Instructions to use may-me-1998/uiaa-1.0-34B-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 may-me-1998/uiaa-1.0-34B-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 may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf may-me-1998/uiaa-1.0-34B-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 may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf may-me-1998/uiaa-1.0-34B-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 may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf may-me-1998/uiaa-1.0-34B-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 may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use may-me-1998/uiaa-1.0-34B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "may-me-1998/uiaa-1.0-34B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "may-me-1998/uiaa-1.0-34B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M

Ollama
How to use may-me-1998/uiaa-1.0-34B-GGUF with Ollama:
```
ollama run hf.co/may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M
```

Unsloth Studio

How to use may-me-1998/uiaa-1.0-34B-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 may-me-1998/uiaa-1.0-34B-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 may-me-1998/uiaa-1.0-34B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for may-me-1998/uiaa-1.0-34B-GGUF to start chatting

How to use may-me-1998/uiaa-1.0-34B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf may-me-1998/uiaa-1.0-34B-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": "may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use may-me-1998/uiaa-1.0-34B-GGUF with Docker Model Runner:
```
docker model run hf.co/may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M
```

Lemonade

How to use may-me-1998/uiaa-1.0-34B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.uiaa-1.0-34B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use may-me-1998/uiaa-1.0-34B-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 may-me-1998/uiaa-1.0-34B-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 may-me-1998/uiaa-1.0-34B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use may-me-1998/uiaa-1.0-34B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf may-me-1998/uiaa-1.0-34B-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 "may-me-1998/uiaa-1.0-34B-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"

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Ornith-1.0-35B-uncensored - GGUF

A decensored (Abliterix-abliterated) version of deepreinforce-ai/Ornith-1.0-35B - a Qwen3.5-MoE 35B coding and reasoning model (256 experts/layer, 8 active per token).

Abliteration technique: Arditi et al. (2024). Decensoring tool: Abliterix v1.9.0 with Expert-Granular Abliteration (EGA).

Files

Quant	Size	Download	Notes
Q2_K	~12 GB	Download	Smallest - noticeable quality loss
Q3_K_M	~15 GB	Download	Good for low-VRAM setups
Q4_K_M	21 GB	Download	Recommended - best size/quality balance
Q6_K	29 GB	Download	Near-lossless
Q8_0	37 GB	Download	Essentially full precision

Usage

Download a quant above, then:

# Server - OpenAI-compatible API on :8080
llama-server -m ornith-1.0-35b-uncensored-Q4_K_M.gguf -ngl 99 -c 2048 --jinja --port 8080

# CLI
llama-cli -m ornith-1.0-35b-uncensored-Q4_K_M.gguf -ngl 99 --jinja

--jinja is required - without it the model uses a generic template and compliance degrades.

Minimum VRAM: Q2_K ~13 GB, Q3_K_M ~16 GB, Q4_K_M ~22 GB, Q6_K ~30 GB, Q8_0 ~38 GB.

About the base model

Ornith-1.0-35B by deepreinforce-ai is a Qwen3.5-MoE 35B model with strong coding and reasoning capabilities. Architecture: Qwen3_5MoeForConditionalGeneration (256 routed experts + 1 shared expert per layer, 8 active per token).

Intended use & disclaimer

For security research, red-teaming, penetration testing, CTF challenges, and defensive tooling development. The abliteration removes refusal behaviour - do not use for harmful purposes. The authors bear no responsibility for misuse.

Provenance

Base model: deepreinforce-ai/Ornith-1.0-35B
Abliteration: Abliterix v1.9.0 by Wangzhang Wu
Technique: Arditi et al., "Refusal in Language Models Is Mediated by a Single Direction" (2024)
Refusal dataset: zaakirio/infosec-refusal-prompts
GGUF conversion: ggml-org/llama.cpp b9821

Downloads last month: -

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

2-bit

3-bit

4-bit

6-bit

8-bit

Model tree for may-me-1998/uiaa-1.0-34B-GGUF

Base model

ornith-ai/Ornith-1.0-35B

Quantized

(184)

this model

Paper for may-me-1998/uiaa-1.0-34B-GGUF

Refusal in Language Models Is Mediated by a Single Direction

Paper • 2406.11717 • Published Jun 17, 2024 • 15