Instructions to use nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-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 nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-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 nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

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 nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

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 nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

Use Docker

docker model run hf.co/nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-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": "nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

Ollama
How to use nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF with Ollama:
```
ollama run hf.co/nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0
```
Unsloth Desktop

How to use nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

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": "nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF with Docker Model Runner:
```
docker model run hf.co/nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0
```

Lemonade

How to use nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF-Q4_0

List all available models

lemonade list

Hermes Agent

How to use nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-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 nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

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 nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0

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 "nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF:Q4_0" \
  --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 Q4_0-Q8_0-out v3 (GGUF)

⚠️ Runtime Compatibility Notice

This is a qwen4exp architecture GGUF (Gated-DeltaNet hybrid + 512-expert routed MoE + MTP). Stock llama.cpp does not support this architecture. Use a qwen4exp-enabled llama.cpp build (e.g. the unslothai/llama.cpp fork with qwen4exp support) with --mtp-draft for speculative decoding.

Overview

Quantized GGUF build of Qwen3.8 Flash Next (general.size_label: 512x56B), the 48-layer Qwen4exp hybrid MoE (24 heads, 2 KV heads, GDN recurrent layers with full attention every 4 layers, 512 experts / 10 active, ngram-PLE layer, built-in MTP heads), 262,144-token native context.

Quantization: Q4_0 weights with Q8_0 output/embedding tensors (-Q8out v3 recipe), imatrix-calibrated (Qwen3.8-Flash-Next-calibration-v6, 926 entries / 582 chunks)
Shards: 3 (-00001-of-00003.gguf … -00003-of-00003.gguf), 96 GiB total
MTP: Multi-Token Prediction heads included for speculative decoding (7 proposal tokens)
Vision: vision projector tensors included (image-text-to-text capable)

Files

File	Size
`Qwen3.8-Flash-Next-Q4_0-Q8out-v3-00001-of-00003.gguf`	37.7 GiB
`Qwen3.8-Flash-Next-Q4_0-Q8out-v3-00002-of-00003.gguf`	37.8 GiB
`Qwen3.8-Flash-Next-Q4_0-Q8out-v3-00003-of-00003.gguf`	20.0 GiB

Download all three shards into the same directory, then load shard 1.

Usage (qwen4exp-enabled llama.cpp)

llama-server -m Qwen3.8-Flash-Next-Q4_0-Q8out-v3-00001-of-00003.gguf \
  --mmproj mmproj-f16.gguf -ngl 99 -c 262144 --port 8080

Built and benchmarked locally on Apple M5 Pro (64 GB unified memory) with the Slipstream streaming-MoE llama.cpp fork.

Downloads last month: 150

GGUF

Model size

177B params

Architecture

qwen4exp

Hardware compatibility

4-bit

Model tree for nitinpanj/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

(405)

this model