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

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.

Downloads last month: 14

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for SuNavar/Pygenesis_ResolveExpert

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(369)

this model