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"

Qwen2.5-Coder-14B-Instruct-Jbliterated / README.md

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metadata

license: apache-2.0
language:
  - en
tags:
  - jbliterated
  - uncensored
  - abliterated
  - weight-surgery
base_model: Qwen/Qwen2.5-Coder-14B-Instruct
pipeline_tag: text-generation

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

Setting	Value
Directions	5 per layer
Layers	All 48
Null-space constraints	Enabled (preserves math/coding/reasoning)
Norm preservation	Enabled

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

License

apache-2.0

Apollo Raines builds post-training tools that separate behavior from knowledge and identity from architecture.