Views
No views yet
temp=0.6 / top_p=0.95 / top_k=20) using the model's own built-in MTP heads — no external drafter, no greedy hack.1pip install mtplx
2mtplx startmlx-community/Qwen3.5-4B-MLX-8bit MLX affine 8-bit
trunk and grafts back the official native MTP head from Qwen/Qwen3.5-4B. The
MTP head is stored as mtp.safetensors; layer-0 attention/MLP linears are
quantized to 4-bit affine group-64, while mtp.fc and the MTP norms stay BF16.mtplx startCustom Hugging Face repo, then enter:Youssofal/Qwen3.5-4B-Optimized-MTPLXmtplx_runtime.jsonmax_tokens=48, temperature=0.6, top_p=0.95, top_k=20).Youssofal/Qwen3.5-4B-MTPLX-Optimized-Speed) remains faster in
absolute tok/s because its 4-bit trunk has a faster AR baseline.1{
2 "bits": 4,
3 "group_size": 64,
4 "mode": "affine",
5 "output_size_bytes": 86701040,
6 "output_tensor_count": 29,
7 "policy": "cyankiwi",
8 "quantization": "body-int4",
9 "quantized_linears": {
10 "mtp.layers.0.mlp.down_proj": {"bits": 4, "group_size": 64, "mode": "affine"},
11 "mtp.layers.0.mlp.gate_proj": {"bits": 4, "group_size": 64, "mode": "affine"},
12 "mtp.layers.0.mlp.up_proj": {"bits": 4, "group_size": 64, "mode": "affine"},
13 "mtp.layers.0.self_attn.k_proj":{"bits": 4, "group_size": 64, "mode": "affine"},
14 "mtp.layers.0.self_attn.o_proj":{"bits": 4, "group_size": 64, "mode": "affine"},
15 "mtp.layers.0.self_attn.q_proj":{"bits": 4, "group_size": 64, "mode": "affine"},
16 "mtp.layers.0.self_attn.v_proj":{"bits": 4, "group_size": 64, "mode": "affine"}
17 },
18 "source_tensor_count": 15
19}pip install mtplx