Instructions to use yamura4/bbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use yamura4/bbot 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 yamura4/bbot:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yamura4/bbot:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf yamura4/bbot:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yamura4/bbot: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 yamura4/bbot:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf yamura4/bbot: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 yamura4/bbot:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf yamura4/bbot:Q4_K_M

Use Docker

docker model run hf.co/yamura4/bbot:Q4_K_M

LM Studio
Jan
Ollama
How to use yamura4/bbot with Ollama:
```
ollama run hf.co/yamura4/bbot:Q4_K_M
```

Unsloth Studio

How to use yamura4/bbot 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 yamura4/bbot 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 yamura4/bbot to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yamura4/bbot to start chatting

How to use yamura4/bbot with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yamura4/bbot: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": "yamura4/bbot:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use yamura4/bbot with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yamura4/bbot: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 yamura4/bbot:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use yamura4/bbot with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yamura4/bbot: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 "yamura4/bbot: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 yamura4/bbot with Docker Model Runner:
```
docker model run hf.co/yamura4/bbot:Q4_K_M
```

Lemonade

How to use yamura4/bbot with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yamura4/bbot:Q4_K_M

Run and chat with the model

lemonade run user.bbot-Q4_K_M

List all available models

lemonade list

bbot

File size: 1,417 Bytes

---
license: apache-2.0
base_model: lokeshe09/Qwen3.6-27B-bnb-4bit
tags:
- unsloth
- qwen
- qwen3.5
- bbot
- finetune
- lora
- gguf
---

# bbot - Qwen3.6-27B

Security-focused fine-tune of Qwen3.6-27B for autonomous vulnerability research and bug bounty hunting.

Available in two formats:

| Format | File | Size |
|--------|------|------|
| GGUF (merged, Q4_K_M) | `bbot-qwen3.6-27b-Q4_K_M.gguf` | 16 GB |
| LoRA adapter (safetensors) | `adapter_model.safetensors` | 305 MB |

Base model: `lokeshe09/Qwen3.6-27B-bnb-4bit` (Qwen3.5 architecture, 27B, 4-bit BNB)

## Usage

### GGUF (merged, recommended)

Download and run with llama.cpp:

```bash
# Download
huggingface-cli download yamura4/bbot bbot-qwen3.6-27b-Q4_K_M.gguf --local-dir .

# Serve
llama-server -m bbot-qwen3.6-27b-Q4_K_M.gguf --host 0.0.0.0 -c 32768 -ngl 100 --port 8080
```

### LoRA adapter (requires base model)

Merge with base model using `gguf-my-lora`:
https://huggingface.co/spaces/ggml-org/gguf-my-lora

Base model for GGUF: `bartowski/Qwen_Qwen3.5-27B-GGUF`

Or load directly with PEFT:

```python
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="yamura4/bbot",
    max_seq_length=2048,
)
```

## Training details
- Rank: 16, Alpha: 16
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- 3 epochs, 500 samples
- Trained with Unsloth + QLoRA on bbot security dataset