Instructions to use nathanw1014/dflash2-capgate-repro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use nathanw1014/dflash2-capgate-repro 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 nathanw1014/dflash2-capgate-repro
# Run inference directly in the terminal:
llama cli -hf nathanw1014/dflash2-capgate-repro

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nathanw1014/dflash2-capgate-repro
# Run inference directly in the terminal:
llama cli -hf nathanw1014/dflash2-capgate-repro

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 nathanw1014/dflash2-capgate-repro
# Run inference directly in the terminal:
./llama-cli -hf nathanw1014/dflash2-capgate-repro

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 nathanw1014/dflash2-capgate-repro
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nathanw1014/dflash2-capgate-repro

Use Docker

docker model run hf.co/nathanw1014/dflash2-capgate-repro

Llama new
LM Studio
Jan
Ollama
How to use nathanw1014/dflash2-capgate-repro with Ollama:
```
ollama run hf.co/nathanw1014/dflash2-capgate-repro
```
Unsloth Desktop

How to use nathanw1014/dflash2-capgate-repro with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nathanw1014/dflash2-capgate-repro

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": "nathanw1014/dflash2-capgate-repro"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nathanw1014/dflash2-capgate-repro with Docker Model Runner:
```
docker model run hf.co/nathanw1014/dflash2-capgate-repro
```

Lemonade

How to use nathanw1014/dflash2-capgate-repro with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nathanw1014/dflash2-capgate-repro

Run and chat with the model

lemonade run user.dflash2-capgate-repro-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use nathanw1014/dflash2-capgate-repro with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nathanw1014/dflash2-capgate-repro

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 nathanw1014/dflash2-capgate-repro

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nathanw1014/dflash2-capgate-repro with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nathanw1014/dflash2-capgate-repro

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 "nathanw1014/dflash2-capgate-repro" \
  --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"

DFlash2 capability-gate repro (INTENTIONALLY BROKEN test fixture)

Do not use this for serving. It is a deliberately corrupted draft model that exists only to reproduce a llama.cpp speculative-decoding bug and verify its fix (llama.cpp PR #27342, DFlash2 support).

dflash-27b-q8-selector-key-only.gguf is a byte-identical copy of inco.ai's DFlash v1 draft model for Qwen3.8-27B (MIT) with one metadata key injected: dflash.selector_top_k = 16. No selector tensors were added, so the metadata claims a DFlash2 selector lattice that the decode graph never builds.

What it reproduces

On llama.cpp builds where the host arms the DFlash2 path from the dflash.selector_top_k metadata key alone, run this file as -md with --spec-type draft-dflash against a Qwen3.8-27B target:

without the capability gate: no crash and no diagnostic. The v1 graph writes hidden states into t_h_nextn, the host decodes them as candidate token ids, and target verification rejects every draft. Measured on gfx1151/RADV (Vulkan): 0/1556 drafts accepted, 4.77 t/s vs ~11.4 t/s with speculation disabled - a silent 2.4x slowdown with correct output.
with the capability gate: a startup warning ("selector metadata but its decode graph does not build the selector lattice") and a clean fallback to the DFlash v1 draft path (162/295 accepted, 25.6 t/s in the same setup).

Rebuild it yourself

The 30-line generator (plus a second script covering the DSV4-backbone load-refusal case) lives on the fork branch dflash2-capgate-repro; the fix itself is the dflash2-capgate branch, commit 0b0f35d0e ("spec : capability-gate the DFlash2 draft path").

Credit: the underlying draft model is by inco.ai (MIT). This repository only adds one metadata key to make the mismatch reproducible.

Downloads last month: 7

GGUF

Model size

2B params

Architecture

dflash

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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