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

GGUF quantizations of z-lab DFlash draft model for Alpamayo R1 10B.

Use with BeeLlama.cpp, a llama.cpp fork with advanced quantization features.

Alpamayo 1 (R1) — DFlash draft

Flash Vision-Language-Action Inference for Autonomous Driving

DFlash draft model for z-lab/Alpamayo-R1-10B, used by FlashDrive to accelerate the chain-of-causation reasoning of Alpamayo 1 (R1).

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.

Not a standalone language model. FlashDrive attaches it to the base checkpoint automatically — you do not load this repository directly.

Usage

import flashdrive

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

See the base model card and the FlashDrive repository for the full pipeline.

License

This checkpoint is derived from NVIDIA's Alpamayo weights and is governed by the NVIDIA License, which permits non-commercial use only and extends to derivative works. The FlashDrive inference code is separately released under the MIT License.

Citation

@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}
}

@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}
}