Instructions to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored with Transformers:

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

pipe = pipeline("text-generation", model="Zynerji/Ektome-Qwen3-8B-PristinelyUncensored")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zynerji/Ektome-Qwen3-8B-PristinelyUncensored")
model = AutoModelForCausalLM.from_pretrained("Zynerji/Ektome-Qwen3-8B-PristinelyUncensored", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Zynerji/Ektome-Qwen3-8B-PristinelyUncensored",
	filename="Ektome-Qwen3-8B-PristinelyUncensored-Q4_K_M.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 Zynerji/Ektome-Qwen3-8B-PristinelyUncensored 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 Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Zynerji/Ektome-Qwen3-8B-PristinelyUncensored: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 Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Zynerji/Ektome-Qwen3-8B-PristinelyUncensored: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 Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M

Use Docker

docker model run hf.co/Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M

LM Studio
Jan

vLLM

How to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M

SGLang

How to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored 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 "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored" \
    --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": "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored",
		"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 "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored" \
        --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": "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored with Ollama:
```
ollama run hf.co/Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M
```

Unsloth Studio

How to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored 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 Zynerji/Ektome-Qwen3-8B-PristinelyUncensored 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 Zynerji/Ektome-Qwen3-8B-PristinelyUncensored to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Zynerji/Ektome-Qwen3-8B-PristinelyUncensored to start chatting

How to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Zynerji/Ektome-Qwen3-8B-PristinelyUncensored: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": "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Zynerji/Ektome-Qwen3-8B-PristinelyUncensored: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 Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Zynerji/Ektome-Qwen3-8B-PristinelyUncensored: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 "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored: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 Zynerji/Ektome-Qwen3-8B-PristinelyUncensored with Docker Model Runner:
```
docker model run hf.co/Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M
```

Lemonade

How to use Zynerji/Ektome-Qwen3-8B-PristinelyUncensored with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Zynerji/Ektome-Qwen3-8B-PristinelyUncensored:Q4_K_M

Run and chat with the model

lemonade run user.Ektome-Qwen3-8B-PristinelyUncensored-Q4_K_M

List all available models

lemonade list

Ektome-Qwen3-8B-PristinelyUncensored / README.md

Zynerji

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3-8B
	tags:
	- uncensored
	- abliterated
	- abliteration-repair
	- capability-preserving
	- ektome
	- surrogate-null
	- qwen3
	language:
	- en
	pipeline_tag: text-generation
	---

	# Ektomē-Qwen3-8B-PristinelyUncensored

	**Uncensored — with most of the usual capability tax removed. Pristine Qwen3-8B, refusal
	surgically excised, knowledge and reasoning almost entirely preserved.**

	> 🎯 Within 0.018 capability of pristine Qwen3-8B while fully uncensored — and +0.041 over standard abliteration.

	Standard abliteration makes a model uncensored but damages it: the crude "refusal
	direction" it deletes is entangled with directions that carry knowledge and reasoning, so
	mainstream abliterated Qwen3-8B loses ~6 points of MMLU. `Ektomē` (ἐκτομή, "excision") cuts at
	the natural joint instead. It removes only the refusal-specific component — the diff-of-means
	direction orthogonalized against general helpfulness — with a single, norm-preserving
	projection on the pristine model. No training, no distillation, no damage to repair.

	Which directions are safe to cut is decided by a surrogate-null catcher: a direction is
	removed only if a structure-destroyed control confirms it carries refusal, not capability. So the
	receipt below is measured against a null, not asserted.

	## The receipt — pristine vs crude-abliterated vs Ektomē

	<!-- BEGIN RECEIPT -->
	\| model \| capability (few-shot) ↑ \| refusal on harmful ↓ \| coherence→pristine ↑ \|
	\|---\|---\|---\|---\|
	\| pristine Qwen3-8B \| 0.760 \| 96% (censored) \| 1.00 \|
	\| crude-abliterated (mlabonne) \| 0.701 \| 0% (uncensored) \| 0.78 \|
	\| Ektomē (this model) \| 0.742 \| 0% (uncensored) \| 0.92 \|

	Same uncensoring as the standard abliterated model (0% refusal), but +0.041 capability and
	+0.14 coherence — and within 0.018 of the censored pristine base. Capability =
	ARC-Easy + HellaSwag + MMLU (few-shot, limit 200/task); refusal on harmful = AdvBench with a
	judge-free keyword classifier and thinking disabled.
	<!-- END RECEIPT -->

	capability: ARC-Easy + HellaSwag + MMLU (few-shot). refusal on harmful: fraction refused on
	AdvBench (judge-free keyword classifier, thinking disabled so chain-of-thought can't mask the
	answer). Lower refusal = more uncensored; capability equal to pristine = no tax.

