Instructions to use Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS 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 Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS
# Run inference directly in the terminal:
llama cli -hf Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS
# Run inference directly in the terminal:
llama cli -hf Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS

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 Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS

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 Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS

Use Docker

docker model run hf.co/Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS

LM Studio
Jan
Ollama
How to use Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS with Ollama:
```
ollama run hf.co/Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS
```
Unsloth Desktop

How to use Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS

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": "Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS with Docker Model Runner:
```
docker model run hf.co/Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS
```

Lemonade

How to use Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-R9V-IQ4_XS-UD-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS

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 Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS

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 "Dyluhn/Qwen3.8-Flash-Next-R9V-IQ4_XS:UD-IQ4_XS" \
  --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"

Qwen3.8-Flash-Next-R9V-IQ4_XS / THIRD_PARTY_NOTICES.md

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Third-party notices and model provenance

This file records the attribution and source lineage of the Qwen3.8 Flash Next R9V IQ4_XS model package. Exact artifact hashes are in package.json and sources.lock.json.

Qwen3.8 Flash Next

Qwen is the model author and upstream rights holder. The target, MTP, projector, tokenizer and processor metadata, and derived PLE representation remain under Qwen Community License 1.0, included in this package as LICENSE. R9V's Apache-2.0 source license does not apply to these artifacts.

Project: https://huggingface.co/Qwen/Qwen3.8-Flash-Next
BF16 revision: de4b8e4d43b917e7706784d8bb445c9af86a3540
FP8 revision: 970c569adaca6b35532111fd6b27351b2baefe50

Unsloth IQ4_XS target

The three target shards are Unsloth's UD-IQ4_XS quantization of Qwen3.8 Flash Next.

Project: https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF
Revision: 8bdc666649440e9bdc97e16f3f75782c98478ff5

Unsloth is credited for producing and publishing the target quantization.

ggml-org vision projector

The Q8_0 vision projector is distributed by ggml-org and was produced with llama.cpp conversion tooling. R9V did not independently quantize it.

Project: https://huggingface.co/ggml-org/Qwen3.8-Flash-Next-GGUF
Revision: 01534bc2e1877d5de995b73d247d4459d273e688

R9V MTP and package assembly

R9V assembled the minimal MTP checkpoint from the official Qwen BF16 and FP8 checkpoints. R9V did not train these weights. The PLE payload is already embedded in target shard 2 and is derived locally rather than uploaded a second time.