Instructions to use antirez/qwen3.8-flash-next-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 antirez/qwen3.8-flash-next-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 antirez/qwen3.8-flash-next-gguf
# Run inference directly in the terminal:
llama cli -hf antirez/qwen3.8-flash-next-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf antirez/qwen3.8-flash-next-gguf
# Run inference directly in the terminal:
llama cli -hf antirez/qwen3.8-flash-next-gguf

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 antirez/qwen3.8-flash-next-gguf
# Run inference directly in the terminal:
./llama-cli -hf antirez/qwen3.8-flash-next-gguf

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 antirez/qwen3.8-flash-next-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf antirez/qwen3.8-flash-next-gguf

Use Docker

docker model run hf.co/antirez/qwen3.8-flash-next-gguf

LM Studio
Jan

vLLM

How to use antirez/qwen3.8-flash-next-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "antirez/qwen3.8-flash-next-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "antirez/qwen3.8-flash-next-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/antirez/qwen3.8-flash-next-gguf

Ollama
How to use antirez/qwen3.8-flash-next-gguf with Ollama:
```
ollama run hf.co/antirez/qwen3.8-flash-next-gguf
```
Unsloth Desktop

How to use antirez/qwen3.8-flash-next-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf antirez/qwen3.8-flash-next-gguf

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": "antirez/qwen3.8-flash-next-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use antirez/qwen3.8-flash-next-gguf with Docker Model Runner:
```
docker model run hf.co/antirez/qwen3.8-flash-next-gguf
```

Lemonade

How to use antirez/qwen3.8-flash-next-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull antirez/qwen3.8-flash-next-gguf

Run and chat with the model

lemonade run user.qwen3.8-flash-next-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use antirez/qwen3.8-flash-next-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 antirez/qwen3.8-flash-next-gguf

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 antirez/qwen3.8-flash-next-gguf

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use antirez/qwen3.8-flash-next-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf antirez/qwen3.8-flash-next-gguf

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 "antirez/qwen3.8-flash-next-gguf" \
  --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 for DwarfStar

Self-contained GGUFs for DwarfStar, with original BF16 n-grams read directly from disk. Keep the file on a local SSD. No n-gram sidecar is needed. MTP weights are included; enable them with --mtp. Requires DwarfStar's native BF16 n-gram reader. Older versions expecting --ple cannot load these files.

File	File size	Main/MTP weights
Qwen3.8-Flash-Next-Q2.gguf	137.10 GiB	41.73 GiB
Qwen3.8-Flash-Next-Q4.gguf	165.11 GiB	69.74 GiB

Each file contains the same 95.37 GiB BF16 n-gram table. It is not made resident or quantized. Context and runtime buffers require additional RAM.

The main/MTP tensor bytes come unchanged from Ivan Fioravanti's Q2 and Q4 releases. Q2 has imatrix-calibrated IQ2_XXS gate/up and Q2_K down routed experts, with the down rows padded from 640 to 768 inputs. Q4 has calibrated Q4_K gate/up and MXFP4 down. Other tensor formats and MTP weights are unchanged.

The n-grams are copied byte-for-byte from Qwen's original checkpoint at de4b8e4d43b917e7706784d8bb445c9af86a3540, including its padding rows. The packer checks the original hash constants and verifies every copied payload. These are quantized language-model weights, not lossless copies of the whole original model.

Original model: Qwen. Quantized main/MTP releases: Ivan Fioravanti. Native n-gram packaging and disk-only integration: Salvatore Sanfilippo / DwarfStar. The original Qwen Community License is included as LICENSE.

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GGUF

Model size

188B params

Architecture

qwen4exp

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for antirez/qwen3.8-flash-next-gguf

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

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