Maple Pro — preference-optimized Maple (20B-A1B ternary MoE)

A single-file GGUF of Maple (deepgrove/maple-preview, MIT) after a full distillation + DPO campaign: the strongest measured variant of this model family, improving or matching the baseline on all 12 evaluated axes.

Results (measured on a fixed local suite, identical seeds/conditions)

First and only configuration (of 17 tested) achieving simultaneous dominance across every axis. All numbers reproducible; eval suite and logs described in the model card below.

What's inside

• Full LoRA fusion: 64 attention LoRA tensors and 121 DPO-updated base tensors (norms, expert routers, QK-norms) baked into a single GGUF. Attention projections stored as F16 (mixed precision; layers 0–7 keep the base ternary TQ2_0 attention); ternary expert weights untouched (byte-identical to the official base).
• Runtime: 6.6 GB, ~1B active parameters.
• Context: 128K native, GGUF metadata allows 256K — verified to work natively with no flags (needle-tested — see Context section). No other changes to the model: the 256K build differs from the 128K original by a single metadata field.

Context (measured)

5-needle recall (wikitext-2 haystack, depths 10/30/50/70/90%), same seeds:

No length degradation up to 256K: the hybrid SWA design (18/24 layers at window 512) extrapolates ×2 natively, so 256K works by just setting the context size — no rope-scaling flags needed (LM Studio: set the slider). Remaining misses are section-binding confusions between same-shape needles, uniform across lengths. 384K+ is out of reach without long-context training.

Runtime (important)

The maple architecture is not in upstream llama.cpp. Use the DeepGrove fork (Apache-2.0):

git clone --depth 1 https://github.com/deepgrove-ai/llama.cpp
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j --target llama-server

The Metal backend of the current fork does not implement the ternary TQ2_0 matmuls (experts, and the ternary attention of layers 0–7), so serve with those tensors on CPU:

./build/bin/llama-server -m maple-pro.gguf --port 8080 -t 8 \
  --cpu-moe --no-op-offload \
  -ot 'blk\.[0-7]\.attn_[a-z]+\.weight=CPU'

OpenAI-compatible API on :8080. LM Studio works too: the GGUF was verified on LM Studio's bundled runtime (2.65.1) — just load it and set the context size. Measured decode on an M4 16 GB (idle): ~75 tok/s (base maple-preview: ~94 tok/s — Pro trades some bandwidth for its F16 fused attention).

Serving at 256K

The GGUF advertises a 262144 context: just set the context size to 256K — no rope-scaling flags required (native ×2 extrapolation is needle-verified, see Context section). On a 16 GB machine use quantized KV cache to fit:

./build/bin/llama-server -m maple-pro.gguf --port 8080 -t 8 \
  --ctx-size 262144 \
  --cpu-moe --no-op-offload \
  -ot 'blk\.[0-7]\.attn_[a-z]+\.weight=CPU' \
  --cache-type-k q8_0 --cache-type-v q8_0

KV cache quantization is your choice; q8_0 was used in the tests (~10 GB total on a 16 GB machine).

Training recipe (summary)

1. Semi on-policy distillation: the student samples its own solutions on verified tasks (GSM8K gold answers, IFEval-style constraint checkers, sandboxed coding tests); a frontier teacher API model (GLM-5.3-Flash) corrects only the failures. ~3,000 verified on-policy samples + corrections.
2. SFT on-policy multi-domain (the 11/12-axes intermediate).
3. DPO on preference pairs (student error ↔ verified teacher correction), full-parameter (attention LoRA + norms/routers), SFT-anchored.
4. Zero-tolerance gate: a candidate ships only if it matches or beats the baseline on all axes at once. 16 configurations were rejected by this gate before this one passed.

Credits & license

• Base model: deepgrove/maple-preview and its GGUF release (MIT).
• Runtime: deepgrove-ai/llama.cpp fork.
• This model: MIT, derivative work with attribution. Trained locally on a single M4 16 GB via MLX.
• Made by West — madebywest.com