Instructions to use AtomicChat/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 AtomicChat/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 AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use AtomicChat/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 "AtomicChat/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": "AtomicChat/Qwen3.8-Flash-Next-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Ollama
How to use AtomicChat/Qwen3.8-Flash-Next-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M
```
Unsloth Desktop

How to use AtomicChat/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 AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M

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": "AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AtomicChat/Qwen3.8-Flash-Next-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AtomicChat/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 AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M

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 AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AtomicChat/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 AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M

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 "AtomicChat/Qwen3.8-Flash-Next-GGUF:Q4_K_M" \
  --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"

AlexAtomic commited on Aug 26

Commit

fba4cee

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1 Parent(s): a986af6

card: 27B-style header (README.md)

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README.md +18 -17

README.md CHANGED Viewed

@@ -21,24 +21,25 @@ tags:
 - llama.cpp
 ---
-<center>
-<div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;">
-<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>
-<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>
-<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>
 </div>
-<br/>
-<img src="https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF/resolve/main/hero.png" alt="Qwen3.8-Flash-Next" style="width:560px; max-width:100%; height:auto; margin-bottom:0.6em;"/>
-<div style="display:flex; justify-content:center; gap:0.5em;">
-<a href="https://huggingface.co/Qwen/Qwen3.8-Flash-Next"><strong>Base model: Qwen/Qwen3.8-Flash-Next</strong></a>
-</div>
-</center>
-**Qwen3.8-Flash-Next**, self-quantized to GGUF by [Atomic Chat](https://atomic.chat). Built straight from Qwen's original weights with a per-tensor importance matrix, which we publish alongside the quants. Runs fully offline. This is the first open-weight release of the architecture that will underpin Qwen4.
 ## Highlights
@@ -49,7 +50,7 @@ tags:
 - **262,144-token context**, extensible up to 1,000,000 tokens with RoPE scaling.
 - **Natively multimodal** (causal language model with a vision encoder, image-text-to-text). These GGUF quants cover the text path.
 - **Frontier coding and agentic scores** (Qwen-reported): LiveCodeBench v6 91.9, GPQA Diamond 91.7, SWE-bench Multilingual 81.0, CoWorkBench 73.9.
-- **Full imatrix quantization** over [`calibration_datav3`](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8).
 > [!NOTE]
 > These GGUFs are **self-quantized from the original weights**, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.
@@ -130,7 +131,7 @@ cmake --build llama.cpp/build --config Release -j --target llama-cli llama-serve
 1. Download `Qwen/Qwen3.8-Flash-Next` (original weights).
 2. Convert to GGUF with a [llama.cpp](https://github.com/ggerganov/llama.cpp) build that supports the Qwen3.8-Flash-Next architecture (Gated DeltaNet, Qwen Sparse Attention, n-gram embedding).
-3. Build an importance matrix over `calibration_datav3`.
 4. Quantize the ladder with `--imatrix`; `UD-Q4_K_XL` additionally pins the token-embedding and output tensors to `Q8_0`.
 ## License

 - llama.cpp
 ---
+# How to Run Qwen3.8-Flash-Next Locally
+<p style="margin-top: 0; margin-bottom: 0;">
+  <em>Built from Qwen's original weights with our own importance matrix. The <a href="https://huggingface.co/datasets/AtomicChat/calib-corpora">calibration corpora</a> behind our builds are public.</em>
+</p>
+<div style="display: flex; gap: 8px; align-items: center; margin-top: 10px; margin-bottom: 10px;">
+  <a href="https://atomic.chat/?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_qwen3_8_flash_next&utm_content=btn_atomic"><img src="https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF/resolve/main/btn_atomic.png" width="162" alt="Atomic Chat"></a>
+  <a href="https://discord.gg/8wGSsvmg4V"><img src="https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF/resolve/main/btn_discord.png" width="119" alt="Discord"></a>
+  <a href="https://github.com/AtomicBot-ai/Atomic-Chat"><img src="https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF/resolve/main/btn_github.png" width="115" alt="GitHub"></a>
 </div>
+<ul style="margin: 0 0 12px 0;">
+  <li>Qwen3.8-Flash-Next is the first open-weight release of the architecture behind Qwen4.</li>
+  <li>These GGUFs are self-quantized from Qwen's original weights with our own importance matrix, published alongside the quants.</li>
+  <li>The quants are still uploading and need a llama.cpp build with Qwen3.8-Flash-Next support; Atomic Chat runs it as support ships.</li>
+</ul>
+<hr style="margin: 0 0 16px 0;">
+<img src="https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF/resolve/main/hero.png" alt="Qwen3.8-Flash-Next architecture" style="width:560px; max-width:100%; height:auto;"/>
+*Qwen3.8-Flash-Next architecture (Qwen).*
 ## Highlights
 - **262,144-token context**, extensible up to 1,000,000 tokens with RoPE scaling.
 - **Natively multimodal** (causal language model with a vision encoder, image-text-to-text). These GGUF quants cover the text path.
 - **Frontier coding and agentic scores** (Qwen-reported): LiveCodeBench v6 91.9, GPQA Diamond 91.7, SWE-bench Multilingual 81.0, CoWorkBench 73.9.
+- **Full imatrix quantization** with our public [calibration corpora](https://huggingface.co/datasets/AtomicChat/calib-corpora).
 > [!NOTE]
 > These GGUFs are **self-quantized from the original weights**, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.
 1. Download `Qwen/Qwen3.8-Flash-Next` (original weights).
 2. Convert to GGUF with a [llama.cpp](https://github.com/ggerganov/llama.cpp) build that supports the Qwen3.8-Flash-Next architecture (Gated DeltaNet, Qwen Sparse Attention, n-gram embedding).
+3. Build an importance matrix over our public [calibration corpora](https://huggingface.co/datasets/AtomicChat/calib-corpora).
 4. Quantize the ladder with `--imatrix`; `UD-Q4_K_XL` additionally pins the token-embedding and output tensors to `Q8_0`.
 ## License