Text Generation
GGUF
English
llama-cpp
davinci-resolve
video-editing
local-inference
qwen
conversational
Instructions to use SuNavar/Pygenesis_ResolveExpert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use SuNavar/Pygenesis_ResolveExpert with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="SuNavar/Pygenesis_ResolveExpert", filename="pygenesis-resolve-q4km.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use SuNavar/Pygenesis_ResolveExpert 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 SuNavar/Pygenesis_ResolveExpert # Run inference directly in the terminal: llama cli -hf SuNavar/Pygenesis_ResolveExpert
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf SuNavar/Pygenesis_ResolveExpert # Run inference directly in the terminal: llama cli -hf SuNavar/Pygenesis_ResolveExpert
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 SuNavar/Pygenesis_ResolveExpert # Run inference directly in the terminal: ./llama-cli -hf SuNavar/Pygenesis_ResolveExpert
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 SuNavar/Pygenesis_ResolveExpert # Run inference directly in the terminal: ./build/bin/llama-cli -hf SuNavar/Pygenesis_ResolveExpert
Use Docker
docker model run hf.co/SuNavar/Pygenesis_ResolveExpert
- LM Studio
- Jan
- vLLM
How to use SuNavar/Pygenesis_ResolveExpert with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SuNavar/Pygenesis_ResolveExpert" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SuNavar/Pygenesis_ResolveExpert", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SuNavar/Pygenesis_ResolveExpert
- Ollama
How to use SuNavar/Pygenesis_ResolveExpert with Ollama:
ollama run hf.co/SuNavar/Pygenesis_ResolveExpert
- Unsloth Studio
How to use SuNavar/Pygenesis_ResolveExpert 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 SuNavar/Pygenesis_ResolveExpert 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 SuNavar/Pygenesis_ResolveExpert to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SuNavar/Pygenesis_ResolveExpert to start chatting
- Pi
How to use SuNavar/Pygenesis_ResolveExpert with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SuNavar/Pygenesis_ResolveExpert
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": "SuNavar/Pygenesis_ResolveExpert" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use SuNavar/Pygenesis_ResolveExpert with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SuNavar/Pygenesis_ResolveExpert
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 SuNavar/Pygenesis_ResolveExpert
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use SuNavar/Pygenesis_ResolveExpert with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SuNavar/Pygenesis_ResolveExpert
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 "SuNavar/Pygenesis_ResolveExpert" \ --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 SuNavar/Pygenesis_ResolveExpert with Docker Model Runner:
docker model run hf.co/SuNavar/Pygenesis_ResolveExpert
- Lemonade
How to use SuNavar/Pygenesis_ResolveExpert with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SuNavar/Pygenesis_ResolveExpert
Run and chat with the model
lemonade run user.Pygenesis_ResolveExpert-{{QUANT_TAG}}List all available models
lemonade list
File size: 3,769 Bytes
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language:
- en
license: apache-2.0
pipeline_tag: text-generation
library_name: llama-cpp
tags:
- davinci-resolve
- video-editing
- local-inference
- gguf
- qwen
base_model: Qwen/Qwen2.5-7B-Instruct
---
# Pygenesis ResolveExpert
Pygenesis ResolveExpert is a task-oriented assistant model for **DaVinci Resolve** workflows (Edit, Color, Fusion, Fairlight, Deliver), packaged for local inference as GGUF.
## Model Overview
`pygenesis-resolve-q4km.gguf` is a quantized local-inference variant of a Resolve-focused fine-tuned model.
Main goals:
- Provide practical, step-by-step help for real editing and post-production tasks.
- Keep latency reasonable on desktop hardware.
- Run fully on-device with no cloud dependency.
## Studio vs Free: Context Behavior
The model is the same in both editions.
The key difference is **how runtime context is provided**.
### DaVinci Resolve Studio (Integrated Plugin)
In Studio, the Workflow Integration plugin can automatically read and pass context such as:
- Current Resolve page (`Media`, `Cut`, `Edit`, `Fusion`, `Color`, `Fairlight`, `Deliver`)
- Current project and timeline names
- Basic timeline metadata
This usually improves relevance because answers are grounded in the user’s current working state.
### DaVinci Resolve Free (Companion App)
Resolve Free does not support the Workflow Integration plugin, so usage is via **Pygenesis Companion** (external app).
In this mode, context is provided manually:
- User selects the active page
- User can optionally provide project/timeline names
The model quality is unchanged, but contextual precision depends on the information entered by the user.
## Practical Summary
- **Same model weights** in Studio and Free.
- **Studio**: automatic context -> more situational responses.
- **Free**: manual context -> still useful responses, with lower contextual precision when input context is incomplete.
## Recommended Use Cases
- Resolve workflow troubleshooting ("how do I do X in Color/Fusion/Edit?").
- Page-specific checklists ("what should I review here?").
- Export and performance best practices.
- Actionable next steps for practical post-production decisions.
## Limitations
- Not a replacement for official Blackmagic documentation.
- Advanced workflows may still require iterative clarification.
- In Free mode, missing manual context can reduce specificity.
## Prompting Tips
For best results, include:
- Current Resolve page.
- Clear goal ("match two shots", "export for YouTube 4K", etc.).
- Constraints (GPU, Resolve version, footage type, deadline).
Example:
> "I am on the Color page. I have two shots with different exposure and need a fast matching workflow without damaging skin tones."
## Installation & Usage
Pygenesis ResolveExpert is distributed as a **packaged installer**, not as a manual developer setup.
The installer handles:
- GPU detection and inference backend selection (CUDA, Vulkan, or CPU)
- Model download from this Hugging Face repository
- UI installation for your Resolve edition
After installation:
| Edition | How you use it |
|---------|----------------|
| **DaVinci Resolve Studio** | Open the integrated plugin from **Workspace → Workflow Integrations → Pygenesis Resolve Tutor** |
| **DaVinci Resolve Free** | Launch **Pygenesis Companion** (standalone app installed alongside the model) |
Inference runs locally on your machine. No account or API key is required beyond downloading the model through the installer.
## Intended Users
Editors, colorists, and technical users who want a local assistant tailored to DaVinci Resolve workflows.
## Acknowledgements
- Built for the Pygenesis ResolveExpert project.
- Resolve integration behavior follows Blackmagic’s Studio/Free plugin constraints.
|