Instructions to use julianmb/Qwen3.8-Flash-Next-IQ4_XS-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 julianmb/Qwen3.8-Flash-Next-IQ4_XS-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 julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF:IQ4_XS

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "julianmb/Qwen3.8-Flash-Next-IQ4_XS-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": "julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF:IQ4_XS

Ollama
How to use julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF with Ollama:
```
ollama run hf.co/julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF:IQ4_XS
```
Unsloth Desktop

How to use julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF: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": "julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-IQ4_XS-GGUF-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use julianmb/Qwen3.8-Flash-Next-IQ4_XS-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 julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF: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 julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF: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 "julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF: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 GGUFs — provenance-verified quants for Strix Halo

Two files:

file	size	what it is
`Qwen3.8-Flash-Next-IQ4_XS-PLE.gguf`	91 GiB	recommended daily driver — iq4_xs trunk with the 27G PLE n-gram table at iq4_nl
`Qwen3.8-Flash-Next-IQ4_XS.gguf`	116 GiB	static reference quant, PLE at q8_0
`mtp-Qwen3.8-Flash-Next-Q8_0.gguf`	3.9 GiB	MTP draft sidecar for nathanw1014-lineage engines (fork-specific — will NOT load on apepojken/mainline)

provenance

every quant descends from an F16 that was byte-verified against the official Qwen/Qwen3.8-Flash-Next-FP8 checkpoint: hyper-connection norms folded to (1 + w) (97/97 tensors — the converter bug that produces deterministic garbage is fixed in our pipeline), PLE fp8 scale applied, expert stacking identity probed 512x3, GDN v-head reorder checked. details: https://github.com/julianmb/haloq38flash

the PLE cut (what makes the 91G special)

the 51B-parameter n-gram lookup table was moved from q8_0 (54G) to iq4_nl (27G) via --tensor-type. hash-gathered lookup rows tolerate the precision drop — verified by smoke and full benchmark, no degradation observed:

depth	static 116G plain/mtp t/s	PLE 91G plain/mtp t/s
0	29.2 / 48.4	29.9 / 53.1
8k	22.9 / 42.8	24.1 / 56.4
32k	19.5 / 29.5	20.1 / 30.2

prefill at 32k: 384 → 397 t/s. no collapse at depth. engine: nathanw1014 strix-halo-vulkan (ad914eb), vulkan/radv, q8_0 KV, -ub 2048, temp 0.

fork compatibility caveat (important)

the iq4_nl PLE rows assert in SOME forks: engines that feed gathered PLE rows directly as mul_mat B operands without dequantizing (apepojken qwen4exp-spec-mtp) abort at ggml-vulkan.cpp:7794 (b_type must be F32/F16/Q8_1). verified working on nathanw1014 strix-halo-vulkan. if your engine asserts on load or first token, use the 116G static file instead.

provenance note

the same --tensor-type cut applied to unverified-source quants will NOT fix a broken converter (hc norms, PLE scale) — garbage in, garbage out. ours is built from a fixed, audited pipeline.

128k+ context caveat (measured)

the depth story is not monotonic. measured on the same engine (nathanw1014 vulkan, q8_0 kv, temp 0, single runs):

depth	static 116g plain/mtp t/s	PLE 91g plain/mtp t/s
0	29.2 / 48.4	29.9 / 53.1
8k	22.9 / 42.8	24.1 / 56.4
32k	19.5 / 29.5	20.1 / 30.2
128k	10.8 / 26.9	11.0 / 18.6

plain decode collapses with depth on both quants (~11 t/s at 128k) — the cost is context-mechanics (sparse-attention indexer), not the quant.
with mtp at 128k the PLE quant measured SLOWER than static (18.6 vs 26.9, single runs): plausible draft-acceptance drop from ple quantization noise compounding over deep n-gram history. unverified mechanism, n=1 caveat.
practical: <=32k work → PLE file. 128k+ contexts → static file.

license

qwen community license 1.0 (distribution permitted with notice; maas restrictions apply). base model: Qwen/Qwen3.8-Flash-Next.

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GGUF

Model size

177B params

Architecture

qwen4exp

Hardware compatibility

4-bit

8-bit

Model tree for julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF

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