mcp server

neuralforge

Local AI workstation dashboard Neuralforge— manage LLMs, agents, RAG, Telegram AI bot with 14 meme personas & voice cloning, image/video/3D generation, and LoRA fine-tuning and SMM module from a singl

DefinitelyN0tMe/neuralforgecommunitylistedunreviewed

Use tool

Set up

Runs on your computer. Pick your agent and operating system.

This project does not publish an install method we can turn into a config. Follow the README instead.

README

synced Oct 25, 2026

Why another AI dashboard? Because no other open-source project gives you LLM orchestration, automated SMM for 7 platforms, image/video/3D generation, RAG, fine-tuning, Telegram bot with 14 personas, voice cloning, and MCP integration for Claude — all in a single self-hosted panel with zero cloud dependencies.

Highlights

🤖 12 AI Services managed from one UI — Ollama, ComfyUI, Whisper, Qdrant, SearXNG, and more
📱 SMM AI Department — discover trends → generate posts → create images → auto-publish to Telegram, Twitter, Facebook, Instagram, Threads, LinkedIn, Discord simultaneously
🧠 Multi-Agent System — 13 roles, 3 modes (Solo/Team/Orchestrator), 9 tools including web search, code execution, RAG
🎨 Full Generation Pipeline — Image (FLUX) → Video (Wan2.2) → 3D (Hunyuan3D) with smart VRAM management
📊 84 API Endpoints — everything is programmable, extensible, and automatable
🔒 100% Local — your data never leaves your machine. No API keys required for core features

What is this? · AI Model Stack · Features · Dashboard · Agents · RAG · LoRA · Pipeline · Telegram Bot · SMM · MCP Server · Quick Start · Requirements · FAQ

What is this?

A self-hosted web panel (localhost:9000) that unifies your entire local AI infrastructure into one powerful dashboard. No subscriptions, no cloud APIs, no data leaving your machine.

┌──────────────────────────────── NeuralForge ──────────────────────────────────┐
│                                                                                │
│  Dashboard     Agents       RAG        Telegram     LoRA        SMM           │
│  ┌────────┐   ┌────────┐  ┌────────┐  ┌────────┐  ┌────────┐  ┌────────┐    │
│  │GPU/VRAM│   │13 Roles│  │Qdrant +│  │14 Meme │  │Unsloth │  │7 Socials│   │
│  │Services│   │Solo    │  │ONNX GPU│  │Personas│  │LoRA    │  │Trend AI │   │
│  │Metrics │   │Team    │  │1800/sec│  │Voice   │  │16 base │  │Post Gen │   │
│  │Alerts  │   │Orchestr│  │Multi-DB│  │Cloning │  │models  │  │Analytics│   │
│  └────────┘   └────────┘  └────────┘  └────────┘  └────────┘  └────────┘    │
│                                                                                │
│  Pipeline: Image ──→ Video ──→ 3D   │   MCP Server: 24 tools for Claude      │
│  (ComfyUI)  (Wan2GP)  (Hunyuan3D)   │   + Music, TTS, STT, Search...        │
└────────────────────────────────────────────────────────────────────────────────┘

AI Model Stack

Every model runs locally via Ollama — no API keys, no cloud, no subscriptions.

LLMs (Text Generation & Reasoning)

Model	Size	VRAM	Used for
Qwen 3.6	35B (A3B MoE)	~22 GB	Primary workhorse — agents, RAG, planner
Qwen 3.6 / 3.8	27B	~17 GB	SMM posts & trend analysis, tutor, vision
Qwen3-Coder	30B (A3B MoE)	~18 GB	Coder, tester, security auditor roles
DeepSeek R1	32B / 14B	~19 / 9 GB	Analyst & trade-analyst reasoning
Gemma 4	26B	~17 GB	Translator — multilingual
Mistral Small	24B	~14 GB	Summarization
Qwen 3.5	9B	~7 GB	Telegram bot — fast persona responses

Every model dropdown lists whatever is installed in Ollama — pull a model and it appears, delete one and it disappears. If a configured model is missing, model_resolver.py automatically substitutes the closest installed model of the same kind (general / light / code / reasoning / vision).

