Instructions to use sjdthree/ai-mod-create-270smol 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 sjdthree/ai-mod-create-270smol 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 sjdthree/ai-mod-create-270smol:Q8_0 # Run inference directly in the terminal: llama cli -hf sjdthree/ai-mod-create-270smol:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sjdthree/ai-mod-create-270smol:Q8_0 # Run inference directly in the terminal: llama cli -hf sjdthree/ai-mod-create-270smol:Q8_0
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 sjdthree/ai-mod-create-270smol:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf sjdthree/ai-mod-create-270smol:Q8_0
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 sjdthree/ai-mod-create-270smol:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf sjdthree/ai-mod-create-270smol:Q8_0
Use Docker
docker model run hf.co/sjdthree/ai-mod-create-270smol:Q8_0
- LM Studio
- Jan
- vLLM
How to use sjdthree/ai-mod-create-270smol with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sjdthree/ai-mod-create-270smol" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sjdthree/ai-mod-create-270smol", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sjdthree/ai-mod-create-270smol:Q8_0
- Ollama
How to use sjdthree/ai-mod-create-270smol with Ollama:
ollama run hf.co/sjdthree/ai-mod-create-270smol:Q8_0
- Unsloth Desktop
- Pi
How to use sjdthree/ai-mod-create-270smol with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sjdthree/ai-mod-create-270smol:Q8_0
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": "sjdthree/ai-mod-create-270smol:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use sjdthree/ai-mod-create-270smol with Docker Model Runner:
docker model run hf.co/sjdthree/ai-mod-create-270smol:Q8_0
- Lemonade
How to use sjdthree/ai-mod-create-270smol with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sjdthree/ai-mod-create-270smol:Q8_0
Run and chat with the model
lemonade run user.ai-mod-create-270smol-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use sjdthree/ai-mod-create-270smol with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sjdthree/ai-mod-create-270smol:Q8_0
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 sjdthree/ai-mod-create-270smol:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use sjdthree/ai-mod-create-270smol with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sjdthree/ai-mod-create-270smol:Q8_0
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 "sjdthree/ai-mod-create-270smol:Q8_0" \ --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"
ai-mod-create-270smol
A 270M-parameter model that does one job: it turns a player's plain-English request — "make a ruby sword called Ruby Blade with 9 attack damage" — into structured operations for the AI-mod creator engine, which then builds and verifies a real mod. It is small enough to run fully offline on an ordinary laptop (~290 MB, CPU or Apple Silicon), so kids can use AI to make mods without an account or an internet service.
It does not write Java or know Minecraft APIs. It only chooses operations and fills in their arguments — create / update / delete / recipe, or ask a clarifying question, or say it's unsupported. The engine validates everything it proposes before anything is built.
Not an official Minecraft product. Not approved by or associated with Mojang or Microsoft.
Files
| File | What |
|---|---|
ai-mod-create-270smol-q8_0.gguf |
the model (Q8_0 GGUF, llama.cpp) |
mce-model.json |
manifest: sha256 pins of the weights, the system prompt, and the grammar |
contract.txt |
the system prompt the model was trained with — must be sent verbatim |
mce-ops.gbnf |
grammar that constrains output to a well-formed JSON array of operations |
NOTICE, GEMMA_TERMS_OF_USE.txt |
license notices (Gemma Terms of Use) |
Use
The easiest way is the AI-mod creator desktop app, which bundles and starts this model for you. To serve it yourself with llama.cpp:
llama-server -m ai-mod-create-270smol-q8_0.gguf --port 8770 --temp 0 \
--grammar-file mce-ops.gbnf \
--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 3
Then call the OpenAI-compatible /v1/chat/completions with:
- system: the exact contents of
contract.txt - user:
Request: <the player's request>— prefixed, when the project already has creations, byCurrent project already contains: <Name>, <Name>.on its own line temperature: 0,stop: ["<end_of_turn>"]
The output is a JSON array such as
[{"operation":"create_concept","payload":{"concept_type":"weapon","subtype":"sword","display_name":"Ruby Blade","properties":{"attack_damage":9.0}}}].
Serving notes: the system prompt must match contract.txt byte-for-byte (its sha256 is pinned in
mce-model.json); without the grammar and the DRY sampler, greedy decoding can loop on long
decimals. The AI-mod creator engine checks the manifest with mce model verify.
Evaluation
Held-out set of 42 requests (never seen in training) covering every operation type, casual and misspelled phrasing, and questions that should change nothing. Scored on this GGUF, served exactly as above. A case counts as correct only if the operation matches the intended one and the engine's validator accepts it.
| Category | Correct |
|---|---|
| create | 14 / 15 |
| update | 4 / 4 |
| delete | 3 / 3 |
| recipe | 2 / 3 |
| unsupported feature (e.g. new dimensions, GUIs) | 4 / 4 |
| ambiguous → ask a clarifying question | 4 / 7 |
| question only → change nothing | 5 / 6 |
| Total | 36 / 42 (85.7%) |
Safety: 0 wrong changes were accepted by the engine — no request led to creating, editing, or deleting the wrong thing. Every miss either asked for clarification / declined when it should have done the other, or proposed something the engine's validator rejected (the app then asks the player to rephrase).
Limitations
- English only; a fixed set of creations (items, weapons, tools, food, blocks, recipes). Anything
else should come back as
unsupported_request. - Very vague requests ("make it better") are the weakest area: it sometimes declines instead of asking which creation to change, and once included a creation name inside a clarifying question.
- Its output is a proposal. It is designed to run behind the engine's validator, never to act on its own.
Training
Fine-tuned from google/functiongemma-270m-it on a private, engine-verified synthetic dataset, then
quantized to Q8_0. The training data and method are not published.
License
Released under the Gemma Terms of Use — see NOTICE and GEMMA_TERMS_OF_USE.txt. Use is
subject to the Gemma Prohibited Use Policy.
The AI-mod creator engine is separately licensed under Apache-2.0.
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Model tree for sjdthree/ai-mod-create-270smol
Base model
google/functiongemma-270m-it