Instructions to use ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX 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("ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX")
config = load_config("ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX")

# 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 ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX"

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": "ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX 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 "ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX"

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 ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX"

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 "ilyakam/Geer-Ornith-1.0-35B-A3B-4-8bit-MLX" \
  --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"

docs(card): document Geer 0.1.0 validation

by ilyakam - opened 20 days ago

base: refs/heads/main

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@@ -51,17 +51,20 @@ available in the [Geer source repository][geer].
 ## Use with Geer
-This candidate is not yet selected automatically by a released Geer hardware
-profile. Geer model profiles must be tied to measured quality, long-context,
-memory-pressure, and full-stack validation on the applicable Apple Silicon
-memory class.
 ## Evaluation status
 Deep Reinforce AI publishes results for the upstream BF16 Ornith model in its
 [model card][upstream]. Geer has not yet reproduced the upstream benchmark
 suite for this mixed quantization, so upstream scores should not be treated as
-measured results for this conversion.
 ## License and attribution

 ## Use with Geer
+Geer downloads this repository at an immutable revision, verifies every file
+against `geer-build-manifest.json`, and activates the verified Hugging Face
+snapshot without copying the model into another directory. Geer 0.1.0 selects
+this conversion on 32 GB and 48 GB Macs, with 64K and 128K context windows
+respectively and BF16 KV cache.
 ## Evaluation status
 Deep Reinforce AI publishes results for the upstream BF16 Ornith model in its
 [model card][upstream]. Geer has not yet reproduced the upstream benchmark
 suite for this mixed quantization, so upstream scores should not be treated as
+measured results for this conversion. Although the upstream architecture and
+repository metadata support image-and-text inputs, Geer 0.1.0 has validated
+this conversion only for text-based agentic coding workflows.
 ## License and attribution