Instructions to use aimeri/spoomplesmaxx-mockingbird-36B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use aimeri/spoomplesmaxx-mockingbird-36B-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-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf aimeri/spoomplesmaxx-mockingbird-36B-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-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf aimeri/spoomplesmaxx-mockingbird-36B-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-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf aimeri/spoomplesmaxx-mockingbird-36B-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-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf aimeri/spoomplesmaxx-mockingbird-36B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/aimeri/spoomplesmaxx-mockingbird-36B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use aimeri/spoomplesmaxx-mockingbird-36B-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-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-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/aimeri/spoomplesmaxx-mockingbird-36B-GGUF:Q4_K_M
- Ollama
How to use aimeri/spoomplesmaxx-mockingbird-36B-GGUF with Ollama:
ollama run hf.co/aimeri/spoomplesmaxx-mockingbird-36B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use aimeri/spoomplesmaxx-mockingbird-36B-GGUF with Docker Model Runner:
docker model run hf.co/aimeri/spoomplesmaxx-mockingbird-36B-GGUF:Q4_K_M
- Lemonade
How to use aimeri/spoomplesmaxx-mockingbird-36B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aimeri/spoomplesmaxx-mockingbird-36B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.spoomplesmaxx-mockingbird-36B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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base_model: aimeri/spoomplesmaxx-mockingbird-36B
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library_name: gguf
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pipeline_tag: text-generation
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tags:
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- roleplay
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- creative-writing
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language:
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- en
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---
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# spoomplesmaxx-mockingbird-36B — GGUF (static)
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Static GGUF quants of
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[spoomplesmaxx-mockingbird-36B](https://huggingface.co/aimeri/spoomplesmaxx-mockingbird-36B),
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the first of the mimids. Weighted/imatrix quants (calibrated on the model's
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own training corpus) live in
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[-i1-GGUF](https://huggingface.co/aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF);
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prefer those at 3–4 bit if your runtime supports them.
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| Quant | Size | Notes |
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|---|---|---|
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| Q3_K_M | ~18 GB | the 18GB target; fine with a good card |
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| Q4_K_S | ~21 GB | |
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| Q4_K_M | ~22 GB | recommended balance |
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| Q5_K_M | ~26 GB | closest to bf16 behavior |
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The seed-native chat template is embedded in the GGUF metadata — llama.cpp,
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koboldcpp, and LM Studio pick it up automatically.
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## Sampling — read this part
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```
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temperature 1.0 · top_p 0.9 · repeat_penalty 1.0 (OFF)
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```
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> **⚠ Never use repetition, presence, or frequency penalties.**
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> The template ends every message with `<seed:eos>`; context-wide penalties
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> suppress that token, the model stops ending its turns, and generation
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> degenerates into the base model's untrained Chinese vocabulary. Many
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> frontend presets default repeat_penalty to 1.05–1.1 — set it back to 1.0.
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> Use DRY or XTC if you want extra anti-repetition; both leave special
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> tokens alone.
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Usable temperature window is ~0.95–1.05: lower loops verbatim, higher frays.
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Full details, corpus notes, and training story on the
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[main model card](https://huggingface.co/aimeri/spoomplesmaxx-mockingbird-36B).
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*mimids 01 · Apache 2.0*
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