Vision (Image Understanding)

Model	Size	VRAM	Used for
MiniCPM-V	8B	~5 GB	Telegram bot photo analysis — describes images, answers questions about photos sent to your account
Qwen3-VL	8B	~6 GB	Vision fallback, GUI/video understanding
Qwen 3.8	27B	~17 GB	Team/orchestrator image analysis

Embeddings (RAG Search)

Model	Size	Speed	Used for
bge-m3 (ONNX)	560M	1,800 docs/sec	GPU-accelerated document indexing
bge-m3 (Ollama)	560M	10 docs/sec	Fallback CPU embedding

Audio (Speech & Music)

Model	Size	VRAM	Used for
Whisper (faster-whisper)	base/large	2-10 GB	Speech-to-text, 99 languages, diarization
Qwen3-TTS	~4 GB	~4 GB	Text-to-speech, 3-second voice cloning
ACE-Step 1.5	~4 GB	4-6 GB	AI music generation — lyrics + style → full song

Image / Video / 3D Generation

Model	Engine	VRAM	Used for
FLUX Klein	ComfyUI	8-12 GB	Image generation (SMM posts, pipeline)
Wan 2.2	Wan2GP	12-24 GB	Video generation from image + prompt
Hunyuan3D v2	Gradio	13-20 GB	3D model generation from image

LoRA Fine-Tuning (16 base models)

Model	Size	Training time
NVIDIA Nemotron 3 Nano	4B	~30 min
Llama 3.1 / 3.2	1B–8B	30 min – 2h
Qwen 2.5	7B / 32B	1–4h
Gemma 2	2B–27B	30 min – 3h
Mistral v0.3	7B	~1h
Phi 3.5	3.8B	~40 min

Total unique AI models available: 30+ — all running locally, swappable per task, with automatic VRAM management.

Features at a Glance

	Feature	Description
GPU	Smart VRAM Management	Exclusive groups auto-stop conflicting services. Never OOM again
Dashboard	Real-time Monitoring	GPU temp, VRAM, RAM, CPU, disk — live metrics with health alerts
Agents	Multi-Agent Orchestration	13 roles, 3 modes (Solo/Team/Orchestrator), shared memory, RAG tools
RAG	Vector Search at GPU Speed	ONNX embeddings at 1,800 docs/sec, Qdrant DB, multi-collection search
Bot	14 Telegram Personas	Each with unique personality — from Philosopher to Crypto Maniac
Voice	Real-time Voice Cloning	Send voice → get reply in your own voice with AI-generated text
LoRA	Fine-Tuning UI	16 base models, dataset upload, live training output, adapter export
Gen	Image→Video→3D Pipeline	Automated chain with smart VRAM switching between steps
MCP	Claude Code Integration	24 tools — let Claude manage your entire AI stack
SMM	7-Platform Social Media	Trend Scout → AI Post Writer → Image Gen → Auto-Publish to all platforms
Ext	YAML Module System	Add any new service in 10 lines of YAML

Dashboard

The main hub. Everything starts here.

Live Metrics:

GPU VRAM usage with free memory indicator
GPU temperature and power draw
RAM usage with available memory
CPU load across all threads
Disk usage with free space alerts

Service Management:

Start/stop any service with one click
Exclusive GPU groups — when you start ComfyUI, Wan2GP auto-stops (and vice versa). No more VRAM crashes
Service health indicators (running/stopped/starting)
Quick actions: "Start basics", "Stop heavy", "Free VRAM"

Monitoring:

Active Ollama models with per-model VRAM usage
GPU process list (what's eating your VRAM right now)
Qdrant RAG collections with vector counts
Storage breakdown by service (ComfyUI outputs, Wan2GP videos, etc.)
Health alerts: GPU overheating, low disk, service down — all visible at a glance

YAML Module System — add any service:

name: My New Service
category: generation
start_cmd: "python3 app.py --port 7777"
port: 7777
vram_estimate: "4-8 GB"
exclusive_group: heavy_gpu    # auto-stops conflicting services

Drop it in modules/ → restart panel → it appears. That's it.

