Instructions to use SuNavar/Pygenesis_ResolveExpert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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

Pygenesis_ResolveExpert / README.md

SuNavar

Update README.md

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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.