Instructions to use prasadvittaldev/dogmatix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use prasadvittaldev/dogmatix-GGUF 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 prasadvittaldev/dogmatix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prasadvittaldev/dogmatix-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prasadvittaldev/dogmatix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prasadvittaldev/dogmatix-GGUF: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 prasadvittaldev/dogmatix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prasadvittaldev/dogmatix-GGUF: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 prasadvittaldev/dogmatix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prasadvittaldev/dogmatix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prasadvittaldev/dogmatix-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use prasadvittaldev/dogmatix-GGUF with Ollama:
```
ollama run hf.co/prasadvittaldev/dogmatix-GGUF:Q4_K_M
```

Unsloth Studio

How to use prasadvittaldev/dogmatix-GGUF 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 prasadvittaldev/dogmatix-GGUF 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 prasadvittaldev/dogmatix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prasadvittaldev/dogmatix-GGUF to start chatting

How to use prasadvittaldev/dogmatix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prasadvittaldev/dogmatix-GGUF:Q4_K_M

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": "prasadvittaldev/dogmatix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use prasadvittaldev/dogmatix-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prasadvittaldev/dogmatix-GGUF: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 "prasadvittaldev/dogmatix-GGUF: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"

Docker Model Runner
How to use prasadvittaldev/dogmatix-GGUF with Docker Model Runner:
```
docker model run hf.co/prasadvittaldev/dogmatix-GGUF:Q4_K_M
```

Lemonade

How to use prasadvittaldev/dogmatix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prasadvittaldev/dogmatix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.dogmatix-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prasadvittaldev/dogmatix-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prasadvittaldev/dogmatix-GGUF: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 prasadvittaldev/dogmatix-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

dogmatix v0.2 — GGUF

GGUF quantisations of dogmatix v0.2, a QLoRA fine-tune of google/gemma-4-E2B-it (~2.3B effective) into a local, Python-specialised web-application coding agent. For llama.cpp / LM Studio / Ollama.

v0.2 supersedes v0.1 (the v0.1 GGUFs are on the v0.1 branch). See the full model card for honest, single-harness benchmarks and limitations.

Files

file	quant	size	CPU tok/s*
`dogmatix-q8_0.gguf`	Q8_0	4.6 GB	~19
`dogmatix-q5_k_m.gguf`	Q5_K_M	3.4 GB	~26
`dogmatix-q4_k_m.gguf`	Q4_K_M	3.2 GB	~30

*Rough llama-bench text-generation on 4 CPU threads; identified as gemma4 E2B, 4.63B.

Q4_K_M is the smallest/fastest and the usual default; Q8_0 is the most faithful to the bf16 weights. All three were verified to load and generate.

What it is for

Small, well-specified Python web work — FastAPI, Flask, SQLAlchemy/SQLModel — through an agent loop (read → edit → test → fix). v0.2 is a sharper web-app specialisation than v0.1: best of the line at end-to-end app-building (pass@1 0.18 base → 0.36 v0.2 on a 12-spec hidden-suite benchmark; monotone across checkpoints, not yet statistically significant), at a measured cost to Django (0.500 vs 0.567 base) and general Python (MBPP 0.700 vs 0.760 base). If you need Django or a general coder as much as app-building, use the v0.1 branch or the base.

Note on tool-calling

dogmatix expects the serving translation layer in the repo for full agentic tool-calling (turn-boundary handling, blind-call guard, client tool-name mapping). Raw GGUF inference works for generation; agentic use through Claude Code / Pi goes through that proxy.

Author

Prasad Vittaldev

Downloads last month: 357

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

5-bit

8-bit

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Model tree for prasadvittaldev/dogmatix-GGUF

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

prasadvittaldev/dogmatix

Quantized

(1)

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