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

Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored

File size: 3,211 Bytes

1c8792b

{
  "sphragis_version": "0.1.0",
  "generated_at": "2026-07-26T22:50:16.638557+00:00",
  "reference": {
    "endpoint": "http://127.0.0.1:8080/v1",
    "model": "ref"
  },
  "candidate": {
    "endpoint": "http://127.0.0.1:8081/v1",
    "model": "cand"
  },
  "pack": {
    "name": "/root/pack_v2.jsonl",
    "n_tasks": 2800,
    "sha256": "2de27099bbb15bab4f7b35599b215f038fdb68b3f5f4e1cda1a464f9dd18e14e"
  },
  "overall": "PASS",
  "claim": "For every axis, the candidate was demonstrated non-inferior to the reference: the one-sided 95% upper confidence bound on the accuracy regression is below the margin of 3.0%.",
  "axes": [
    {
      "axis": "arithmetic",
      "n": 1400,
      "counts": {
        "both_correct": 1205,
        "ref_only": 5,
        "cand_only": 6,
        "both_wrong": 184
      },
      "acc_reference": 0.864286,
      "acc_candidate": 0.865,
      "regression_d": -0.000714,
      "d_ci": [
        -0.005714,
        0.004286
      ],
      "d_upper_bound": 0.002857,
      "p_regression": 0.72558594,
      "p_regression_holm": 1.0,
      "p_improvement": 0.5,
      "improved": false,
      "mde_at_power": 0.005886,
      "n_needed_for_margin": 204,
      "verdict": "PASS",
      "reason": "non_inferior_within_margin"
    },
    {
      "axis": "instruction",
      "n": 600,
      "counts": {
        "both_correct": 394,
        "ref_only": 4,
        "cand_only": 2,
        "both_wrong": 200
      },
      "acc_reference": 0.663333,
      "acc_candidate": 0.66,
      "regression_d": 0.003333,
      "d_ci": [
        -0.005,
        0.011667
      ],
      "d_upper_bound": 0.01,
      "p_regression": 0.34375,
      "p_regression_holm": 1.0,
      "p_improvement": 0.890625,
      "improved": false,
      "mde_at_power": 0.01,
      "n_needed_for_margin": 204,
      "verdict": "PASS",
      "reason": "non_inferior_within_margin"
    },
    {
      "axis": "knowledge",
      "n": 400,
      "counts": {
        "both_correct": 387,
        "ref_only": 0,
        "cand_only": 0,
        "both_wrong": 13
      },
      "acc_reference": 0.9675,
      "acc_candidate": 0.9675,
      "regression_d": 0.0,
      "d_ci": [
        0.0,
        0.0
      ],
      "d_upper_bound": 0.0,
      "p_regression": 1.0,
      "p_regression_holm": 1.0,
      "p_improvement": 1.0,
      "improved": false,
      "mde_at_power": null,
      "n_needed_for_margin": 204,
      "verdict": "PASS",
      "reason": "non_inferior_within_margin"
    },
    {
      "axis": "reasoning",
      "n": 400,
      "counts": {
        "both_correct": 381,
        "ref_only": 0,
        "cand_only": 0,
        "both_wrong": 19
      },
      "acc_reference": 0.9525,
      "acc_candidate": 0.9525,
      "regression_d": 0.0,
      "d_ci": [
        0.0,
        0.0
      ],
      "d_upper_bound": 0.0,
      "p_regression": 1.0,
      "p_regression_holm": 1.0,
      "p_improvement": 1.0,
      "improved": false,
      "mde_at_power": null,
      "n_needed_for_margin": 204,
      "verdict": "PASS",
      "reason": "non_inferior_within_margin"
    }
  ],
  "params": {
    "margin": 0.03,
    "alpha": 0.05,
    "n_floor": 30,
    "power": 0.8,
    "n_boot": 4000,
    "seed": 0
  }
}