Instructions to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-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 aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-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 aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-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 aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS

Use Docker

docker model run hf.co/aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-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": "aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS

SGLang

How to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF with Ollama:
```
ollama run hf.co/aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS
```
Unsloth Desktop

How to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-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": "aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS
```

Lemonade

How to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.spoomplesmaxx-jackdaw-35B-A3-i1-GGUF-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-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 aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-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 aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-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 "aimeri/spoomplesmaxx-jackdaw-35B-A3-i1-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"

spoomplesmaxx-jackdaw-35B-A3-i1-GGUF

Weighted / imatrix quants of aimeri/spoomplesmaxx-jackdaw-35B-A3.

Static quants are not provided: at every size that matters the imatrix quant of the same name is at least as good, so this repo is the whole set.

Provenance


llama.cpp	`b9222` (pinned; Qwen3.5 MoE conversion needs >= b9180)
Source	bf16 GGUF converted from the SFT checkpoint with `convert_hf_to_gguf.py`
imatrix	`llama-imatrix --parse-special`, 192 MB
Calibration	48 MB, 12.0M in-domain tokens (952 documents) rendered through this model's own chat template and truncated to exact 8192-token windows so imatrix chunks land on document boundaries; the generic term is unsloth's published imatrix for this architecture, merged after our own pass

The in-domain half exists because this is a sparse MoE: a token only touches its routed experts, so a small generic corpus leaves rare experts with no importance data at all. Coverage was gated before quantizing — every quantizable tensor has importance data and every expert row was activated above a token floor.

The Q4_K_M quant was verified after quantization: greedy generations terminate on <|im_end|> (not the token cap), think blocks close, no stray glyphs.

Measured

KL divergence of each quant against this model's own bf16, on a holdout that was excluded from calibration (evaluating on your own calibration domain flatters the numbers). Lower KLD is closer to the unquantized model.

quant	size	KLD mean	KLD median	KLD p99	ΔPPL	same top-1
`i1-IQ4_XS`	18.7 GB	0.0223	0.0123	0.129	+0.83%	100.0%
`i1-Q4_K_M`	21.4 GB	0.0195	0.0102	0.121	+0.92%	100.0%

same top-1 is the share of holdout positions where the quant's most likely token is unchanged from bf16.

Which one do I want?

Roughly, higher in the table is smaller and worse. i1-Q4_K_M is the recommended default; i1-IQ4_XS if you need to save a little; i1-Q6_K if you have the memory and want to stop thinking about it.

Link	Type	Size (GB)	Notes
GGUF	i1-IQ4_XS	18.7
GGUF	i1-Q4_K_M	21.4	fast, recommended

Usage

llama-server -m spoomplesmaxx-jackdaw-35B-A3-i1-GGUF.i1-Q4_K_M.gguf -ngl 99 -c 32768 --jinja

--jinja matters: the chat template is embedded in the GGUF and pre-opens the <think> block, which is the behaviour the model was trained for. Tool calls use Qwen3.5's XML convention (<tool_call><function=NAME><parameter=KEY>VALUE</parameter></function></tool_call>).