Text Generation
MLX
Safetensors
qwen3_5
apple-silicon
speculative-decoding
qwen
qwen3
qwen3-next
mtp
mtplx
local-ai
conversational
4-bit precision
Instructions to use finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed"
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": "finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed 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 "finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed"
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 finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed
Run Hermes
hermes
- MLX LM
How to use finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "finbase0530/Qwen3.6-27B-MTPLX-Optimized-Speed", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 5,541 Bytes
0aa3682 | 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 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | {
"architectures": [
"Qwen3_5ForConditionalGeneration"
],
"eos_token_id": [
248046,
248044
],
"image_token_id": 248056,
"language_model_only": false,
"mlx_lm_extra_tensors": {
"mtp_file": "mtp.safetensors",
"mtp_tensor_count": 15
},
"model_type": "qwen3_5",
"mtplx_mtp_quantization": {
"bits": 4,
"description": "Load calibrated CyanKiwi MTP layer linears as packed MLX INT4; keep mtp.fc and MTP norms BF16.",
"group_size": 32,
"mode": "affine",
"policy": "cyankiwi",
"prequantized": true
},
"mtplx_policy": {
"base_trunk": "models/Qwen3.6-27B-MLXCommunity-4bit-mtp-graft",
"description": "Legacy flat 4-bit MLXCommunity trunk with calibrated CyanKiwi prequantized INT4 MTP sidecar; speed-ceiling probe only, not production default.",
"main_policy": "mlx-community-flat-4bit",
"mtp_policy": "cyankiwi-calibrated-int4-prequantized",
"mtp_sidecar_source": "models/Qwen3.6-27B-MTPLX-CyanKiwi-Packed-BF16-INT4-v3",
"name": "mlx-community-4bit-cyankiwi-mtp-speed-probe",
"source": "Qwen/Qwen3.6-27B"
},
"quantization": {
"bits": 4,
"group_size": 64,
"mode": "affine"
},
"quantization_config": {
"bits": 4,
"group_size": 64,
"mode": "affine"
},
"text_config": {
"attention_bias": false,
"attention_dropout": 0.0,
"attn_output_gate": true,
"bos_token_id": 248044,
"dtype": "bfloat16",
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"full_attention_interval": 4,
"head_dim": 256,
"hidden_act": "silu",
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"linear_conv_kernel_dim": 4,
"linear_key_head_dim": 128,
"linear_num_key_heads": 16,
"linear_num_value_heads": 48,
"linear_value_head_dim": 128,
"mamba_ssm_dtype": "float32",
"max_position_embeddings": 262144,
"model_type": "qwen3_5_text",
"mtp_num_hidden_layers": 1,
"mtp_use_dedicated_embeddings": false,
"num_attention_heads": 24,
"num_hidden_layers": 64,
"num_key_value_heads": 4,
"output_gate_type": "swish",
"pad_token_id": null,
"partial_rotary_factor": 0.25,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"mrope_interleaved": true,
"mrope_section": [
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],
"partial_rotary_factor": 0.25,
"rope_theta": 10000000,
"rope_type": "default"
},
"tie_word_embeddings": false,
"use_cache": true,
"vocab_size": 248320
},
"tie_word_embeddings": false,
"transformers_version": "4.57.1",
"video_token_id": 248057,
"vision_config": {
"deepstack_visual_indexes": [],
"depth": 27,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"in_channels": 3,
"initializer_range": 0.02,
"intermediate_size": 4304,
"model_type": "qwen3_5",
"num_heads": 16,
"num_position_embeddings": 2304,
"out_hidden_size": 5120,
"patch_size": 16,
"spatial_merge_size": 2,
"temporal_patch_size": 2
},
"vision_end_token_id": 248054,
"vision_start_token_id": 248053
}
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