Instructions to use darthceltic85/determinex-engineer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use darthceltic85/determinex-engineer with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="darthceltic85/determinex-engineer", filename="determinex-engineer-v11-dsl.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use darthceltic85/determinex-engineer 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 darthceltic85/determinex-engineer # Run inference directly in the terminal: llama cli -hf darthceltic85/determinex-engineer
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf darthceltic85/determinex-engineer # Run inference directly in the terminal: llama cli -hf darthceltic85/determinex-engineer
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 darthceltic85/determinex-engineer # Run inference directly in the terminal: ./llama-cli -hf darthceltic85/determinex-engineer
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 darthceltic85/determinex-engineer # Run inference directly in the terminal: ./build/bin/llama-cli -hf darthceltic85/determinex-engineer
Use Docker
docker model run hf.co/darthceltic85/determinex-engineer
- LM Studio
- Jan
- Ollama
How to use darthceltic85/determinex-engineer with Ollama:
ollama run hf.co/darthceltic85/determinex-engineer
- Unsloth Studio
How to use darthceltic85/determinex-engineer with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for darthceltic85/determinex-engineer to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for darthceltic85/determinex-engineer to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for darthceltic85/determinex-engineer to start chatting
- Pi
How to use darthceltic85/determinex-engineer with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf darthceltic85/determinex-engineer
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "darthceltic85/determinex-engineer" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use darthceltic85/determinex-engineer with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf darthceltic85/determinex-engineer
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 darthceltic85/determinex-engineer
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use darthceltic85/determinex-engineer with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf darthceltic85/determinex-engineer
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 "darthceltic85/determinex-engineer" \ --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"
- Docker Model Runner
How to use darthceltic85/determinex-engineer with Docker Model Runner:
docker model run hf.co/darthceltic85/determinex-engineer
- Lemonade
How to use darthceltic85/determinex-engineer with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull darthceltic85/determinex-engineer
Run and chat with the model
lemonade run user.determinex-engineer-{{QUANT_TAG}}List all available models
lemonade list
determinex-engineer
C1 Engineer โ Builder in the Determinex compiler-verified multi-agent coding system.
- Base model: Qwen2.5-Coder-1.5B-Instruct (1.5B)
- Fine-tune: LoRA on the Determinex Semantic DSL corpus, compiler-validated
- Ollama tag:
determinex-engineer-v11-dsl - Size: 1.53 GB (GGUF, quantized)
Determinex never trusts a model's output directly โ every generation is verified against a real compiler/test oracle before it's accepted. This model is one of three specialists (Engineer / Observer / Sentinel) that coordinate through a shared latent space (the Rosetta Stone).
Usage
ollama create determinex-engineer-v11-dsl -f Modelfile
ollama run determinex-engineer-v11-dsl
License
This model is a fine-tune of a base model licensed under Apache 2.0. Weights
here are released under the same Apache 2.0 terms. See LICENSE in this repo.
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We're not able to determine the quantization variants.