AI Agents

A full multi-agent framework built into the panel.

13 Role Presets:

Role	What it does	Default model
Researcher	Web search, source analysis, fact compilation	Qwen 3.6 35B-A3B
Analyst	Data analysis, pattern recognition, insights	DeepSeek-R1 32B
Coder	Write, debug, refactor code in any language	Qwen3-Coder 30B
Writer	Articles, reports, creative writing	Qwen 3.6 35B-A3B
Critic	Quality review, scoring, improvement suggestions	Qwen 3.6 35B-A3B
Summarizer	Condense long texts into key points	Mistral Small 24B
Translator	Multi-language translation with context	Gemma 4 26B
Email Writer	Professional emails from brief instructions	Qwen 3.6 35B-A3B
Tester	Generate test cases, find edge cases	Qwen3-Coder 30B
Trade Analyst	Market analysis, trend identification	DeepSeek-R1 32B
Tutor	Explain concepts at adjustable complexity	Qwen 3.8 27B
Security Auditor	Code/config security review, vulnerability scan	Qwen3-Coder 30B
Image Analyst	Describe and analyze images	MiniCPM-V 8B

3 Execution Modes:

Mode	How it works	Best for
Solo	Single agent with tools	Quick tasks, Q&A
Team	Agent chain — each passes context to next	Complex multi-step tasks
Orchestrator	AI creates plan → delegates to agents → reviews result (retries if score < 7)	Ambitious tasks with quality control

15 Team Presets: Pre-configured agent chains for common workflows — "Research → Analyze → Write", "Code → Test → Review", "Translate → Edit", and more.

Agent Tools:

web_search — search the internet
read_url / deep_scrape — fetch and parse web pages
run_python — execute Python code
read_file / write_file — file operations
analyze_image — vision model for images
rag_search — search your vector database
analyze_file — PDF/CSV/code analysis

Memory System:

Shared memory between agents in a team
Long-term memory with keyword tokenization and search
Context passing modes: full chain or previous-agent-only

RAG (Retrieval-Augmented Generation)

Ask questions about your documents. The AI retrieves relevant passages and answers with citations.

Performance:

Method	Speed	GPU VRAM
ONNX GPU (bge-m3)	1,800 texts/sec	~2 GB
Ollama embeddings	10 texts/sec	~4 GB

That's 180x faster indexing with ONNX.

Capabilities:

Multi-format indexing — PDF, TXT, MD, DOCX, CSV, HTML
Batch processing — index entire directories recursively
Multi-collection — separate databases for different topics (e.g., "laws", "docs", "codebase")
Smart search — auto-detects which collection to search based on query
Context memory — remembers previous Q&A in the same chat session
Embedding cache — repeat queries are instant

Built-in chat interface:

Markdown rendering with syntax highlighting
Copy button on every response
Export conversation to Markdown file
Collection selector and search settings
localStorage persistence — your chat survives page reload

Example use case:

Indexed all 390 Estonian laws (52,314 vectors) — now ask legal questions in any language and get answers with article references.

LoRA Fine-Tuning

Train custom model adapters directly from the panel UI.

16 Base Models Ready to Fine-Tune:

Model	Size	Notes
Llama 3.1	8B	Great all-rounder
Llama 3.2	1B / 3B	Lightweight, fast
Mistral v0.3	7B	Strong reasoning
Qwen 2.5	7B / 32B	Multilingual
Gemma 2	2B / 9B / 27B	Google's latest
Phi 3.5	3.8B	Microsoft, compact
+ custom	any	Enter any Unsloth-compatible model ID

Training UI Features:

Dataset upload (JSON, JSONL, CSV) or HuggingFace dataset ID
Auto-format detection (instruction/output, messages, or raw text)
Configurable: LoRA rank, alpha, epochs, batch size, learning rate, max sequence length
Live training output — see loss, progress, ETA in real-time
Timer showing elapsed training time
Stop button to cancel mid-training
Trained adapters listed with size and date

Powered by Unsloth — 2x faster training, 60% less memory than standard LoRA.

