Instructions to use netprojects/GLiDE-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use netprojects/GLiDE-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("netprojects/GLiDE-mlx-4bit")
config = load_config("netprojects/GLiDE-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

How to use netprojects/GLiDE-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 "netprojects/GLiDE-mlx-4bit"

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": "netprojects/GLiDE-mlx-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use netprojects/GLiDE-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 "netprojects/GLiDE-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 netprojects/GLiDE-mlx-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use netprojects/GLiDE-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 "netprojects/GLiDE-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 "netprojects/GLiDE-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"

GLiDE-mlx-4bit / README.md

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---
license: other
license_name: fastino-community
license_link: LICENSE
base_model: fastino/GLiDE
base_model_relation: quantized
library_name: mlx
pipeline_tag: image-text-to-text
tags: [mlx, decision-model, decision-making, calibration, jevbench, qwen3_5, apple-silicon, glide]
---
# GLiDE — MLX 4-bit

*Built and tested on Apple M5 Max (128 GB) · upstream revision GLiDE v1.2 (`d199b6d`)*

A 4-bit MLX conversion (affine, group size 64) of [fastino/GLiDE](https://huggingface.co/fastino/GLiDE) (GLiDE v1.2, revision `d199b6d`), Fastino's decision model built on Qwen3.8-27B, for Apple Silicon. 15.3 GB of weights.

**Built with Fastino GLiDE.** Published by [NetProjects](https://netprojects.com) (Internet Projects Ltd). Not affiliated with Fastino.

GLiDE is a **decision model**, not a chat model. Give it a state (text, JSON or images) and typed questions (`choice`, `noul` for yes / no, `score`) and it returns a probability for every option, in one forward pass per question. Nothing is generated.

> **Serve it with [glide-mlx](https://github.com/internet-projects/glide-mlx), not with `mlx_lm.server` or a plain `mlx_vlm` generate call.** GLiDE reads its answer from a separate 255-way readout (`readout.safetensors`) and lets its full-attention layers read the whole prompt in both directions; generic MLX snippets do neither.

## Results (MacBook Pro M5 Max, 128 GB, 29 October 2026)

| | this checkpoint |
|---|---|
| Same top answer as Fastino's own PyTorch code (bf16) | **197 / 204** questions; probability difference mean 0.012 |
| Yes / no and score questions | 132 / 132 identical |
| JevBench public (231, JevBench v1.2.1, `--adapter typesafe`) | **213 / 231** (easy 48/48 · standard 72/72 · hard 93/111) |
| Time per JevBench item (prompts up to about 4K tokens) | 0.45 s median |

The answers that differ from Fastino's code are all `choice` questions with many options where the reference's top two options were close (top-two margin 0.09 or less). The comparison set has 72 requests with 204 questions: text and JSON states of 30 to 1,500 words, 12 image requests, and all three question types. The smallest build: about half the size of the 8-bit build, with probabilities that move a little more. For the closest match to the original, use [netprojects/GLiDE-mlx-8bit](https://huggingface.co/netprojects/GLiDE-mlx-8bit).

## What is kept as in the original

- **Reading:** the 16 full-attention layers see the whole prompt (no causal mask), the 48 Gated DeltaNet layers keep their recurrence, as GLiDE's `noncausal_full_attention`.
- **Answer:** the last token's hidden state goes through GLiDE's 255-way readout, kept at its original precision; softmax over the options at the temperature in `decision_config.json`.
- **Prompts and images:** GLiDE's own `prompt.py` and `images.py`, unchanged; the vision encoder stays at its original precision.
- **API:** the same `POST /v1/systemone` request and response format as GLiDE's server.

## Use

```bash
git clone https://github.com/internet-projects/glide-mlx && cd glide-mlx
uv venv --python 3.12 .venv && uv pip install --python .venv/bin/python -r requirements.txt
hf download netprojects/GLiDE-mlx-4bit --local-dir glide-mlx-4bit
.venv/bin/python -m glide_mlx.server --model glide-mlx-4bit --port 8790    # http://127.0.0.1:8790/v1/systemone
```

Python: `from glide_mlx import GlideMLX; g = GlideMLX.from_pretrained("glide-mlx-4bit"); g.decide(state, questions)`.

## License

GLiDE is licensed under the **Fastino Community License** (`LICENSE`, `NOTICE`, `licenses/Qwen3.8-27B-LICENSE` are included). This conversion is a Derivative Work under that license and is distributed under the same terms. In short: commercial use requires displaying "Built with Fastino GLiDE" (section 4), and production commercial use by an entity with annual revenue above USD 10 million requires a separate agreement with Fastino (sections 2–3). Read the license for the full terms.

Credits: Fastino (GLiDE), Qwen team (Qwen3.8-27B, Apache-2.0), MLX and mlx-vlm. Conversion by Paris Michalas, Partner at [NetProjects](https://netprojects.com). Not affiliated with Fastino.

