Instructions to use Anbeeld/Alpamayo-R1-10B-DFlash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Anbeeld/Alpamayo-R1-10B-DFlash-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Anbeeld/Alpamayo-R1-10B-DFlash-GGUF with Ollama:
```
ollama run hf.co/Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Anbeeld/Alpamayo-R1-10B-DFlash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Anbeeld/Alpamayo-R1-10B-DFlash-GGUF with Docker Model Runner:
```
docker model run hf.co/Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M
```

Lemonade

How to use Anbeeld/Alpamayo-R1-10B-DFlash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Alpamayo-R1-10B-DFlash-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Anbeeld/Alpamayo-R1-10B-DFlash-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Anbeeld/Alpamayo-R1-10B-DFlash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Anbeeld/Alpamayo-R1-10B-DFlash-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Alpamayo-R1-10B-DFlash-GGUF / README.md

Anbeeld

Fix README metadata and assets

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	---
	base_model: z-lab/Alpamayo-R1-10B-DFlash
	tags:
	- safetensors
	- qwen3
	- flashdrive
	- dflash
	- speculative-decoding
	- block-diffusion
	- draft-model
	- autonomous-driving
	- vision-language-action
	- robotics
	- en
	- arxiv:2602.06036
	- base_model:z-lab/Alpamayo-R1-10B
	- base_model:finetune:z-lab/Alpamayo-R1-10B
	- license:other
	- region:us
	---

	# Alpamayo R1 10B DFlash GGUF

	GGUF quantizations of [z-lab DFlash draft model](https://huggingface.co/z-lab/Alpamayo-R1-10B-DFlash) for [Alpamayo R1 10B](https://huggingface.co/z-lab/Alpamayo-R1-10B).

	Use with [BeeLlama.cpp](https://github.com/Anbeeld/beellama.cpp), a llama.cpp fork with advanced quantization features.

	---

	# Alpamayo 1 (R1) — DFlash draft

	Flash Vision-Language-Action Inference for Autonomous Driving

	[![Paper](https://img.shields.io/badge/arXiv-2602.06036-b31b1b.svg)](https://arxiv.org/abs/2602.06036)
	[![GitHub](https://img.shields.io/badge/GitHub-FlashDrive-181717?logo=github)](https://github.com/z-lab/flashdrive)
	[![Blog](https://img.shields.io/badge/Blog-FlashDrive-blue)](https://z-lab.ai/projects/flashdrive/)
	[![Models](https://img.shields.io/badge/%F0%9F%A4%97-Models-yellow)](https://huggingface.co/collections/z-lab/flashdrive)

	[DFlash](https://github.com/z-lab/dflash) draft model for [z-lab/Alpamayo-R1-10B](https://huggingface.co/z-lab/Alpamayo-R1-10B), used by [FlashDrive](https://github.com/z-lab/flashdrive) to accelerate the chain-of-causation reasoning of [Alpamayo 1 (R1)](https://huggingface.co/nvidia/Alpamayo-R1-10B).

	DFlash (ICML 2026) uses a lightweight block-diffusion draft to propose several tokens in parallel; the target verifies each block in a single forward, preserving its output distribution. This draft is a 2-layer Qwen3-style network (block size 8) conditioned on target hidden states from layers 24/30/31/32/34. The repository also ships `mask_embedding.pt`, the trained mask-token embedding FlashDrive appends to the target's embedding table.

	> [!NOTE]
	> Not a standalone language model. FlashDrive attaches it to the [base checkpoint](https://huggingface.co/z-lab/Alpamayo-R1-10B) automatically — you do not load this repository directly.

	## Usage

	```python
	import flashdrive

	# from_pretrained fetches this -DFlash checkpoint automatically
	model = flashdrive.from_pretrained("z-lab/Alpamayo-R1-10B")
	```

	See the [base model card](https://huggingface.co/z-lab/Alpamayo-R1-10B) and the [FlashDrive repository](https://github.com/z-lab/flashdrive) for the full pipeline.

	## License

	This checkpoint is derived from NVIDIA's Alpamayo weights and is governed by the [NVIDIA License](https://huggingface.co/nvidia/Alpamayo-1.5-10B/blob/main/LICENSE), which permits non-commercial use only and extends to derivative works. The [FlashDrive](https://github.com/z-lab/flashdrive) inference code is separately released under the [MIT License](https://github.com/z-lab/flashdrive/blob/main/LICENSE).

	## Citation

	```bibtex
	@inproceedings{chen2026dflash,
	title = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
	author = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
	booktitle = {International Conference on Machine Learning (ICML)},
	year = {2026}
	}
	```

	```bibtex
	@article{li2026flashdrive,
	title = {{FlashDrive: Flash Vision-Language-Action Inference for Autonomous Driving}},
	author = {Li, Zekai and Liang, Yihao and Zhang, Hongfei and Chen, Jian and Liang, Yesheng and Liu, Zhijian},
	year = {2026}
	}
	```