Image-Text-to-Text
MLX
Safetensors
qwen3_5
vision-language
apple-silicon
quantized
conversational
8-bit precision
Instructions to use incept5/Qwen3.8-27B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use incept5/Qwen3.8-27B-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("incept5/Qwen3.8-27B-MLX-8bit") config = load_config("incept5/Qwen3.8-27B-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
- Pi
How to use incept5/Qwen3.8-27B-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 "incept5/Qwen3.8-27B-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": "incept5/Qwen3.8-27B-MLX-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use incept5/Qwen3.8-27B-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 "incept5/Qwen3.8-27B-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 incept5/Qwen3.8-27B-MLX-8bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use incept5/Qwen3.8-27B-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 "incept5/Qwen3.8-27B-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 "incept5/Qwen3.8-27B-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"
Verification: add reproducible test image, inference snippet, and results
Browse files
README.md
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## Verification (smoke test)
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```
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The capital of France is Paris.
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```
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**Vision
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(test image: red square top-left, blue circle top-right, green inverted triangle bottom-centre, label "MLX VISION TEST")
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```
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Based on the image provided, here is a description of the shapes, their colors, and the text present:
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## Verification (smoke test)
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Smoke-tested on Apple silicon with `mlx-vlm` after quantization — a text prompt plus a
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vision prompt against a deterministic, network-free test image (coloured shapes + a text
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label), so the test is fully reproducible.
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### Test image
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### How it was run
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```python
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from mlx_vlm import load, generate
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from mlx_vlm.prompt_utils import apply_chat_template
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from mlx_vlm.utils import load_config
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model, processor = load("incept5/Qwen3.8-27B-MLX-8bit")
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config = load_config("incept5/Qwen3.8-27B-MLX-8bit")
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# vision
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msgs = [{"role": "user", "content": "Describe the shapes and their colours in this image, and read any text you see."}]
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prompt = apply_chat_template(processor, config, msgs, num_images=1)
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print(generate(model, processor, prompt, image=["test.jpg"], max_tokens=256, verbose=False))
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```
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(Text prompt used the same pattern with `num_images=0` and no `image=` argument.)
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### Results
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**Text** — _"In one sentence, what is the capital of France?"_
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The capital of France is Paris.
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```
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**Vision** — _"Describe the shapes and their colours in this image, and read any text you see."_
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```
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Based on the image provided, here is a description of the shapes, their colors, and the text present:
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