---
license: mit
base_model:
- deepgrove/maple-preview
tags:
- maple
- gguf
- dpo
- preference-optimization
- on-policy-distillation
- moe
- ternary
- apple-silicon
library_name: gguf
---

# 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)

| Axis | Base Maple | **Maple Pro** |
|---|---|---|
| Coding probe (26 tasks) | 20 | **24** |
| GSM8K (pooled n=200) | 90/100 | **90.5/100** |
| HumanEval+ (50) | 44 | 44 |
| IFEval official checker (100) | 63 | **65** |
| Tools eval (12) | 11 | **12/12** |
| Knowledge (25) | 22 | **23** |
| Browser-agent probe (10) | 3 | **5** |
| IF-10 / IF-30 | 7 / 19 | **8** / 19 |
| DROP-30 | 20 | 20 |
| Median tokens/answer | 759 | **570 (−25%)** |

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:

| Context | Mode | Recall |
|---|---|---|
| 16K–131K | native | 3/5 |
| **256K** | **native (no flags)** | **4/5** |
| 256K | YaRN ×2 | 4/5 (same) |
| 384K | YaRN ×3 | 2/5 — fails |

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):

```sh
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:

```sh
./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:

```sh
./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](https://huggingface.co/deepgrove/maple-preview)
  and its GGUF release (MIT).
- Runtime: [deepgrove-ai/llama.cpp](https://github.com/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](https://madebywest.com)