
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": "pipenetwork/Inkling-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use pipenetwork/Inkling-MLX-4bit 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 "pipenetwork/Inkling-MLX-4bit"
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 pipenetwork/Inkling-MLX-4bit
Run Hermes
hermes
- OpenClaw new
How to use pipenetwork/Inkling-MLX-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "pipenetwork/Inkling-MLX-4bit"
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 "pipenetwork/Inkling-MLX-4bit" \ --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"
File size: 1,857 Bytes
ecbc3d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | """CLI: convert/quantize an Inkling checkpoint to MLX.
Examples:
python -m inkling_mlx.convert_cli --src /path/Inkling-src --dst out-bf16
python -m inkling_mlx.convert_cli --src /path/Inkling-src --dst out-4bit --bits 4
"""
from __future__ import annotations
import argparse
import time
import mlx.core as mx
from .convert import convert_model
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--src", required=True, help="Inkling bf16 source dir (HF layout)")
ap.add_argument("--dst", required=True, help="output dir")
ap.add_argument("--bits", type=int, default=None, choices=[2, 3, 4, 5, 6, 8],
help="quantization bits; omit for bf16 passthrough")
ap.add_argument("--group-size", type=int, default=64)
ap.add_argument("--dtype", default="bfloat16", choices=["bfloat16", "float16"])
ap.add_argument("--device", default="gpu", choices=["gpu", "cpu"],
help="cpu avoids the Metal GPU-timeout watchdog on huge tensors (slower, robust)")
ap.add_argument("--recipe", default="uniform", choices=["uniform", "experts_only"],
help="experts_only keeps attention + embed/unembed at bf16 (coherent 4-bit-sized build)")
args = ap.parse_args()
if args.device == "cpu":
mx.set_default_device(mx.cpu)
print("[convert] using CPU device (avoids Metal command-buffer timeout)")
dtype = {"bfloat16": mx.bfloat16, "float16": mx.float16}[args.dtype]
t0 = time.time()
print(f"[convert] {args.src} -> {args.dst} bits={args.bits} group_size={args.group_size} dtype={args.dtype} recipe={args.recipe}")
convert_model(args.src, args.dst, bits=args.bits, group_size=args.group_size, out_dtype=dtype, recipe=args.recipe)
print(f"[convert] done in {time.time()-t0:.0f}s -> {args.dst}")
if __name__ == "__main__":
main()
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