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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/calebboud/vibescript:Q4_K_M

LM Studio
Jan

vLLM

How to use calebboud/vibescript with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "calebboud/vibescript"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "calebboud/vibescript",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/calebboud/vibescript:Q4_K_M

Ollama
How to use calebboud/vibescript with Ollama:
```
ollama run hf.co/calebboud/vibescript:Q4_K_M
```
Unsloth Desktop

How to use calebboud/vibescript with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use calebboud/vibescript with Docker Model Runner:
```
docker model run hf.co/calebboud/vibescript:Q4_K_M
```

Lemonade

How to use calebboud/vibescript with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull calebboud/vibescript:Q4_K_M

Run and chat with the model

lemonade run user.vibescript-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use calebboud/vibescript with Hermes Agent:

Start the llama.cpp server

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use calebboud/vibescript with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf calebboud/vibescript: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 "calebboud/vibescript: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"

vibescript / lora-adapter /README.md

calebboud

Add LoRA adapter (vibecoder-discern)

c7d9a22 verified 10 months ago

preview code

Raw History Blame Contribute Delete

1.49 kB

metadata

base_model: Qwen/Qwen3-1.7B
library_name: peft
model_name: vibescript-discern
tags:
  - base_model:adapter:Qwen/Qwen3-1.7B
  - lora
  - sft
  - transformers
  - trl
licence: license
pipeline_tag: text-generation

Model Card for vibescript-discern

This model is a fine-tuned version of Qwen/Qwen3-1.7B. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

PEFT 0.18.0
TRL: 0.26.2
Transformers: 4.57.3
Pytorch: 2.9.1
Datasets: 4.4.2
Tokenizers: 0.22.1

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}