dryymatt/Wizard-Vibe-Core

🧙‍♂️ Wizard-Vibe Core

Sandbox-First Architecture — single-file SSE streaming code generator with Reflect-Select self-healing and A2A native deploy.

Quick Start

chmod +x sandbox.sh && ./sandbox.sh

Or with hot-reload:

HOT_RELOAD=1 ./sandbox.sh

Architecture

core.py          ← Single-file: SSE server + orchestrator + self-heal + deploy
hot_reload.py    ← File watcher for dev mode (auto-restart on changes)
static/          ← Liquid Glass UI (minimalist HTML/CSS/JS)
sandbox.sh       ← One-command bootstrap
Dockerfile       ← Containerized deployment

API

Endpoint	Method	Description
/	GET	Liquid Glass UI
/api/health	GET	Health check
/api/stream	POST	SSE streaming code generation
/api/publish	POST	GitHub + A2A deploy
/api/status?session_id=X	GET	Session status
/api/preview?session_id=X	GET	Sandbox iframe content
/.well-known/agent.json	GET	A2A agent card

Models

• Vision/UI → microsoft/Phi-3-vision-128k-instruct
• Logic/Backend → deepseek-ai/DeepSeek-V3-0324
• Code/Infra → Qwen/Qwen3-Coder-30B-A3B-Instruct
• Fallback → mistralai/Mistral-7B-Instruct-v0.3

Links

• HF Repo: https://huggingface.co/dryymatt/Wizard-Vibe-Core
• HF Space: https://dryymatt-wizard-vibe-core.hf.space
• GitHub Mirror: https://github.com/M523zappin/Wizard-Vibe-Core

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Built with the Reflect-Select self-healing architecture. Every line validated in sandbox before publish.

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'dryymatt/Wizard-Vibe-Core'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.