Image-Text-to-Text
MLX
Safetensors
English
qwen3_5_mtp
quantized
mtp
speculative-decoding
draft-model
Instructions to use inferencerlabs/Qwen3.6-27B-MTP-MLX-Q4.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use inferencerlabs/Qwen3.6-27B-MTP-MLX-Q4.5 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("inferencerlabs/Qwen3.6-27B-MTP-MLX-Q4.5") config = load_config("inferencerlabs/Qwen3.6-27B-MTP-MLX-Q4.5") # 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
Upload model file
Browse files- config.json +120 -0
config.json
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{
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"block_size": 3,
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"model_type": "qwen3_5_mtp",
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"quantization_config": {
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"group_size": 64
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},
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"text_config": {
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_output_gate": true,
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"bos_token_id": 248044,
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"dtype": "bfloat16",
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"eos_token_id": 248044,
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"full_attention_interval": 4,
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"head_dim": 256,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 17408,
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"layer_types": [
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"full_attention",
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"linear_attention",
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"full_attention"
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],
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"linear_conv_kernel_dim": 4,
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"linear_key_head_dim": 128,
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"linear_num_key_heads": 16,
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"linear_num_value_heads": 48,
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"linear_value_head_dim": 128,
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"mamba_ssm_dtype": "float32",
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"max_position_embeddings": 262144,
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"model_type": "qwen3_5_text",
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"mtp_num_hidden_layers": 1,
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"mtp_use_dedicated_embeddings": false,
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"num_attention_heads": 24,
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"num_hidden_layers": 64,
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"num_key_value_heads": 4,
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"output_gate_type": "swish",
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"pad_token_id": null,
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"partial_rotary_factor": 0.25,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"mrope_interleaved": true,
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"mrope_section": [
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],
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"partial_rotary_factor": 0.25,
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"rope_theta": 10000000,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"use_cache": true,
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"vocab_size": 248320
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},
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"tie_word_embeddings": false,
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"vision_config": {}
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}
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