Instructions to use empero-ai/Qwen3.8-35B-A3B-Distill-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/Qwen3.8-35B-A3B-Distill-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/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

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/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

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/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Use Docker

docker model run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "empero-ai/Qwen3.8-35B-A3B-Distill-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/Qwen3.8-35B-A3B-Distill-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Ollama
How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Ollama:
```
ollama run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
```
Unsloth Desktop

How to use empero-ai/Qwen3.8-35B-A3B-Distill-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/Qwen3.8-35B-A3B-Distill-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": "empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Docker Model Runner:
```
docker model run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
```

Lemonade

How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-35B-A3B-Distill-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use empero-ai/Qwen3.8-35B-A3B-Distill-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/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use empero-ai/Qwen3.8-35B-A3B-Distill-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/Qwen3.8-35B-A3B-Distill-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 "empero-ai/Qwen3.8-35B-A3B-Distill-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"

Qwen3.8-35B-A3B — GGUF

Developed by Empero

GGUF quantizations of empero-ai/Qwen3.8-35B-A3B-Distill — a distillation of the Qwen3.8 frontier models into the Qwen3.6-35B-A3B Mixture-of-Experts architecture — for llama.cpp, Ollama, LM Studio, Jan, KoboldCpp, and other stock GGUF runtimes.

This card is about choosing a file and running it. The capability writeup, benchmark results, and best practices live on the main model card.

35B total parameters with ~3B active per token — the MoE sparsity means it runs considerably faster than a dense 35B at the same quant, but the whole weight file still has to fit in RAM or VRAM.

Qwen3.6-class models are hybrids: 30 Gated DeltaNet layers and 10 full-attention layers, with 256 experts routed 8-per-token. A recent llama.cpp build with Qwen3.6 / Gated DeltaNet MoE support is required — older builds will fail to load the architecture.

Files

File	Quant	Size	Notes
`Qwen3.8-35B-A3B-IQ2_M.gguf`	IQ2_M	12.558 GB	Smallest usable. Fits a 16 GB card.
`Qwen3.8-35B-A3B-Q2_K.gguf`	Q2_K	13.839 GB	2-bit K-quant; widest runtime support at this size.
`Qwen3.8-35B-A3B-IQ3_M.gguf`	IQ3_M	16.340 GB	Strong quality per byte at 3-bit.
`Qwen3.8-35B-A3B-Q3_K_M.gguf`	Q3_K_M	17.664 GB	Conventional 3-bit K-quant.
`Qwen3.8-35B-A3B-IQ4_XS.gguf`	IQ4_XS	19.628 GB	Near Q4_K_M quality, ~2 GB smaller.
`Qwen3.8-35B-A3B-Q4_K_M.gguf`	Q4_K_M	21.713 GB	Recommended. Best quality/size balance for most users.
`Qwen3.8-35B-A3B-Q5_K_M.gguf`	Q5_K_M	25.348 GB	Higher quality, modest size increase.
`Qwen3.8-35B-A3B-Q6_K.gguf`	Q6_K	29.209 GB	Near-lossless.
`Qwen3.8-35B-A3B-Q8_0.gguf`	Q8_0	37.802 GB	Highest-quality quantization.
`Qwen3.8-35B-A3B-BF16.gguf`	BF16	71.067 GB	Full precision reference.
`mmproj-Qwen3.8-35B-A3B-F16.gguf`	F16	0.899 GB	Vision projector. Pair with any text quant above for image input.

The IQ* quants and the 2/3-bit K-quants are calibrated with an importance matrix, which is what keeps them coherent at these bit-widths.

Sizes are exact decimal GB from the uploaded files (1 GB = 1,000,000,000 bytes).

What fits?

Weight-size guidance at modest context. The KV cache is the dominant cost at long context and may require offload regardless of weight quant:

Quant	Guidance
IQ2_M / Q2_K	16 GB VRAM, or 16 GB system RAM. The smallest that stay coherent.
IQ3_M / Q3_K_M	20-24 GB VRAM, or 24 GB system RAM.
IQ4_XS / Q4_K_M	24 GB VRAM for a full GPU load; comfortable on CPU with 32 GB RAM.
Q5_K_M / Q6_K	32 GB VRAM, or 48 GB system RAM.
Q8_0	48 GB VRAM, or 64 GB system RAM.
BF16	80 GB+ VRAM, or 96 GB system RAM. Reference only.

Because only ~3B parameters are active per token, CPU and partial-offload inference are far more practical here than for a dense model of the same file size.

Usage

llama.cpp

llama-cli -m Qwen3.8-35B-A3B-Q4_K_M.gguf \
  --temp 0.6 --top-p 0.95 --top-k 20 \
  -n 16384 -cnv

Use the built-in chat template (-cnv). The model is a reasoning model: every answer opens with a <think> block, so allow a generous -n and strip the <think>...</think> span for end users.

Vision

Pair the projector with any text quant:

llama-mtmd-cli -m Qwen3.8-35B-A3B-Q4_K_M.gguf \
  --mmproj mmproj-Qwen3.8-35B-A3B-F16.gguf \
  --image photo.jpg -p "Describe this image."

The vision tower is inherited unchanged from the Qwen3.6-35B-A3B base — the distillation was text-only and vision behaviour was not evaluated.

Ollama / LM Studio / Jan / KoboldCpp

Download the GGUF of your choice and load it directly; the chat template is embedded in the file. Recommended sampling: temperature=0.6, top_p=0.95, top_k=20.

Verifying downloads

SHA256SUMS in this repo lists checksums for every GGUF:

sha256sum -c SHA256SUMS --ignore-missing

Provenance & licensing

Quantizations of empero-ai/Qwen3.8-35B-A3B-Distill, a distillation of the Qwen3.8 frontier models into Qwen/Qwen3.6-35B-A3B. Weights are Apache-2.0, inherited from the Qwen base, shared as-is.

Stay in the loop

Support / Donate

If this model helped you, consider supporting the project:

BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x

Acknowledgements

Developed and released by Empero
Base model: Qwen3.6-35B-A3B (Alibaba Qwen team)
GGUF quantization: llama.cpp (ggml-org)

Downloads last month: 95,061

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for empero-ai/Qwen3.8-35B-A3B-Distill-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

empero-ai/Qwen3.8-35B-A3B-Distill

Quantized

(8)

this model