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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/xautonomics/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF

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

How to use xautonomics/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use xautonomics/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use xautonomics/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.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 xautonomics/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.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 xautonomics/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use xautonomics/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF with OpenClaw:

Start the llama.cpp server

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

A per-layer GGUF library of Unsloth's unsloth/Qwen3.8-Flash-Next-GGUF (UD-IQ4_XS). These are the same quantized weights, sliced once into one file per transformer block plus the shared parts, so any contiguous layer window [A, B) can be assembled at load time. It's built for dreamcatcher, xAutonomics' fork of ik_llama.cpp, which serves a library either in a single process or split across hosts.


base model	`Qwen/Qwen3.8-Flash-Next`
source quant	`unsloth/Qwen3.8-Flash-Next-GGUF` · `UD-IQ4_XS` · pinned revision `38bb39ee97` · 3 shards
architecture	`qwen4exp` · 48 blocks
files	51 (embd, layer, other, output) · 87.8 GiB
integrity	`manifest.json`: blake2b-128 per whole file and per tensor
sliced with	`layer_distribution.slice` (the slicer commit is in the `SLICER_COMMIT` file); nothing was re-quantized

Support status

Measured on the dreamcatcher tree (status as of c2f8b680; the linked document is authoritative):

qwen4exp — CUDA verified (monolith coherent 23.2 tok/s; library head/tail ring coherent 17.4 tok/s; library ≡ monolith tensor-wise and forward-pass byte-identical). Ring token-exactness: [0,40)+[40,48) on CPU is token-identical to the monolith (64/64); other splits and CUDA rings diverge at near-tie tokens (6/15) from attention-kernel path differences (meta#98), not window metadata — the earlier "tail drops per_layer_token_embd" explanation was wrong (ple.layers = [1], always in the head; meta#91 fixed). llama-server --model-dir on the library is token-identical to the monolith (64/64, 1224/1224 hashes; meta#90 fixed). Vulkan fixed (was degenerate on every vendor — AMD RDNA3/RADV and NVIDIA coopmat1 alike; the cause was a short MULTI_ADD src0 descriptor range, not flash attention or BF16 tensor placement): NVIDIA coopmat1 verified coherent and agreeing with CUDA within rounding; RDNA3 re-measure pending; RDNA4 / Intel ANV not measured for this architecture (meta#88).

Only model files from huggingface.co/xautonomics are supported by dreamcatcher. Other GGUFs, including libraries you slice yourself, may load but are unsupported. Do not rename the files: the loader expects blk-NNNNN.gguf / parts-*.gguf / manifest.json exactly.

Layout

blk-00000.gguf … blk-00047.gguf   one transformer block each
parts-embd.gguf                          token embedding (every stage loads it)
parts-output.gguf                        output norm + head (every stage loads it)
parts-other.gguf                         remaining non-block tensors (every stage loads it)
manifest.json                            file list, sizes, per-file + per-tensor hashes, source provenance

A stage that serves layers [A, B) needs blk-A … blk-(B-1) plus the parts-* files and manifest.json.

Use

Single process (the whole model on one host):

llama-server --model-dir /models/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF

Multi-host ring (stages exchange hidden states over TCP; each host needs low-latency, direct node-to-node links — see the dreamcatcher README):

llama-stage-runner --role tail --listen 8081 --model-dir /models/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF --layers 24,48
llama-stage-runner --role head --connect <tail-host>:8081 --model-dir /models/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF --layers 0,24

Plan a split for your hosts (weights-only screen; the proof is a survived prefill), and verify the download before launching:

python3 -m layer_distribution.plan /models/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF/manifest.json --node a:16GiB:200GiB:8080 --node b:24GiB:200GiB:8081
python3 -c "from layer_distribution import verify; r = verify('/models/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF', '/models/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF/manifest.json'); print(r.passed, r.hash_verified)"

Provenance and license

Weights are byte-for-byte the tensors of unsloth/Qwen3.8-Flash-Next-GGUF at revision 38bb39ee97 (Unsloth's dynamic quantization of Qwen/Qwen3.8-Flash-Next). Source shard names and sizes are recorded in manifest.json (source.shards[], source.content_hash). License follows the base model: qwen-community-1.0, see LICENSE. Credit to the base-model authors and to Unsloth for the quantization.

About

Published by xAutonomics for dreamcatcher.

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GGUF

Model size

3B params

Architecture

qwen4exp

Hardware compatibility

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Model tree for xautonomics/Qwen3.8-Flash-Next-UD-IQ4_XS.LAYR.GGUF

Base model

Qwen/Qwen3.8-Flash-Next

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