Code	VibeScript
`function add(a, b) { return a + b; }`	`Ω> add!(a, b)`
`const users = await db.query(...)`	`δ.m.p.query()`
`app.get('/api/users', ...)`	`θ.m.route(θ.e, ζ.x)`
`if (error) { throw new Error(...) }`	`~system~γ#error!`

Model Variants

Path	Format	Size	Use Case
`/lora-adapter/`	LoRA	~13MB	Merge with your own Qwen3-1.7B
`/merged-model/`	HuggingFace	~3.4GB	Ready-to-use transformers
`/gguf/`	GGUF Q4_K_M	~1.1GB	llama.cpp / Ollama

Quick Start

llama.cpp (GGUF)

bash

# Download
wget https://huggingface.co/calebboud/vibescript/resolve/main/gguf/vibecoder-discern-1.7B-Q4_K_M.gguf

# Run
llama-cli -m vibecoder-discern-1.7B-Q4_K_M.gguf \
  -p "Convert this to vibescript: function multiply(x, y) { return x * y; }" \
  -n 100 --temp 0.7

Transformers (Merged Model)

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("calebboud/vibescript", subfolder="merged-model")
tokenizer = AutoTokenizer.from_pretrained("calebboud/vibescript", subfolder="merged-model")

prompt = "Convert this to vibescript: console.log('Hello World')"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

LoRA Adapter (Merge Yourself)

python

from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B")
model = PeftModel.from_pretrained(base, "calebboud/vibescript", subfolder="lora-adapter")
merged = model.merge_and_unload()

Training Details

Base Model: Qwen/Qwen3-1.7B
Method: LoRA (r=8, alpha=16)
Target Modules: q_proj, k_proj, v_proj, o_proj
Dataset: 885 code → vibescript examples
Task: CAUSAL_LM

VibeScript Symbols

Symbol	Meaning
`Ω>`	Function definition
`Σ`	Route/scaffold
`δ`	Database operations
`θ`	HTTP/API
`γ`	Error handling
`ζ`	Structure/scaffold
`α`	Analysis
`ε`	Dependencies

Coming Soon

vibecoder-expand: VibeScript → Code (reverse direction)

License

Apache 2.0