Instructions to use zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6"

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": "zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6 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 "zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6"

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 zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6

Run Hermes

hermes

OpenClaw new

How to use zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6"

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 "zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6" \
  --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"

MLX LM

How to use zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6 / README.md

zecanard

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language:
  - en
  - zh
  - ko
  - ja
license: apache-2.0
base_model: Jackrong/Gemopus-4-26B-A4B-it
library_name: mlx
tags:
  - mlx
  - 3-bit
  - gemma
  - gemma4
  - instruction-tuned
  - reasoning
  - alignment
pipeline_tag: text-generation

🦆 zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6

This model was converted to MLX from Jackrong/Gemopus-4-26B-A4B-it using mlx-vlm version 0.6.3. Please refer to the original model card for more details.

🌟 Quality

Mixed-precision quantized language model with an effective 4.295 bits per weight. Combines the size and speed benefits of a 3-bit quant with higher precision where it matters most.

mlx_vlm.convert --quantize --q-group-size 32 --quant-predicate mixed_3_6

🛠️ Customizations

This quant includes a bugfix for tools calling. It is aware of the current date, and also enables thinking (if available). You may disable this behavior by deleting the following line from the chat template, or changing true to false:

{%- set enable_thinking = true %}

You may need to adjust your environment’s Reasoning Section Parsing to recognize <|channel>thought as the Start String, and <channel|> as the End String.

🖥️ Use with `mlx`

pip install -U mlx-vlm

mlx_vlm.generate --model zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6 --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>

🦆 zecanard/Gemopus-4-26B-A4B-it-MLX-3bit-mixed_3_6

🌟 Quality

🛠️ Customizations

🖥️ Use with mlx

🖥️ Use with `mlx`