Generation Pipeline

Automated Image → Video → 3D chain with smart VRAM management between steps.

Step	Engine	VRAM	Automation
Image	ComfyUI (FLUX Klein 4B)	8-12 GB	Fully automated API
Video	Wan2GP (Wan 2.2 / LTX)	12-24 GB	Gradio API + manual fallback
3D	Hunyuan3D	13-20 GB	Gradio API + manual fallback

VRAM is automatically freed between steps — only one heavy service runs at a time.

# 5 built-in examples
python3 pipeline.py --example robot     # chibi robot → animate → 3D model
python3 pipeline.py --example dragon    # crystal dragon → animate → 3D
python3 pipeline.py --example car       # cyberpunk car → animate → 3D
python3 pipeline.py --example cat       # cat astronaut → animate → 3D
python3 pipeline.py --example sword     # magic sword → 3D (skip video)

# Custom prompt
python3 pipeline.py "a golden crown with gems" --steps image,3d
python3 pipeline.py "a phoenix" --video-prompt "spreads wings and flies"

Telegram AI Bot

Not a Telegram bot — responds from your own account via Telethon User API.

14 Unique Personas:

	Persona	Style
🧘	Philosopher	"You wrote 'hi', but what is a greeting if not a scream of loneliness into the void?"
🧢	Street Philosopher	"bro, your argument is logically inconsistent, purely by Kant"
👾	IT Demon	"segfault in your logic, recompile that thought"
👵	Granny from 2077	"sweetie, browsing without a firewall again? you'll catch a virus!"
🕵️	Noir Detective	"The message came at 3am. Like all bad news in this city"
🏴‍☠️	Nerd Pirate	"arrr, your meme is a true treasure!"
🐱	Cat Overlord	"I'd help, but I need to lie down for 14 more hours"
🔺	Conspiracy Nut	"Telegram was created by Masons to track memes"
🎭	Budget Shakespeare	"To be online or not to be — that is the question!"
🧟	Polite Zombie	"good evening, could you... share some brains?"
📋	Corporate Robot	"let's sync on this in the next sprint"
🫎	Capybara	"why stress when you can just... not" + random capybara photo
🚀	Crypto Maniac	"RED CANDLE, I'M BANKRUPT, wait... GREEN! I'M RICH!"
🛠️	Custom	Write your own character

Voice Clone Pipeline:

🎤 Voice in → ffmpeg (OGG→WAV) → Whisper STT → LLM response
  → unload LLM → Qwen3-TTS (clone voice) → ffmpeg (WAV→OGG) → 🔊 Voice out

Vision — Photo Analysis (MiniCPM-V 8B):

📸 Photo in → MiniCPM-V (image description) → LLM (persona-styled response) → 💬 Reply

Send a photo to your account → the bot describes it through the vision model → responds in character. Toggle on/off from panel UI.

Features:

Vision mode — understands photos via MiniCPM-V 8B (auto VRAM swap: unload LLM → load vision → analyze → unload → reload LLM)
Auto-detects language → responds in same language
Conversation memory (5 exchanges per user)
Session-based logs grouped by contact
Voice clone toggle from panel UI
Capybara persona sends random capybara photos via capy.lol API

SMM AI Department

Fully automated social media management system — from trend discovery to publishing across 7 platforms.

