Image-Text-to-Text
MLX
Safetensors
English
mlx-vlm
qwen
qwen3.8
distillation
multimodal
4-bit precision
8-bit precision
bf16
Instructions to use PocketAiHub/Qwen3.8-9B-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use PocketAiHub/Qwen3.8-9B-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("PocketAiHub/Qwen3.8-9B-MLX") config = load_config("PocketAiHub/Qwen3.8-9B-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
- Atomic Chat
File size: 991 Bytes
c38a225 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | {
"image_processor": {
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Qwen3VLImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_pixels": 16777216,
"merge_size": 2,
"min_pixels": 65536,
"patch_size": 16,
"rescale_factor": 0.00392156862745098,
"temporal_patch_size": 2
},
"processor_class": "Qwen3VLProcessor",
"video_processor": {
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"fps": 2.0,
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"max_frames": 768,
"max_pixels": 25165824,
"merge_size": 2,
"min_frames": 4,
"min_pixels": 4096,
"patch_size": 16,
"rescale_factor": 0.00392156862745098,
"temporal_patch_size": 2,
"video_processor_type": "Qwen3VLVideoProcessor"
}
}
|