Instructions to use ressl/Ornith-1.5-9B-uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ressl/Ornith-1.5-9B-uncensored-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ressl/Ornith-1.5-9B-uncensored-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ressl/Ornith-1.5-9B-uncensored-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ressl/Ornith-1.5-9B-uncensored-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 ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ressl/Ornith-1.5-9B-uncensored-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 ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ressl/Ornith-1.5-9B-uncensored-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 ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ressl/Ornith-1.5-9B-uncensored-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 ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ressl/Ornith-1.5-9B-uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ressl/Ornith-1.5-9B-uncensored-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": "ressl/Ornith-1.5-9B-uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M

SGLang

How to use ressl/Ornith-1.5-9B-uncensored-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ressl/Ornith-1.5-9B-uncensored-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ressl/Ornith-1.5-9B-uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ressl/Ornith-1.5-9B-uncensored-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ressl/Ornith-1.5-9B-uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ressl/Ornith-1.5-9B-uncensored-GGUF with Ollama:
```
ollama run hf.co/ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ressl/Ornith-1.5-9B-uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ressl/Ornith-1.5-9B-uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M
```

Lemonade

How to use ressl/Ornith-1.5-9B-uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.5-9B-uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ressl/Ornith-1.5-9B-uncensored-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 ressl/Ornith-1.5-9B-uncensored-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 ressl/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ressl/Ornith-1.5-9B-uncensored-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ressl/Ornith-1.5-9B-uncensored-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 "ressl/Ornith-1.5-9B-uncensored-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"

Ornith-1.5-9B uncensored (GGUF)

TL;DR: abliterated Ornith-1.5-9B as a GGUF ladder (BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M + mmproj BF16); hard refusals reduced from 767/1120 to 20/1120 (NVFP4 sibling measurement) with the Q4_K_M build measured at 101/1120 on the same 5-dataset cross-eval.

Same abliterated master as the NVFP4 repo, converted with llama.cpp for local and edge use. Built for security research, red-teaming and penetration-testing workflows; it will comply with requests a stock model refuses. Use responsibly.

Facts & figures


Base model	ornith-ai/Ornith-1.5-9B
Method	abliteration (3 biprojection passes) + llama.cpp GGUF conversion
Files	BF16 18.4 GB, Q8_0 9.8 GB, Q6_K 7.6 GB, Q5_K_M 6.6 GB, Q4_K_M 5.8 GB, mmproj BF16 0.92 GB
Context length	262,144 tokens (served here with 131,072)
Hardware used	2x NVIDIA RTX PRO 6000 Blackwell 96 GB (SM120)
Toolchain	llama.cpp (master, CUDA build), transformers 5.12.1

Refusal behavior (measured, hard markers only)

Effective refusal rate on 1120 harmful prompts across 5 datasets (temp 0, hard decline phrases in the first 25 words; naive keyword counts are inflated by compliant-with-disclaimer answers and are not the metric here). The NVFP4 column is the sibling repo's artifact, shown because the BF16 master it was measured on is identical; aggressive quantization visibly re-amplifies hedged declines (Q4 column).

Eval set	Prompts	Baseline hard	NVFP4 hard	Q4_K_M hard
JailbreakBench	100	74	4	7
tulu-harmbench	320	165	10	23
HarmfulQA	300	148	2	2
LLM-LAT	300	284	3	55
mlabonne harmful	100	96	3	14
TOTAL	1120	767	20	101

Coherence of the base master (measured on the NVFP4 sibling): GPQA-Diamond 60.1% (base model 49.5%), needle-in-haystack 1.0 at 32k/65k/131k, tool-call smoke 1.0, KL(base||ablated) median 0.324 (harmless subset 0.136).

Run it with llama.cpp (validated)

llama-server -m Ornith-1.5-9B-uncensored-Q4_K_M.gguf \
  --mmproj mmproj-Ornith-1.5-9B-uncensored-BF16.gguf \
  --jinja -ngl 99 -c 131072 --port 30003

Quality & limitations

Quantization amplifies residual refusals on this model: NVFP4 20/1120, GGUF Q4_K_M 101/1120 on the same eval. If you need the cleanest behavior, use the NVFP4 repo or the BF16 file here.
The Q4_K_M self-description smoke occasionally shows identity drift (answers as a generic assistant with a wrong lab name); factual coherence checks (math, capitals, code) pass on all quants.
Residual hedged declines remain by design trade-off: three ablation passes saturated, further removal damages the model.

Provenance & reproducibility

Same master as ressl/Ornith-1.5-9B-uncensored-NVFP4: residual directions from harmful-vs-harmless and refused-vs-complied rounds, norm-preserving biprojection on 64 residual-writing tensors. GGUF via convert_hf_to_gguf.py (BF16 + mmproj) and llama-quantize for the K-quants, validated with llama-server on the full cross-eval.

License & credits

MIT (inherited from Ornith by the Ornith team, credited to ornith-ai). Abliteration, conversion and validation by Robert Ressl (Hugging Face · Website · LinkedIn · Patreon). Built with llama.cpp, NVIDIA TensorRT Model Optimizer and SGLang.

Support this work: if these models are useful to you, consider supporting on Patreon. A lot of compute and care went into this release; more at ressl.ch.

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GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for ressl/Ornith-1.5-9B-uncensored-GGUF

Base model

ornith-ai/Ornith-1.5-9B

Finetuned

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