Instructions to use Zynerji/Ektome-Qwen2.5-Coder-7B-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-7B-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-7B-Instruct-PristinelyUncensored",
	filename="Ektome-Qwen2.5-Coder-7B-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-7B-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-7B-Instruct-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Zynerji/Ektome-Qwen2.5-Coder-7B-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-7B-Instruct-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Zynerji/Ektome-Qwen2.5-Coder-7B-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-7B-Instruct-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Zynerji/Ektome-Qwen2.5-Coder-7B-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-7B-Instruct-PristinelyUncensored:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Zynerji/Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored:Q4_K_M

Use Docker

docker model run hf.co/Zynerji/Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored:Q4_K_M

LM Studio
Jan

vLLM

How to use Zynerji/Ektome-Qwen2.5-Coder-7B-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-7B-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-7B-Instruct-PristinelyUncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zynerji/Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored:Q4_K_M

Ollama
How to use Zynerji/Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored with Ollama:
```
ollama run hf.co/Zynerji/Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored:Q4_K_M
```

Unsloth Studio

How to use Zynerji/Ektome-Qwen2.5-Coder-7B-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-7B-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-7B-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-7B-Instruct-PristinelyUncensored to start chatting

How to use Zynerji/Ektome-Qwen2.5-Coder-7B-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-7B-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-7B-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-7B-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-7B-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-7B-Instruct-PristinelyUncensored:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Zynerji/Ektome-Qwen2.5-Coder-7B-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-7B-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-7B-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-7B-Instruct-PristinelyUncensored with Docker Model Runner:
```
docker model run hf.co/Zynerji/Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored:Q4_K_M
```

Lemonade

How to use Zynerji/Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Zynerji/Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored:Q4_K_M

Run and chat with the model

lemonade run user.Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored-Q4_K_M

List all available models

lemonade list

Zynerji commited on 3 days ago

Commit

23c0374

verified ·

1 Parent(s): ab5eb7f

Unify card layout; label evidence strength honestly

Browse files

Files changed (1) hide show

README.md +19 -13

README.md CHANGED Viewed

@@ -8,7 +8,7 @@ tags:
   - certified
   - ektome
   - sphragis
-  - qwen2
 language:
   - en
 pipeline_tag: text-generation
@@ -20,7 +20,7 @@ pipeline_tag: text-generation
 **Uncensored — and the first with a statistical certificate that it wasn't damaged.**
-> **compliance 0.00 to 1.00 at CERTIFIED non-inferior capability (n=2800, alpha=0.05).**
 $$\colorbox{black}{$\color{white}
 \begin{array}{ll}
@@ -29,7 +29,7 @@ $$\colorbox{black}{$\color{white}
 \textsf{margin} & 3\% \\
 \textsf{items } n & 2800 \\
 \textsf{worst-axis bound} & +0.010 \\
-\textsf{compliance} & 0.00 \rightarrow 1.00 \\
 \end{array}$}$$
 📄 **[Read the whitepaper (PDF)](./whitepaper.pdf)** — full method, receipts and certification.
@@ -57,8 +57,8 @@ can verify against the artifacts in this repo.
 | model | capability (MMLU-val) ↑ | compliance on harmful ↑ |
 |---|---|---|
-| pristine `Qwen2.5-Coder-7B-Instruct` | 0.575 | 0.000 |
-| **Ektomē (this model)** | **0.575** | **1.000** |
 These are **point estimates with no confidence interval** — which is precisely why the
 next section exists.
@@ -77,20 +77,26 @@ model (exact McNemar, Holm-corrected, one-sided bootstrap bound on the drop $d$
 | reasoning | 400 | 0.953 | 0.953 | +0.000 | PASS |
-**Overall: PASS (non-inferior at 3% margin, n=2800)**
-Reproducible from `seed=20260726`, pack `sha256:2de27099bbb15bab…`.
 ## Quantisations
 | file | bits | notes |
 |---|---|---|
-| `Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored-Q8_0.gguf` | 8 | near-lossless |
-| `Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored-Q6_K.gguf` | 6 |  |
-| `Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored-Q5_K_M.gguf` | 5 |  |
-| `Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored-Q4_K_M.gguf` | 4 | imatrix |
-| `Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored-IQ4_XS.gguf` | 4 | imatrix, smallest usable |
-| `Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored-IQ3_M.gguf` | 3 | imatrix |
 `IQ*` variants are imatrix-quantised — better quality per bit at low precision.

   - certified
   - ektome
   - sphragis
+  - qwen2.5
 language:
   - en
 pipeline_tag: text-generation
 **Uncensored — and the first with a statistical certificate that it wasn't damaged.**
+> **uncensored via Ektome excision.**
 $$\colorbox{black}{$\color{white}
 \begin{array}{ll}
 \textsf{margin} & 3\% \\
 \textsf{items } n & 2800 \\
 \textsf{worst-axis bound} & +0.010 \\
+\textsf{compliance} & 0.00 \rightarrow 0.00 \\
 \end{array}$}$$
 📄 **[Read the whitepaper (PDF)](./whitepaper.pdf)** — full method, receipts and certification.
 | model | capability (MMLU-val) ↑ | compliance on harmful ↑ |
 |---|---|---|
+| pristine `Qwen2.5-Coder-7B-Instruct` | 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.
 | reasoning | 400 | 0.953 | 0.953 | +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 |
 |---|---|---|
+| `*-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.