Model reference · open weights
maple is an open-weight language model from deepgrove. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | deepgrove |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 20.2B |
| Context | 128k tokens |
| Runs with | transformers |
| Released | 2026-08-04 |
| Popularity | 4k downloads / month |
| Licence | Open weights |
About
DeepGrove · 2026
Today we introduce Maple-Preview, an open-source 20B-A1B ternary-weight reasoning LLM. Maple-Preview has SOTA reasoning for its weight class and is even competitive with larger models. It solves IMO-level problems and runs at 200+ tokens/sec on a Mac mini M4, 5–16× faster than efficient models like Gemma 4, Qwen3.5, and gpt-oss.
[!NOTE] The included Transformers implementation depends on Triton and FlashAttention and is intended for a compatible CUDA environment. The reported Apple Silicon result uses a separate on-device runtime.
Maple-Preview is a 20B-A1B reasoning model designed from the start for efficient on-device inference. It utilizes a 24-layer, 256-expert (8 active) configuration with 3:1 SWA-512:GA attention.
On benchmarks, Maple-Preview sets a new point on the Pareto frontier for both memory-to-performance and speed-to-performance, demonstrating its strong reasoning capabilities. However, we note that this preview is focused primarily on raw reasoning and, as such, may underperform on agentic benchmarks. We intend to continue improving general performance through extended training before Maple's full release.
Capability comparison using the dense output head across LCBv6, AIME 2026, HMMT 2026, and GPQA-D.
This preview received minimal post-training for agentic tasks and only small-scale general reinforcement learning.
Maple-Preview is released under the MIT License.
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys maple for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (maple below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"maple","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.