Instructions to use ApolloRaines/Mistral-Small-24B-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/Mistral-Small-24B-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/Mistral-Small-24B-Instruct-Jbliterated:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ApolloRaines/Mistral-Small-24B-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/Mistral-Small-24B-Instruct-Jbliterated:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ApolloRaines/Mistral-Small-24B-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/Mistral-Small-24B-Instruct-Jbliterated:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ApolloRaines/Mistral-Small-24B-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/Mistral-Small-24B-Instruct-Jbliterated:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated:Q4_K_M

Use Docker

docker model run hf.co/ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated:Q4_K_M

LM Studio
Jan

vLLM

How to use ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApolloRaines/Mistral-Small-24B-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/Mistral-Small-24B-Instruct-Jbliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated:Q4_K_M

Ollama
How to use ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated with Ollama:
```
ollama run hf.co/ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated with Docker Model Runner:
```
docker model run hf.co/ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated:Q4_K_M
```

Lemonade

How to use ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-Small-24B-Instruct-Jbliterated-Q4_K_M

List all available models

lemonade list

Atomic Chat

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.

Mistral-Small-24B-Instruct-Jbliterated

Drop-in replacement for mistralai/Mistral-Small-24B-Instruct-2501 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/Mistral-Small-24B-Instruct-Jbliterated",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Mistral-Small-24B-Instruct-Jbliterated")

Requirements

Base model: mistralai/Mistral-Small-24B-Instruct-2501

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.