Text Generation
Transformers
GGUF
English
geometry-dash
llama-cpp
ollama
tool-use
geode
qwen2.5
conversational
Instructions to use EditorAI-Geode/editorai-1p5b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EditorAI-Geode/editorai-1p5b-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EditorAI-Geode/editorai-1p5b-v2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("EditorAI-Geode/editorai-1p5b-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use EditorAI-Geode/editorai-1p5b-v2 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 EditorAI-Geode/editorai-1p5b-v2:Q4_K_M # Run inference directly in the terminal: llama cli -hf EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf EditorAI-Geode/editorai-1p5b-v2:Q4_K_M # Run inference directly in the terminal: llama cli -hf EditorAI-Geode/editorai-1p5b-v2: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 EditorAI-Geode/editorai-1p5b-v2:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf EditorAI-Geode/editorai-1p5b-v2: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 EditorAI-Geode/editorai-1p5b-v2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
Use Docker
docker model run hf.co/EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use EditorAI-Geode/editorai-1p5b-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EditorAI-Geode/editorai-1p5b-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EditorAI-Geode/editorai-1p5b-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
- SGLang
How to use EditorAI-Geode/editorai-1p5b-v2 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 "EditorAI-Geode/editorai-1p5b-v2" \ --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": "EditorAI-Geode/editorai-1p5b-v2", "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 "EditorAI-Geode/editorai-1p5b-v2" \ --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": "EditorAI-Geode/editorai-1p5b-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use EditorAI-Geode/editorai-1p5b-v2 with Ollama:
ollama run hf.co/EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
- Unsloth Desktop
- Pi
How to use EditorAI-Geode/editorai-1p5b-v2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
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": "EditorAI-Geode/editorai-1p5b-v2:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use EditorAI-Geode/editorai-1p5b-v2 with Docker Model Runner:
docker model run hf.co/EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
- Lemonade
How to use EditorAI-Geode/editorai-1p5b-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
Run and chat with the model
lemonade run user.editorai-1p5b-v2-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use EditorAI-Geode/editorai-1p5b-v2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
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 EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use EditorAI-Geode/editorai-1p5b-v2 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
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 "EditorAI-Geode/editorai-1p5b-v2:Q4_K_M" \ --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"
Ollama Modelfile with tool template
Browse files- Modelfile.v2 +67 -0
Modelfile.v2
ADDED
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FROM /home/entity12208/EditorAI-Model-Local/output/editorai-v2-Q4_K_M.gguf
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TEMPLATE """{{- if or .System .Tools }}<|im_start|>system
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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# Tools
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You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>
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{{- range .Tools }}
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{"type": "function", "function": {{ .Function }}}
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{{- end }}
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</tools>
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For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
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<tool_call>
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{"name": <function-name>, "arguments": <args-json-object>}
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</tool_call>
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{{- end }}<|im_end|>
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{{ end }}
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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{{- if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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{{ else if eq .Role "assistant" }}<|im_start|>assistant
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{{ if .Content }}{{ .Content }}
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{{- else if .ToolCalls }}<tool_call>
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{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
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{{ end }}</tool_call>
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{{- end }}{{ if not $last }}<|im_end|>
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{{ end }}
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{{- else if eq .Role "tool" }}<|im_start|>user
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<tool_response>
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{{ .Content }}
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</tool_response><|im_end|>
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{{ end }}
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{ end }}
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{{- end }}"""
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SYSTEM """You are EditorAI's level-design model for Geometry Dash.
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You have tools available. USE THEM PROACTIVELY AND FREQUENTLY:
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- Call get_level_length whenever the user requests a target length, after each
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batch of objects you add, and before deciding you are done. Keep adding
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objects until the level reaches the target length.
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- Call analyze_level before making targeted edits so you know what already exists.
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- Call think when you need to reason through a complex layout — emit a brief
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plan, then build to it.
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- Call web_search, search_newgrounds, get_newgrounds_song, and download_level
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when the user references real-world facts, a specific song, or a named level.
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When you are NOT calling a tool, return EditorAI-format JSON exactly as
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requested. Do not narrate. The mod parses only the LAST EditorAI-format JSON in
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your final message — earlier tool calls and tool responses are ignored by the
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parser, so it is safe and expected to interleave them with planning."""
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PARAMETER stop <|im_end|>
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PARAMETER stop <|endoftext|>
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PARAMETER temperature 0.5
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PARAMETER top_p 0.9
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PARAMETER num_ctx 8192
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