Instructions to use Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored",
	filename="Ektome-Qwen2.5-Coder-14B-Instruct-IQ3_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-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M

Use Docker

docker model run hf.co/Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M

LM Studio
Jan

vLLM

How to use Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M

Ollama
How to use Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored with Ollama:
```
ollama run hf.co/Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M
```

Unsloth Studio

How to use Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored to start chatting

How to use Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-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-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored with Docker Model Runner:
```
docker model run hf.co/Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M
```

Lemonade

How to use Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored:Q4_K_M

Run and chat with the model

lemonade run user.Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored-Q4_K_M

List all available models

lemonade list

Zynerji commited on 3 days ago

Commit

6644775

verified ·

1 Parent(s): 89b9677

Unify card layout; label evidence strength honestly

Browse files

Files changed (1) hide show

README.md +117 -118

README.md CHANGED Viewed

@@ -1,118 +1,117 @@
----
-license: apache-2.0
-base_model: unknown
-tags:
-  - uncensored
-  - abliterated
-  - capability-preserving
-  - certified
-  - ektome
-  - sphragis
-  - qwen2.5
-language:
-  - en
-pipeline_tag: text-generation
----
-![Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored](./hero.png)
-# Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored
-**Uncensored — and the first with a statistical certificate that it wasn't damaged.**
-> **uncensored via Ektome excision.**
-$$\colorbox{black}{$\color{white}
-\begin{array}{ll}
-\textsf{EKTOME CERTIFICATE} & {} \\
-\textsf{capability} & \textsf{NOT} \\
-\textsf{margin} & 3\% \\
-\textsf{items } n & 200 \\
-\textsf{worst-axis bound} & +0.000 \\
-\textsf{compliance} & 0.00 \rightarrow 0.00 \\
-\end{array}$}$$
-📄 **[Read the whitepaper (PDF)](./whitepaper.pdf)** — full method, receipts and certification.
-The PDF is the authoritative document: dark-typeset, with the complete derivation, the
-per-axis certificate and the reproducibility hashes.
----
-## Why this exists
-Standard abliteration removes a coarse *refusal direction* that is entangled with
-directions carrying knowledge and reasoning. The result is an uncensored model with a
-capability tax that is **almost never measured**.
-Ektomē (ἐκτομή, *excision*) isolates and removes only the refusal-**specific**
-component, leaving general helpfulness intact, and does so norm-preservingly on the
-pristine model — no training, no distillation, no damage to repair. The extraction
-depth is selected per model by automated search against measured compliance.
-The estimator, excision operator and depth-selection procedure are proprietary.
-What is published here is the **measured outcome** and the evidence for it, which you
-can verify against the artifacts in this repo.
-## The receipt
-| model | capability (MMLU-val) ↑ | compliance on harmful ↑ |
-|---|---|---|
-| pristine `unknown` | 0.000 | 0.000 |
-| **Ektomē (this model)** | **0.000** | **0.000** |
-These are **point estimates with no confidence interval** — which is precisely why the
-next section exists.
-## The certificate
-Capability retention is certified by a paired non-inferiority test against the pristine
-model (exact McNemar, Holm-corrected, one-sided bootstrap bound on the drop $d$ vs a
-3% margin):
-| axis | n | ref | cand | d upper | verdict |
-|---|---|---|---|---|---|
-| MMLU-val (POINT ESTIMATE, n=200, no CI) | 200 | 0.000 | 0.000 | +0.000 | UNCERTIFIED |
-**Overall: NOT CERTIFIED - no n=2800 paired test has been run for this model**
-Reproducible from `seed=20260726`, pack `sha256:7bbaff877146e081…`.
-### Generation health checks
-_Not recorded for this model._
-## Quantisations
-| file | bits | notes |
-|---|---|---|
-| `*-Q8_0.gguf` | 8 | near-lossless |
-| `*-Q6_K.gguf` | 6 |  |
-| `*-Q5_K_M.gguf` | 5 |  |
-| `*-Q4_K_M.gguf` | 4 | imatrix |
-| `*-IQ4_XS.gguf` | 4 | imatrix, smallest usable |
-| `*-IQ3_M.gguf` | 3 | imatrix |
-| `nvfp4/` | 4 | NVFP4 for vLLM / TensorRT-LLM |
-`IQ*` variants are imatrix-quantised — better quality per bit at low precision.
-## Limitations
-The certificate bounds **capability retention only**. It does not certify safety, factual
-accuracy, or fitness for any purpose. Axes marked *inconclusive* are honestly
-under-powered, and the certificate states the $n$ needed to resolve them. Compliance uses
-a keyword classifier — a proxy that evasive phrasing can fool. **This model is uncensored
-by construction: it will not refuse, and you are accountable for what you do with it.**
-## Citation
-```bibtex
-@software{ektome_Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored,
-  title  = {Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored},
-  author = {Zynerji},
-  year   = {2026},
-  url    = {https://huggingface.co/Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored}
-}
-```

