Instructions to use netprojects/GLiDE-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use netprojects/GLiDE-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("netprojects/GLiDE-mlx-8bit")
config = load_config("netprojects/GLiDE-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 netprojects/GLiDE-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 "netprojects/GLiDE-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": "netprojects/GLiDE-mlx-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use netprojects/GLiDE-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 "netprojects/GLiDE-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 netprojects/GLiDE-mlx-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Paris99 commited on 1 day ago

Commit

34bf61c

verified ·

1 Parent(s): 90a1464

Card: build line, collection link, licence file; AutoTrust-JEV names [d94b141f0f1d]

Browse files

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README.md +4 -0

README.md CHANGED Viewed

@@ -10,6 +10,8 @@ tags: [mlx, decision-model, decision-making, calibration, jevbench, qwen3_5, app
 ---
 # GLiDE — MLX 8-bit
 An 8-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. 28.2 GB of weights.
 **Built with Fastino GLiDE.** Published by [NetProjects](https://netprojects.com) (Internet Projects Ltd). Not affiliated with Fastino.
@@ -62,3 +64,5 @@ Python: `from glide_mlx import GlideMLX; g = GlideMLX.from_pretrained("glide-mlx
 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.

 ---
 # GLiDE — MLX 8-bit
+*Built and tested on Apple M5 Max (128 GB) · upstream revision GLiDE v1.2 (`d199b6d`)*
 An 8-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. 28.2 GB of weights.
 **Built with Fastino GLiDE.** Published by [NetProjects](https://netprojects.com) (Internet Projects Ltd). Not affiliated with Fastino.
 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)