**More NetProjects MLX builds:** [NetProjects MLX builds collection](https://huggingface.co/collections/netprojects/netprojects-mlx-builds-6ae3b2683a59a1b4fc4c0c3a) · [all models at huggingface.co/netprojects](https://huggingface.co/netprojects)

	---
	license: other
	license_name: fastino-community
	license_link: LICENSE
	base_model: fastino/GLiDE
	base_model_relation: quantized
	library_name: mlx
	pipeline_tag: image-text-to-text
	tags: [mlx, decision-model, decision-making, calibration, jevbench, qwen3_5, apple-silicon, glide]
	---
	# GLiDE — MLX 4-bit

	Built and tested on Apple M5 Max (128 GB) · upstream revision GLiDE v1.2 (`d199b6d`)

	A 4-bit MLX conversion (affine, group size 64) of [fastino/GLiDE](https://huggingface.co/fastino/GLiDE) (GLiDE v1.2, revision `d199b6d`), Fastino's decision model built on Qwen3.8-27B, for Apple Silicon. 15.3 GB of weights.

	Built with Fastino GLiDE. Published by [NetProjects](https://netprojects.com) (Internet Projects Ltd). Not affiliated with Fastino.

	GLiDE is a decision model, not a chat model. Give it a state (text, JSON or images) and typed questions (`choice`, `noul` for yes / no, `score`) and it returns a probability for every option, in one forward pass per question. Nothing is generated.

	> Serve it with [glide-mlx](https://github.com/internet-projects/glide-mlx), not with `mlx_lm.server` or a plain `mlx_vlm` generate call. GLiDE reads its answer from a separate 255-way readout (`readout.safetensors`) and lets its full-attention layers read the whole prompt in both directions; generic MLX snippets do neither.

	## Results (MacBook Pro M5 Max, 128 GB, 29 October 2026)

	\| \| this checkpoint \|
	\|---\|---\|
	\| Same top answer as Fastino's own PyTorch code (bf16) \| 197 / 204 questions; probability difference mean 0.012 \|
	\| Yes / no and score questions \| 132 / 132 identical \|
	\| JevBench public (231, JevBench v1.2.1, `--adapter typesafe`) \| 213 / 231 (easy 48/48 · standard 72/72 · hard 93/111) \|
	\| Time per JevBench item (prompts up to about 4K tokens) \| 0.45 s median \|

	The answers that differ from Fastino's code are all `choice` questions with many options where the reference's top two options were close (top-two margin 0.09 or less). The comparison set has 72 requests with 204 questions: text and JSON states of 30 to 1,500 words, 12 image requests, and all three question types. The smallest build: about half the size of the 8-bit build, with probabilities that move a little more. For the closest match to the original, use [netprojects/GLiDE-mlx-8bit](https://huggingface.co/netprojects/GLiDE-mlx-8bit).

	## What is kept as in the original

	- Reading: the 16 full-attention layers see the whole prompt (no causal mask), the 48 Gated DeltaNet layers keep their recurrence, as GLiDE's `noncausal_full_attention`.
	- Answer: the last token's hidden state goes through GLiDE's 255-way readout, kept at its original precision; softmax over the options at the temperature in `decision_config.json`.
	- Prompts and images: GLiDE's own `prompt.py` and `images.py`, unchanged; the vision encoder stays at its original precision.
	- API: the same `POST /v1/systemone` request and response format as GLiDE's server.

	## Use

	```bash
	git clone https://github.com/internet-projects/glide-mlx && cd glide-mlx
	uv venv --python 3.12 .venv && uv pip install --python .venv/bin/python -r requirements.txt
	hf download netprojects/GLiDE-mlx-4bit --local-dir glide-mlx-4bit
	.venv/bin/python -m glide_mlx.server --model glide-mlx-4bit --port 8790 # http://127.0.0.1:8790/v1/systemone
	```

	Python: `from glide_mlx import GlideMLX; g = GlideMLX.from_pretrained("glide-mlx-4bit"); g.decide(state, questions)`.

	## License

	GLiDE is licensed under the Fastino Community License (`LICENSE`, `NOTICE`, `licenses/Qwen3.8-27B-LICENSE` are included). This conversion is a Derivative Work under that license and is distributed under the same terms. In short: commercial use requires displaying "Built with Fastino GLiDE" (section 4), and production commercial use by an entity with annual revenue above USD 10 million requires a separate agreement with Fastino (sections 2–3). Read the license for the full terms.

	Credits: Fastino (GLiDE), Qwen team (Qwen3.8-27B, Apache-2.0), MLX and mlx-vlm. Conversion by Paris Michalas, Partner at [NetProjects](https://netprojects.com). Not affiliated with Fastino.

	More NetProjects MLX builds: [NetProjects MLX builds collection](https://huggingface.co/collections/netprojects/netprojects-mlx-builds-6ae3b2683a59a1b4fc4c0c3a) · [all models at huggingface.co/netprojects](https://huggingface.co/netprojects)