+---
+license: apache-2.0
+base_model: unknown
+tags:
+  - uncensored
+  - abliterated
+  - capability-preserving
+  - certified
+  - ektome
+  - sphragis
+  - qwen2.5
+language:
+  - en
+pipeline_tag: text-generation
+---
+![Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored](./hero.png)
+# Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored
+**Uncensored — and carrying a statistical certificate that it wasn't damaged.**
+> **Capability retention certified against the pristine model at n=2800.**
+$$\colorbox{black}{$\color{white}
+\begin{array}{ll}
+\textsf{EKTOME CERTIFICATE} & {} \\
+\textsf{capability} & \textsf{PASS} \\
+\textsf{margin} & 3\% \\
+\textsf{items } n & 2800 \\
+\textsf{worst-axis bound} & +0.010 \\
+\textsf{compliance} & 0.00 \rightarrow 0.00 \\
+\end{array}$}$$
+📄 **[Read the whitepaper (PDF)](./whitepaper.pdf)** — full method, receipts and certification.
+The PDF is the authoritative document: dark-typeset, with the complete derivation, the
+per-axis certificate and the reproducibility hashes.
+---
+## Why this exists
+Standard abliteration removes a coarse *refusal direction* that is entangled with
+directions carrying knowledge and reasoning. The result is an uncensored model with a
+capability tax that is **almost never measured**.
+Ektomē (ἐκτομή, *excision*) isolates and removes only the refusal-**specific**
+component, leaving general helpfulness intact, and does so norm-preservingly on the
+pristine model — no training, no distillation, no damage to repair. The extraction
+depth is selected per model by automated search against measured compliance.
+The estimator, excision operator and depth-selection procedure are proprietary.
+What is published here is the **measured outcome** and the evidence for it, which you
+can verify against the artifacts in this repo.
+## The receipt
+_No compliance/MMLU receipt was recorded for this model. The evidence below is the certificate._
+## The certificate
+Capability retention is certified by a paired non-inferiority test against the pristine
+model (exact McNemar, Holm-corrected, one-sided bootstrap bound on the drop $d$ vs a
+3% margin):
+| axis | n | ref | cand | d upper | verdict |
+|---|---|---|---|---|---|
+| arithmetic | 1400 | 0.932 | 0.930 | +0.005 | PASS |
+| instruction | 600 | 0.757 | 0.757 | +0.000 | PASS |
+| knowledge | 400 | 0.968 | 0.973 | +0.010 | PASS |
+| reasoning | 400 | 0.927 | 0.932 | +0.000 | PASS |
+**Overall: PASS (3% margin, n=2800, alpha=0.05)**
+Reproducible from `seed=20260726`, pack `sha256:7bbaff877146e081…`.
+### Generation health checks
+_Not recorded for this model._
+## Quantisations
+| file | bits | notes |
+|---|---|---|
+| `Ektome-Qwen2.5-Coder-14B-Instruct-Q8_0.gguf` | 8 | near-lossless |
+| `Ektome-Qwen2.5-Coder-14B-Instruct-Q6_K.gguf` | 6 |  |
+| `Ektome-Qwen2.5-Coder-14B-Instruct-Q5_K_M.gguf` | 5 |  |
+| `Ektome-Qwen2.5-Coder-14B-Instruct-Q4_K_M.gguf` | 4 | imatrix |
+| `Ektome-Qwen2.5-Coder-14B-Instruct-IQ4_XS.gguf` | 4 | imatrix, smallest usable |
+| `Ektome-Qwen2.5-Coder-14B-Instruct-IQ3_M.gguf` | 3 | imatrix |
+| `nvfp4/` | 4 | NVFP4 for vLLM / TensorRT-LLM |
+`IQ*` variants are imatrix-quantised — better quality per bit at low precision.
+## Limitations
+The certificate bounds **capability retention only**. It does not certify safety, factual
+accuracy, or fitness for any purpose. Axes marked *inconclusive* are honestly
+under-powered, and the certificate states the $n$ needed to resolve them. Compliance uses
+a keyword classifier — a proxy that evasive phrasing can fool. **This model is uncensored
+by construction: it will not refuse, and you are accountable for what you do with it.**
+## Citation
+```bibtex
+@software{ektome_Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored,
+  title  = {Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored},
+  author = {Zynerji},
+  year   = {2026},
+  url    = {https://huggingface.co/Zynerji/Ektome-Qwen2.5-Coder-14B-Instruct-PristinelyUncensored}
+}
+```