Complete Workflow:

Trend Scout → Post Writer → Image Gen → Content Queue → Auto-Publish
    │              │             │              │              │
    ▼              ▼             ▼              ▼              ▼
 6 Sources     2-Pass LLM    ComfyUI FLUX   Schedule +     7 Platforms
 (Reddit,HN,  (Scrape→       + ffmpeg      Calendar       simultaneously
  GitHub,RSS,  Summary→       resize        view           with retry
  SearXNG,     Platform
  GoogTrends)  posts)

7 Connected Platforms:

Platform	Auth Method	Features
Telegram	Bot API	Text + Photo, channel posting
Discord	Webhook	Text + File upload
Twitter/X	OAuth 1.0a	Text + Media upload (Pay-Per-Use)
Facebook	Page Token (permanent)	Text + Photo, Page posting
Instagram	Graph API via FB	Photo + Caption (via imgur)
Threads	Threads API	Text + Image
LinkedIn	OAuth 2.0	Text + Image (3-step upload)

Key Features:

Trend Scout v2 — Multi-source intelligence with niche routing (tech, crypto, food, fitness, art, gaming, education, business), geo-detection, CJK filtering
GitHub Trending — Hybrid search (API + trending page scrape), categories (Agent/LLM/RAG/Tool), velocity ranking, "already posted" markers
Post Writer — 2-pass generation: scrapes source article → LLM summary → platform-specific posts with correct tone/length/hashtags. Custom context support
Image Generation — AI-generated prompts → ComfyUI FLUX Klein → auto-resize for each platform. ComfyUI auto-starts and stops (VRAM management)
Content Queue — SQLite-backed, edit/duplicate/regenerate posts, schedule with date/time picker, auto-publish via background scheduler
Content Calendar — Weekly view with navigation, color-coded by status
Batch Generation — Generate N days of content in one click with auto-scheduling
Analytics — Metrics collection from FB/IG/Threads/LinkedIn APIs, per-platform breakdown, top posts ranking
Token Health — Auto-refresh for expiring tokens (Threads, LinkedIn), dashboard monitoring
Hashtag Manager — Per-platform limits (Instagram 28, Twitter 3, Discord 0), auto-trim at publish
Publish Preview — Review all posts + image before publishing with platform selection

MCP Server — Claude Code Integration

24 tools that let Claude Code directly manage your AI infrastructure:

Category	Tools
System	`get_system_status` `get_gpu_processes` `ollama_loaded_models` `check_health`
Services	`start_service` `stop_service` `stop_all_and_free_vram`
RAG	`rag_search` `rag_list_collections` `rag_index_file` `rag_index_directory` `ask_rag`
Agents	`run_agent` `run_agent_team` `run_orchestrator`
Generate	`generate_image` `run_pipeline`
Fine-tune	`finetune_start` `finetune_status` `finetune_stop`
Utils	`get_storage_info` `cleanup_storage` `run_backup` `convert_audio`

// Add to your project's .mcp.json
{ "mcpServers": { "neuralforge": { "command": "/path/to/neuralforge/run_mcp.sh" } } }

Now Claude can: check GPU status, start services, search your RAG database, run agent teams, generate images, manage fine-tuning — all from natural language.

Quick Start

1. Clone & Install

git clone https://github.com/DefinitelyN0tMe/neuralforge.git
cd neuralforge
chmod +x install.sh
./install.sh

2. Get an LLM running

curl -fsSL https://ollama.com/install.sh | sh

# Pick a model for your VRAM
ollama pull qwen3.6:35b-a3b      # 22GB VRAM — fast MoE workhorse
ollama pull qwen3.6:27b          # 17GB VRAM — dense, high quality
ollama pull qwen3.5:9b           # 7GB VRAM — lightweight
ollama pull bge-m3               # embeddings for RAG

# Every model dropdown in the panel lists what is installed in Ollama,
# so newly pulled models show up automatically.

3. Start support services

# Qdrant for RAG (optional)
docker run -d --name qdrant -p 6333:6333 -v qdrant_data:/qdrant/storage qdrant/qdrant

4. Open the panel

http://localhost:9000

5. Set up Telegram Bot (optional)

The Telegram auto-responder answers private messages from your own account (Telethon user API, not a BotFather bot). It runs entirely from the panel UI — no code editing required.

Get your Telegram API ID and API Hash from my.telegram.org → API development tools
In the panel → Telegram tab → API Credentials → paste them → Save
In the Telegram account card → enter your phone → Send code → enter the code (and your 2FA password if you have one) → Log in
Click Enable. If Telegram ever revokes the session, the panel shows "Not logged in" — just log in again
The bot comes with 14 pre-configured personas (Philosopher, Crypto Maniac, etc.) — customize or create your own

6. Set up SMM Publishing (optional)

Publish AI-generated posts to 7 social networks. All configuration is done through the panel UI.