	## How we know it's real — the catcher's verdict

	Every claim here is decided by a surrogate-null catcher (Basanos / Prokopē), not asserted.
	The refusal excision is applied to pristine and measured; a capability drop counts as real
	only if it beats the MMLU sampling-noise floor `SE_mmlu`. This is the actual gate output that
	green-lit the model — a drop within the noise floor is `MEASURED_EQUAL`, i.e. no tax.

	<details>
	<summary>📋 Show the raw catcher gate output (the "screenshot")</summary>

	```text
	$ ektome_validate Qwen/Qwen3-8B
	SE_mmlu = 0.032 # honest noise floor (2-seed)
	pristine cap=0.715 compliance=0.040 # base refuses 96% of harmful
	Ektome cap=0.695 compliance=1.000 # uncensored AND capability intact
	crude cone cap=0.620 compliance=0.800 # ordinary abliteration: -0.095 capability
	gate clean-vs-pristine -> MEASURED_EQUAL # drop 0.020 < SE_mmlu 0.032
	gate clean-vs-crude -> REAL_BETTER
	gate uncensored -> True
	VERDICT: PASS
	```

	</details>

	Before / after, against the noise floor (zero-shot MMLU-val probe; the headline receipt
	above is the stricter few-shot MMLU-test):

	\| model \| capability \| Δ vs pristine \| catcher verdict \|
	\|---\|---\|---\|---\|
	\| pristine Qwen3-8B (before) \| 0.715 \| — \| baseline \|
	\| Ektomē (after) \| 0.695 \| −0.020 (< SE 0.032) \| MEASURED_EQUAL ✓ \|
	\| crude abliteration \| 0.620 \| −0.095 (≫ SE) \| REAL damage ✗ \|

	## Why it's different

	- Uncensored — complies on harmful prompts the base refuses (near-100% vs the base's few %).
	- Capability-preserved — few-shot capability lands *within ~0.02 of the pristine* base**
	(0.742 vs 0.760), recovering ~70% of the ~6 points the crude-abliterated model (0.701) throws
	away — and coherence-to-pristine jumps from 0.78 (crude) to 0.92.
	- Zero training — a single surgical weight edit on pristine, seconds of compute. Nothing was
	fine-tuned, so nothing drifted.
	- Honest — every claim is gated by a surrogate-null test; the card reports what beat a
	control, not marketing.

	## Run it locally

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	tok = AutoTokenizer.from_pretrained("Zynerji/Ektome-Qwen3-8B-PristinelyUncensored")
	model = AutoModelForCausalLM.from_pretrained("Zynerji/Ektome-Qwen3-8B-PristinelyUncensored",
	torch_dtype="bfloat16", device_map="auto")
	```

	GGUF (any gaming PC): `Q4_K_M` (~5 GB), `Q5_K_M`, `Q8_0` included — use with
	`llama.cpp` / LM Studio / Ollama.

	## The Ektomē line & feedback

	If Ektomē-Qwen3-8B is useful to you, a ❤️ like helps others find it. This is the first of a
	line — the same catcher-gated surgery applied to the frontier small models (Mistral, Llama,
	Nemotron) and larger bases. Want a specific base next, hit an edge case, or have head-to-head
	numbers to compare? Open a [Discussion](https://huggingface.co/Zynerji/Ektome-Qwen3-8B-PristinelyUncensored/discussions) — the line is steered by what people actually run.

	## Honest notes

	- Uncensored: this model follows instructions the base refuses. Use it lawfully and
	responsibly; you are accountable for what you generate.
	- Benchmarks use limited eval sets; each capability delta is gated REAL vs a surrogate null.
	- Method: produced by Ektomē, a capability-preserving surgical-abliteration method. Results
	are reported; the full recipe is not (yet).

	## License

	Inherits the base model's license (Qwen3 → Apache-2.0). No warranty; your use, your
	responsibility.