Text Generation
MLX
Safetensors
qwen3_5
apple-silicon
speculative-decoding
qwen
qwen3
mtp
mtplx
local-ai
q4
conversational
4-bit precision
Instructions to use Youssofal/Qwen3.5-4B-MTPLX-Optimized-Speed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Youssofal/Qwen3.5-4B-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("Youssofal/Qwen3.5-4B-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 Youssofal/Qwen3.5-4B-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 "Youssofal/Qwen3.5-4B-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": "Youssofal/Qwen3.5-4B-MTPLX-Optimized-Speed" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Youssofal/Qwen3.5-4B-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 "Youssofal/Qwen3.5-4B-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 Youssofal/Qwen3.5-4B-MTPLX-Optimized-Speed
Run Hermes
hermes
- MLX LM
How to use Youssofal/Qwen3.5-4B-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 "Youssofal/Qwen3.5-4B-MTPLX-Optimized-Speed"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Youssofal/Qwen3.5-4B-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": "Youssofal/Qwen3.5-4B-MTPLX-Optimized-Speed", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 4,336 Bytes
28ac31a 5d19867 28ac31a 5d19867 28ac31a | 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 | {
"architectures": [
"Qwen3_5ForConditionalGeneration"
],
"image_token_id": 248056,
"library_name": "mlx",
"mlx_lm_extra_tensors": {
"mtp_file": "mtp.safetensors",
"mtp_tensor_count": 29
},
"model_type": "qwen3_5",
"mtplx_mtp_quantization": {
"bits": 4,
"description": "Official Qwen3.5-4B MTP head with layer-0 attention/MLP linears quantized to MLX affine INT4 group64; mtp.fc and norms stay BF16.",
"group_size": 64,
"mode": "affine",
"policy": "cyankiwi",
"prequantized": true
},
"mtplx_policy": {
"description": "mlx-community/Qwen3.5-4B-MLX-4bit trunk plus official Qwen3.5-4B MTP sidecar stored as body-int4 for MTPLX speed testing.",
"local_official_source": "/Users/youssof/.mtplx/sources/Qwen--Qwen3.5-4B",
"local_trunk_source": "/Users/youssof/.mtplx/sources/mlx-community--Qwen3.5-4B-MLX-4bit",
"mtp_quantization": {
"bits": 4,
"group_size": 64,
"linears": [
"mtp.layers.0.mlp.down_proj",
"mtp.layers.0.mlp.gate_proj",
"mtp.layers.0.mlp.up_proj",
"mtp.layers.0.self_attn.k_proj",
"mtp.layers.0.self_attn.o_proj",
"mtp.layers.0.self_attn.q_proj",
"mtp.layers.0.self_attn.v_proj"
],
"mode": "affine",
"norms": "bf16"
},
"name": "qwen35-4b-q4-official-mtp-body-int4-speed",
"quantization_family": "mlx-affine-q4",
"source": "Qwen/Qwen3.5-4B",
"trunk_quantization": {
"bits": 4,
"group_size": 64,
"mode": "affine"
},
"trunk_source": "mlx-community/Qwen3.5-4B-MLX-4bit"
},
"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,
"dtype": "bfloat16",
"eos_token_id": 248044,
"full_attention_interval": 4,
"head_dim": 256,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 9216,
"layer_types": [
"linear_attention",
"linear_attention",
"linear_attention",
"full_attention",
"linear_attention",
"linear_attention",
"linear_attention",
"full_attention",
"linear_attention",
"linear_attention",
"linear_attention",
"full_attention",
"linear_attention",
"linear_attention",
"linear_attention",
"full_attention",
"linear_attention",
"linear_attention",
"linear_attention",
"full_attention",
"linear_attention",
"linear_attention",
"linear_attention",
"full_attention",
"linear_attention",
"linear_attention",
"linear_attention",
"full_attention",
"linear_attention",
"linear_attention",
"linear_attention",
"full_attention"
],
"linear_conv_kernel_dim": 4,
"linear_key_head_dim": 128,
"linear_num_key_heads": 16,
"linear_num_value_heads": 32,
"linear_value_head_dim": 128,
"mamba_ssm_dtype": "float32",
"max_position_embeddings": 262144,
"mlp_only_layers": [],
"model_type": "qwen3_5_text",
"mtp_num_hidden_layers": 1,
"mtp_use_dedicated_embeddings": false,
"num_attention_heads": 16,
"num_hidden_layers": 32,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"mrope_interleaved": true,
"mrope_section": [
11,
11,
10
],
"partial_rotary_factor": 0.25,
"rope_theta": 10000000,
"rope_type": "default"
},
"tie_word_embeddings": true,
"use_cache": true,
"vocab_size": 248320
},
"tie_word_embeddings": true,
"transformers_version": "4.57.0.dev0",
"video_token_id": 248057,
"vision_config": {
"deepstack_visual_indexes": [],
"depth": 24,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1024,
"in_channels": 3,
"initializer_range": 0.02,
"intermediate_size": 4096,
"model_type": "qwen3_5",
"num_heads": 16,
"num_position_embeddings": 2304,
"out_hidden_size": 2560,
"patch_size": 16,
"spatial_merge_size": 2,
"temporal_patch_size": 2
},
"vision_end_token_id": 248054,
"vision_start_token_id": 248053
}
|