Instructions to use empero-ai/Qwen3.8-27B-Ridge-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 empero-ai/Qwen3.8-27B-Ridge-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 empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

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 empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

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 empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

Use Docker

docker model run hf.co/empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

LM Studio
Jan

vLLM

How to use empero-ai/Qwen3.8-27B-Ridge-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "empero-ai/Qwen3.8-27B-Ridge-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": "empero-ai/Qwen3.8-27B-Ridge-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

Ollama
How to use empero-ai/Qwen3.8-27B-Ridge-GGUF with Ollama:
```
ollama run hf.co/empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16
```

Unsloth Studio

How to use empero-ai/Qwen3.8-27B-Ridge-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for empero-ai/Qwen3.8-27B-Ridge-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for empero-ai/Qwen3.8-27B-Ridge-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for empero-ai/Qwen3.8-27B-Ridge-GGUF to start chatting

How to use empero-ai/Qwen3.8-27B-Ridge-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use empero-ai/Qwen3.8-27B-Ridge-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

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 "empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16" \
  --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"

Docker Model Runner
How to use empero-ai/Qwen3.8-27B-Ridge-GGUF with Docker Model Runner:
```
docker model run hf.co/empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16
```

Lemonade

How to use empero-ai/Qwen3.8-27B-Ridge-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

Run and chat with the model

lemonade run user.Qwen3.8-27B-Ridge-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use empero-ai/Qwen3.8-27B-Ridge-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 empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

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 empero-ai/Qwen3.8-27B-Ridge-GGUF:BF16

Run Hermes

hermes

Atomic Chat

Qwen3.8-27B-Ridge-3.7bpw

Developed by Empero

A Gated-DeltaNet-aware mixed GGUF of official Qwen/Qwen3.8-27B (1d4bf0f2) for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other stock GGUF runtimes.

This is a quantization of the Qwen3.8-27B checkpoint. Ridge is a probed mix of types written for this architecture: 64 layers = 16 × (3 × GatedDeltaNet → FFN + 1 × GatedAttn → FFN). Generic IQ2_XS and UD-IQ2 do not treat GDN state (ssm_alpha / ssm_beta) or the GDN mixers as first-class. We fixed that.

Nothing was stripped to make the file fit. The native MTP draft head (blk.64 / nextn) stays in the GGUF. Vision is a separate BF16 mmproj.

This card is about choosing the file and running it. The official capability writeup lives on the base model card.

Files

The repository is Qwen3.8-27B-Ridge-GGUF. Use the exact filenames below when downloading or passing -m.

File	Quant	Size	Notes
`Qwen3.8-27B-Ridge-3.7bpw.gguf`	Ridge mix, 3.69 bpw	11.73 GiB / 12.59 GB	this release — text + native MTP
`mmproj-Qwen3.8-27B-BF16.gguf`	BF16	0.87 GiB / 0.93 GB	vision encoder + projector; required for images

If you only want text, download the Ridge GGUF. Add the mmproj for image input.

What fits on a GPU?

These are practical weight-size-based estimates, not a VRAM benchmark. They assume a modest context and leave room for runtime and the KV cache. Image input adds the 0.87 GiB mmproj. The native 262k window and the 1M YaRN extension — make KV the dominant cost and may need offload regardless of weight quant.

Measured: Qwen3.8-27B-Ridge-3.7bpw.gguf fully offloaded to a single RTX PRO 6000 Blackwell (96 GB) runs at ~54 tok/s generation, ~130 tok/s prompt (llama.cpp CUDA, -ngl 99, short smoke). One data point on one card, not a sweep — but a 27B at 11.7 GiB is comfortably interactive on a 16–24 GB card at modest context.

File	Approximate hardware guidance at modest context
Ridge-3.7bpw	The practical 16 GB starting point; 24 GB is comfortable once you add KV and (optionally) the mmproj.
+ mmproj	Add ~1 GiB. Still a 24 GB card for everyday use.

Recipe

Qwen3.8 is a hybrid: three Gated-DeltaNet layers for every full-attention layer. GDN state is disproportionately sensitive to low-bit quantization, so Ridge holds that path high and spends the saved bits by dropping mid-stack FFN.

The Gated-DeltaNet state path is Q8_0. Mixers are Q4_K, not IQ2. That is the difference between this file and a flat 2-bit dump of the same model.

Built with llama.cpp adb55e5, CUDA, importance matrix on 80 × 512-token chunks (--process-output, wikitext + code). MTP tensors are unused during calibration and have no imatrix — IQ2/IQ3 on blk.64 will abort, so the draft head stays Q6_K.

Measured

Same box, same calibration file, llama-perplexity, 80 chunks, -c 512 -b 512. BF16 is our convert of the same official checkpoint.

Candidate	Size	BPW	Wiki-style PPL	vs BF16
BF16 GGUF (this convert)	50.89 GiB	16.00	7.15 ± 0.12	—
Ridge-3.7bpw	11.73 GiB	3.69	7.82 ± 0.14	+9.3 %

Comparison

Published Hugging Face file sizes as of 2026-08-15. PPL is filled only where we measured the file ourselves.

