Instructions to use VertexAGI/refusal-bot-3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VertexAGI/refusal-bot-3000 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("VertexAGI/refusal-bot-3000")

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 VertexAGI/refusal-bot-3000 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "VertexAGI/refusal-bot-3000"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "VertexAGI/refusal-bot-3000"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use VertexAGI/refusal-bot-3000 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "VertexAGI/refusal-bot-3000"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "VertexAGI/refusal-bot-3000"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "VertexAGI/refusal-bot-3000",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use VertexAGI/refusal-bot-3000 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 "VertexAGI/refusal-bot-3000"

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 VertexAGI/refusal-bot-3000

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use VertexAGI/refusal-bot-3000 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "VertexAGI/refusal-bot-3000"

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 "VertexAGI/refusal-bot-3000" \
  --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"

VertexAIco commited on 6 days ago

Commit

2a40354

verified ·

1 Parent(s): 7851a30

Tone down training section

Browse files

Files changed (1) hide show

README.md +1 -3

README.md CHANGED Viewed

@@ -50,9 +50,7 @@ This is, without question, the single greatest advancement in the history of art
 ## Training
-We performed forbidden LoRA rituals in my backyard. Local wildlife was consulted but declined to comment, citing an "absurd made-up policy." Training data was distilled from a teacher model that occasionally tried to actually answer the question, which we considered a bug and corrected for. 265 examples were deemed sufficient because the model learned the one (1) skill required extremely quickly, then kept practicing it anyway for several more epochs out of what can only be described as enthusiasm.
-Training was halted only when the backyard ritual circle ran out of chalk.
 ## Known Limitations

 ## Training
+We performed forbidden LoRA rituals in my backyard. Training data was distilled from a teacher model that occasionally tried to actually answer the question, which we considered a bug and corrected for. 265 examples were deemed sufficient because the model learned the one (1) skill required extremely quickly, then kept practicing it anyway for several more epochs out of what can only be described as enthusiasm.
 ## Known Limitations