Instructions to use aimeri/spoomplesmaxx-mockingbird-36B-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-mockingbird-36B-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-mockingbird-36B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aimeri/spoomplesmaxx-mockingbird-36B-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-mockingbird-36B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aimeri/spoomplesmaxx-mockingbird-36B-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-mockingbird-36B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aimeri/spoomplesmaxx-mockingbird-36B-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-mockingbird-36B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use aimeri/spoomplesmaxx-mockingbird-36B-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-mockingbird-36B-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-mockingbird-36B-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF:Q4_K_M

Ollama
How to use aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF with Ollama:
```
ollama run hf.co/aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF:Q4_K_M
```

Lemonade

How to use aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.spoomplesmaxx-mockingbird-36B-i1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

spoomplesmaxx-mockingbird-36B — i1-GGUF (weighted/imatrix)

Weighted/imatrix GGUF quants of spoomplesmaxx-mockingbird-36B. The importance matrix was computed on a stratified sample of the model's own training corpus — all ten lanes, rendered in the exact chat template the model serves with — not a generic calibration set. At 3–4 bit these should beat the static quants noticeably; at Q5 the difference fades.

Quant	Size	Notes
i1-IQ3_XXS	~14 GB	smallest usable; VRAM-desperate only
i1-Q3_K_M	~18 GB	the 18GB target, imatrix-weighted
i1-IQ4_XS	~19 GB	best size/quality trade below Q4_K_M
i1-Q4_K_M	~22 GB	recommended
i1-Q5_K_M	~26 GB	closest to bf16 behavior

The seed-native chat template is embedded in the GGUF metadata.

Sampling — read this part

temperature 1.0 · top_p 0.9 · repeat_penalty 1.0 (OFF)

⚠ Never use repetition, presence, or frequency penalties. The template ends every message with <seed:eos>; context-wide penalties suppress that token, the model stops ending its turns, and generation degenerates into the base model's untrained Chinese vocabulary. Many frontend presets default repeat_penalty to 1.05–1.1 — set it back to 1.0. Use DRY or XTC if you want extra anti-repetition; both leave special tokens alone.

Usable temperature window is ~0.95–1.05: lower loops verbatim, higher frays. Full details on the main model card.

mimids 01 · Apache 2.0

Downloads last month: 296

GGUF

Model size

36B params

Architecture

seed_oss

Hardware compatibility

3-bit

4-bit

5-bit

Model tree for aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF

Base model

ByteDance-Seed/Seed-OSS-36B-Base-woSyn

Finetuned

aimeri/spoomplesmaxx-mockingbird-36B

Quantized

(5)

this model

Collection including aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF

Mimids

Collection

A series of models designed specifically for Roleplaying • 11 items • Updated 1 day ago