Instructions to use aimeri/spoomplesmaxx-thrasher-24B-i1-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 aimeri/spoomplesmaxx-thrasher-24B-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-thrasher-24B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aimeri/spoomplesmaxx-thrasher-24B-i1-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 aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aimeri/spoomplesmaxx-thrasher-24B-i1-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 aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aimeri/spoomplesmaxx-thrasher-24B-i1-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 aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use aimeri/spoomplesmaxx-thrasher-24B-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-thrasher-24B-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-thrasher-24B-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF:Q4_K_M

Ollama
How to use aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF with Ollama:
```
ollama run hf.co/aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF:Q4_K_M
```

Lemonade

How to use aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.spoomplesmaxx-thrasher-24B-i1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

spoomplesmaxx-thrasher-24B — i1 GGUF (imatrix)

Weighted/imatrix GGUF quants of spoomplesmaxx-thrasher-24B ("Thrash Metal", mimids 02). Calibrated on ~2M chars sampled from the model's own training corpus (the imatrix.dat is included if you want to cut your own sizes). ChatML template embedded — llama.cpp picks it up without ceremony.

Prefer these over the static quants at 3–4 bit; at 5 bit and above the statics are fine.

spoomplesmaxx-thrasher-24B.i1-IQ3_XXS.gguf   ~8.7 GB   smallest usable
spoomplesmaxx-thrasher-24B.i1-Q3_K_M.gguf    ~11 GB    12GB cards
spoomplesmaxx-thrasher-24B.i1-IQ4_XS.gguf    ~12 GB    quality-per-GB pick
spoomplesmaxx-thrasher-24B.i1-Q4_K_M.gguf    ~14 GB    the 24GB-card sweet spot

Sampler (swept on the full-precision model): temperature 1.0 · min_p 0.05. A mild repetition_penalty 1.05 eliminated the verbatim-loop tail in our sweep at the cost of a rare unfinished turn — a reasonable opt-in here. See the main card for the full story: the token surgery that put ChatML on a Mistral base, the checkpoint-selection battery, and the measured sampler guidance.

For adults. Stays in character by design; bring your own moderation. Apache 2.0.