Instructions to use deepgrove/maple-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepgrove/maple-preview with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="deepgrove/maple-preview", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("deepgrove/maple-preview", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepgrove/maple-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepgrove/maple-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepgrove/maple-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepgrove/maple-preview

SGLang

How to use deepgrove/maple-preview with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "deepgrove/maple-preview" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepgrove/maple-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "deepgrove/maple-preview" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepgrove/maple-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepgrove/maple-preview with Docker Model Runner:
```
docker model run hf.co/deepgrove/maple-preview
```

maple-preview / README.md

deepgrove-team

Upload folder using huggingface_hub

ac1ddd7 verified about 15 hours ago

preview code

Raw

History Blame Contribute Delete

1.94 kB

	---
	license: mit
	language: en
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- causal-lm
	- mixture-of-experts
	- reasoning
	- ternary
	- custom-code
	---

	# Maple-Preview

	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.

	- 20B-A1B Model
	- 218 tok/s M4 Mac mini
	- 5.31 GB Checkpoint
	- 131,072 Token context

	![Maple-Preview speed and performance frontier](assets/01-speed-frontier.png)

	> [!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.

	## Architecture

	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.

	## Evaluation

	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.

	![Benchmark score comparison](assets/05-benchmark-scores-table.png)

	Capability comparison using the dense output head across LCBv6, AIME 2026, HMMT 2026, and GPQA-D.

	## Limitations

	This preview received minimal post-training for agentic tasks and only
	small-scale general reinforcement learning.

	## License

	Maple-Preview is released under the [MIT License](LICENSE).