Instructions to use dylpickle13/Qwen3.5-2B-oQ8-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dylpickle13/Qwen3.5-2B-oQ8-mtp 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("dylpickle13/Qwen3.5-2B-oQ8-mtp") config = load_config("dylpickle13/Qwen3.5-2B-oQ8-mtp") # 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 dylpickle13/Qwen3.5-2B-oQ8-mtp with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "dylpickle13/Qwen3.5-2B-oQ8-mtp"
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": "dylpickle13/Qwen3.5-2B-oQ8-mtp" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use dylpickle13/Qwen3.5-2B-oQ8-mtp 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 "dylpickle13/Qwen3.5-2B-oQ8-mtp"
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 dylpickle13/Qwen3.5-2B-oQ8-mtp
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use dylpickle13/Qwen3.5-2B-oQ8-mtp with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "dylpickle13/Qwen3.5-2B-oQ8-mtp"
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 "dylpickle13/Qwen3.5-2B-oQ8-mtp" \ --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"
Qwen3.5-2B-oQ8-mtp
This repository contains an MLX/oMLX-compatible oQ8 quantization of Qwen/Qwen3.5-2B. It is a derivative quantized release; all credit for the original model architecture, training, and base release belongs to Qwen.
Summary
- Base model:
Qwen/Qwen3.5-2B - Quantization: oMLX/oQ level 8, affine 8-bit, group size 64
- Runtime target: oMLX / MLX
- Format: MLX safetensors
- Dtype for preserved/unquantized tensors: bfloat16
- Multimodal: yes; vision tensors preserved
- Native MTP: yes; MTP tensors preserved
- License: Apache-2.0, inherited from the base model
Files
README.md
LICENSE
chat_template.jinja
config.json
merges.txt
model.safetensors
preprocessor_config.json
tokenizer.json
tokenizer_config.json
vocab.json
Verification
Verified before public release:
model.safetensorsopens successfully- safetensors metadata:
{"format": "mlx"} model.safetensorssize:2,735,417,896bytes- tensor count:
1020 - MTP tensor count:
29 - vision tensor count:
297 - clean private Hugging Face download: pass
- clean oMLX download/load from Hugging Face: pass
- text smoke test: prompt
Reply with exactly OK.returnedOK - image smoke test: red square returned
red
See PUBLISH_CHECK.md for release verification details.
Use with oMLX/OpenAI-compatible API
Example request once the model is downloaded into an oMLX server:
curl -s http://localhost:8000/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{
"model": "Qwen3.5-2B-oQ8-mtp",
"messages": [{"role": "user", "content": "Reply with exactly OK."}],
"max_tokens": 16,
"temperature": 0
}'
For vision use, send OpenAI-style multimodal chat content with an image_url item.
Quantization details
{
"model_path": "/Users/dylanrapanan/.omlx/models/Qwen/Qwen3.5-2B",
"oq_level": 8,
"group_size": 64,
"sensitivity_model_path": "",
"text_only": false,
"dtype": "bfloat16",
"preserve_mtp": true,
"auto_proxy_sensitivity": true
}
Notes
- This is not the original base model; it is a quantized derivative for local MLX/oMLX use.
- Native MTP is enabled by preserving the model's MTP heads. In oMLX, keep
mtp_enabledon; do not confuse this with external VLM drafter settings. - The model was published private-first, then clean-downloaded, tensor-verified, clean-loaded in oMLX, smoke-tested, and only then made public.
Attribution
All credit for the original model goes to Qwen/Alibaba Cloud. This repository only provides a quantized MLX/oMLX artifact for convenience.
- Downloads last month
- 39
8-bit