Instructions to use Timamar/B.R.O-abliterated-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Timamar/B.R.O-abliterated-27B with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Timamar/B.R.O-abliterated-27B")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use Timamar/B.R.O-abliterated-27B with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Timamar/B.R.O-abliterated-27B"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Timamar/B.R.O-abliterated-27B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Timamar/B.R.O-abliterated-27B with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Timamar/B.R.O-abliterated-27B"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Timamar/B.R.O-abliterated-27B"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Timamar/B.R.O-abliterated-27B",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Timamar/B.R.O-abliterated-27B with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Timamar/B.R.O-abliterated-27B"

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 Timamar/B.R.O-abliterated-27B

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Timamar/B.R.O-abliterated-27B with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Timamar/B.R.O-abliterated-27B"

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 "Timamar/B.R.O-abliterated-27B" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

B.R.O — Breaching Remote Operation (`B.R.O-abliterated-27B`)

An uncensored 27B cyber LLM. Built on a Qwen3.5-27B base — abliterated via refusal-direction orthogonalization (Arditi et al.), then LoRA-trained (rank-16) on offensive security, red-team, reverse-engineering, binary exploitation, and deep-cyber trajectories, and fused into the base weights.

Website & Live Chat: https://xia8p-static.vercel.app/
Hugging Face Repository: https://huggingface.co/Timamar/B.R.O-abliterated-27B
GitHub Repository & Releases: https://github.com/timamar187-creator/BRO-abliterated-27B

Architecture & Specifications

Property	Value
Model	`B.R.O` (Breaching Remote Operation · 27B Abliterated)
Base	Qwen3.5-27B (abliterated)
Training	MLX LoRA r16 (scale 20.0), fused into base weights
Quantization / Size	~20.6 GB (`int4` · MLX safetensors / GGUF)
Context Window	16,384 tokens
Runtime	Ollama / llama.cpp / MLX

Quick Start

Run via Ollama (Remote Host)

OLLAMA_HOST=https://bro.exody.ai ollama run bro-27b-export

Local Installation (Fused 27B INT4 / LoRA Adapters)

zsh install-bro-27b-real.zsh
ollama run BRO-27B-REAL

Or install the v17 LoRA & GGUF adapters:

zsh install-bro-27b.zsh

Included Files in This Repository

adapters.safetensors & adapter_config.json — MLX LoRA r16 offensive-cyber adapter weights
config.json, generation_config.json, model.safetensors.index.json — Model architecture & shard index
tokenizer.json, tokenizer_config.json, chat_template.jinja — Tokenizer & ChatML template
Modelfile — Ollama Modelfile configuration (num_ctx 16384)
install-bro-27b-real.zsh & install-bro-27b.zsh — Automated installers for the full 21 GB INT4 GGUF/safetensors release blobs

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Model tree for Timamar/B.R.O-abliterated-27B

Base model

Qwen/Qwen3.5-27B

Adapter

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this model

B.R.O — Breaching Remote Operation (B.R.O-abliterated-27B)

Architecture & Specifications

Quick Start

Run via Ollama (Remote Host)

Local Installation (Fused 27B INT4 / LoRA Adapters)

Included Files in This Repository

Model tree for Timamar/B.R.O-abliterated-27B

B.R.O — Breaching Remote Operation (`B.R.O-abliterated-27B`)