File	Publisher	Size	Nominal band	PPL vs this BF16
BF16	this convert	50.89 GiB	16 bpw	7.15
`UD-IQ2_XXS`	unsloth	8.39 GiB	~2.1 bpw	not measured here (Unsloth quotes 82.5 % top-1 vs BF16)
`UD-IQ2_M`	unsloth	9.61 GiB	~2.4 bpw	not measured
`IQ2_XXS`	bartowski	8.75 GiB	~2.2 bpw	not measured
`Q3_K_S`	unsloth	11.71 GiB	~3.1 bpw	not measured
Ridge-3.7bpw	empero-ai	11.73 GiB	3.69 bpw	7.82 (+9 %)
`IQ3_XXS`	bartowski	11.76 GiB	~2.9 bpw	not measured
`UD-Q3_K_XL`	unsloth	12.52 GiB	~3.4 bpw	not measured

Quick start

llama.cpp (`llama-cli`)

Sampling from the official Qwen3.8 card. Thinking is on by default.

# thinking
llama-cli \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  -ngl 99 -n 16384 \
  --temp 1.0 --top-p 0.95 --top-k 20 \
  -p "Explain the design tradeoffs in a Gated-DeltaNet hybrid model."

# instruct (thinking off)
llama-cli \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  -ngl 99 --reasoning off \
  --temp 0.7 --top-p 0.80 --top-k 20 --presence-penalty 1.5 \
  -p "Say hello in one short sentence."

llama.cpp (`llama-server`)

llama-server \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  -c 16384 --port 8080

Ollama

ollama run hf.co/empero-ai/Qwen3.8-27B-Ridge-GGUF

Or a local Modelfile:

FROM ./Qwen3.8-27B-Ridge-3.7bpw.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 20

ollama create qwen38-ridge -f Modelfile
ollama run qwen38-ridge

LM Studio / jan / KoboldCpp

Download Qwen3.8-27B-Ridge-3.7bpw.gguf and load it. Preserve the embedded Qwen3.8 chat template if the runtime asks you to select one.

llama.cpp with MTP draft speculation

The Ridge GGUF keeps the native MTP head. Use a recent llama.cpp build that supports --spec-type draft-mtp:

llama-server \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  --spec-type draft-mtp \
  --spec-draft-n-max 6 \
  -c 16384 --port 8080

If your runtime does not support MTP, the file still runs as a normal 27B — you just will not get the draft speedup.

Vision (image input)

Download the text GGUF and mmproj-Qwen3.8-27B-BF16.gguf.

llama.cpp (`llama-mtmd-cli`)

llama-mtmd-cli \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  --mmproj mmproj-Qwen3.8-27B-BF16.gguf \
  --image ./photo.jpg \
  -p "Describe this image in detail." \
  --temp 0.7 --top-p 0.80 --top-k 20 \
  -c 16384

llama.cpp server

llama-server \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  --mmproj mmproj-Qwen3.8-27B-BF16.gguf \
  -c 16384 --port 8080

Sampling

Qwen3.8 is a hybrid thinking model. Responses open with a <think>…</think> block unless thinking is disabled.

Mode	temperature	top_p	top_k	presence_penalty
Thinking (default)	1.0	0.95	20	0.0
Instruct (thinking off)	0.7	0.80	20	1.5

Use the runtime chat/completions path rather than hand-rolling a different prompt format. The embedded template is Qwen3.8's, including tool-use (<tool_call>…</tool_call>).

Long context

Native context is 262,144 tokens, extensible to 1,000,000 with YaRN. Set -c to what you actually need — the KV cache, not the 11.7 GiB weights, is what blows up a 16–24 GB card at long context.

Limitations

Not lossless. +9 % wiki-style PPL vs our BF16 convert
Context costs memory. Weight size is only part of the hardware budget.
MTP is runtime-dependent. The head is in the file; the speedup needs a runtime that knows draft-mtp.

Stay in the loop

Support / Donate

If this model helped you, consider supporting the project:

BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x
XMR: 42Dbm5xg5Nq26fdyzfEU7KBnAJfhi7Cvz5J2ex5CzHXkfKuNEJzYCcmJ1GTbgjFZ5MBx72sdG1G9239Cd6rsZfv4QeDkYJY

Provenance & licensing

Quantization of Qwen/Qwen3.8-27B @ 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0. Weights are Apache-2.0, inherited from the Qwen base, shared as-is.

Acknowledgements

Developed and released by Empero
Base model: Qwen3.8-27B (Alibaba Qwen team)
GGUF quantization: llama.cpp (ggml-org)

Downloads last month: -

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for empero-ai/Qwen3.8-27B-Ridge-GGUF

Base model

Qwen/Qwen3.8-27B

Quantized

(397)

this model

Qwen3.8-27B-Ridge-3.7bpw

Files

What fits on a GPU?

Recipe

Measured

Comparison

Quick start

llama.cpp (llama-cli)

llama.cpp (llama-server)

Ollama

LM Studio / jan / KoboldCpp

llama.cpp with MTP draft speculation

Vision (image input)

llama.cpp (llama-mtmd-cli)

llama.cpp server

Sampling

Long context

Limitations

Stay in the loop

Support / Donate

Provenance & licensing

Acknowledgements

Model tree for empero-ai/Qwen3.8-27B-Ridge-GGUF

llama.cpp (`llama-cli`)

llama.cpp (`llama-server`)

llama.cpp (`llama-mtmd-cli`)