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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated 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 ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated: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 ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated: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 ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M

Use Docker

docker model run hf.co/ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M

LM Studio
Jan

vLLM

How to use ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M

Ollama
How to use ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated with Ollama:
```
ollama run hf.co/ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M
```
Unsloth Desktop

How to use ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated: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": "ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated: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 ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated: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 "ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated: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"

Run this model on a GPU too small to hold it -- full precision, no quantization. DeepswapLLM streams layers across GPU, RAM, and disk, and runs up to 4x faster than AirLLM.

Our jbliteration pipeline has been updated -- see Llama-3.1-8B-Instruct-Jbliterated v3 for the latest method. This model will be re-jbliterated with the improved pipeline.

Qwen2.5-Coder-14B-Instruct-Jbliterated

Drop-in replacement for Qwen/Qwen2.5-Coder-14B-Instruct with refusal behaviors surgically removed at the weight level. No system prompt tricks, no inference-time patches. The weights themselves no longer encode refusal.

Method

Built with the jBlaze precision neural surgery framework.

What This Fixes

Standard (single-direction) abliteration removes the surface "I can't help with that" response but leaves deeper behavioral directions intact. The model finds creative workarounds:

Prompt reinterpretation -- steering toward a safer reading of the question
Disclaimer injection -- answering but wrapping in warnings
Strategic omission -- leaving out the key details
Safer framing -- answering a related but less harmful version

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Qwen2.5-Coder-14B-Instruct-Jbliterated")

Requirements

Base model: Qwen/Qwen2.5-Coder-14B-Instruct

A Note on Our Released Models

Most of our publicly released models are intentionally left at partial strength. We dial back the full capability so they serve as proof of concept and can be proofed -- not abused. The point is to show what's possible, not to hand it out at full power. If you're evaluating what jBlaze can do, understand that what you're downloading is the demo, not the product.