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"

Dyluhn commited on 21 days ago

Commit

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1 Parent(s): cee003d

Add 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.