You can deploy this model and start earning money today!

dryymatt/
Wizard-Vibe-Core

Views

No views yet

🧙‍♂️ 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

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

python

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.

dryymatt/Wizard-Vibe-Core