Instructions to use mlx-community/Qwopus3.6-35B-A3B-Coder-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Qwopus3.6-35B-A3B-Coder-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("mlx-community/Qwopus3.6-35B-A3B-Coder-8bit") config = load_config("mlx-community/Qwopus3.6-35B-A3B-Coder-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
- Pi
How to use mlx-community/Qwopus3.6-35B-A3B-Coder-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 "mlx-community/Qwopus3.6-35B-A3B-Coder-8bit"
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": "mlx-community/Qwopus3.6-35B-A3B-Coder-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/Qwopus3.6-35B-A3B-Coder-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 "mlx-community/Qwopus3.6-35B-A3B-Coder-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 mlx-community/Qwopus3.6-35B-A3B-Coder-8bit
Run Hermes
hermes
mlx-community/Qwopus3.6-35B-A3B-Coder-8bit
This model mlx-community/Qwopus3.6-35B-A3B-Coder-8bit was converted
to MLX format from Jackrong/Qwopus3.6-35B-A3B-Coder
using mlx-vlm version 0.4.4.
This is a 8bit MLX quantized conversion. It keeps the source model's chat template and multimodal processor configuration for text/coding, image, and video-style inputs. The language model weights were quantized with MLX 8-bit affine quantization; the multimodal vision components are preserved for image/video inputs.
Refer to the original model card for model details, license, and intended use.
Use with mlx
pip install -U mlx-vlm
Image input
python -m mlx_vlm.generate \
--model mlx-community/Qwopus3.6-35B-A3B-Coder-8bit \
--max-tokens 512 \
--temperature 0.0 \
--prompt "Describe this image." \
--image <path_to_image>
Text / coding input
python -m mlx_vlm.generate \
--model mlx-community/Qwopus3.6-35B-A3B-Coder-8bit \
--max-tokens 512 \
--temperature 0.2 \
--prompt "Write a Python function that parses a JSONL file and counts records by label."
Notes
- This is a 8bit MLX quantized version of
Jackrong/Qwopus3.6-35B-A3B-Coder. - The model is intended for Apple Silicon inference with MLX.
- For multimodal usage, prefer
mlx-vlmrather than plainmlx-lm. - License: Apache 2.0, inherited from the source model metadata.
Conversion
mlx_vlm.convert \
--hf-path Jackrong/Qwopus3.6-35B-A3B-Coder \
--mlx-path Qwopus3.6-35B-A3B-Coder-8bit \
--quantize \
--q-bits 8 \
--q-group-size 64 \
--q-mode affine
- Downloads last month
- -
8-bit
Model tree for mlx-community/Qwopus3.6-35B-A3B-Coder-8bit
Base model
Qwen/Qwen3.6-35B-A3B