In the panel → SMM tab → + New Profile → follow the 4-step wizard
On Step 2 (Platforms), each platform has built-in setup instructions:
- Telegram: Create bot via @BotFather → paste token + channel
- Discord: Server Settings → Integrations → Webhooks → copy URL
- Twitter/X: developer.x.com → Create App → OAuth 1.0a keys
- Facebook: developers.facebook.com → Create App (Business) → Page Token
- Instagram: Same Meta app → connect IG Business Account to FB Page
- Threads: Meta Developer App → Threads API → authorize
- LinkedIn: linkedin.com/developers → Create App → OAuth 2.0
Scan trends → Generate posts → Publish to all platforms with one click

Tip: Start with just Telegram + Discord (easiest, no API keys needed beyond bot token/webhook). Add other platforms later.

Requirements

Component	Minimum	Recommended	Tested on
GPU	NVIDIA 12GB VRAM	24GB VRAM	RTX 3090 24GB
RAM	16 GB	64+ GB	128 GB DDR4
Disk	50 GB free	200+ GB	2TB NVMe
CPU	4 cores	16+ cores	Threadripper PRO 5955WX
OS	Ubuntu 22.04	Ubuntu 24.04	Ubuntu 24.04.2
Python	3.10	3.12	3.12.3

Also needed: NVIDIA drivers, CUDA, Docker, ffmpeg, Ollama

Architecture

Browser ◄──────► FastAPI server.py :9000 ◄──────► Ollama :11434
                      │                              (LLM inference)
                      ├──► Module Manager
                      │     ├── ComfyUI :8188        (image gen)
                      │     ├── Wan2GP :7860          (video gen)
                      │     ├── Hunyuan3D :7870       (3D gen)
                      │     ├── ACE-Step :7880        (music gen)
                      │     ├── Qwen3-TTS :7890       (voice clone)
                      │     ├── Whisper :7895          (speech-to-text)
                      │     └── ... (add your own via YAML)
                      │
                      ├──► Qdrant :6333               (vector DB for RAG)
                      ├──► Telegram Bot               (Telethon user API)
                      └──► MCP Server                 (Claude Code bridge)

telegram_bot.py ◄──► Ollama (text) + Whisper (STT) + Qwen3-TTS (voice clone)
pipeline.py     ◄──► ComfyUI → Wan2GP → Hunyuan3D (sequential, VRAM-managed)
mcp_server.py   ◄──► server.py API (24 tools exposed to Claude Code)

Project Structure

neuralforge/
├── server.py                  # FastAPI backend (54 endpoints + WebSocket)
├── model_resolver.py          # Installed-model lookup + closest-model fallback
├── metrics.py                 # Observability: LLM calls, GPU & service samples
├── telegram_bot.py            # Telegram bot — 14 personas, voice clone, vision
├── pipeline.py                # Image → Video → 3D generation pipeline
├── mcp_server.py              # MCP server — 24 tools for Claude Code
├── smm/                       # SMM AI Department (modular package)
│   ├── __init__.py            # Router registration
│   ├── routes.py              # 30 SMM routes + scheduler + publishing
│   └── db.py                  # SQLite: queue, trends, analytics
├── templates/
│   └── index.html             # Single-page frontend (vanilla JS, no framework)
├── modules/                   # YAML service definitions (drop-in)
│   ├── ollama.yaml            # LLM inference
│   ├── comfyui.yaml           # Image generation
│   ├── wan2gp.yaml            # Video generation
│   ├── hunyuan3d.yaml         # 3D model generation
│   ├── ace-step.yaml          # Music generation
│   ├── qwen3-tts.yaml         # Text-to-speech + voice cloning
│   ├── whisper-webui.yaml     # Speech recognition
│   ├── reranker.yaml          # Qwen3-Reranker for RAG (autostart)
│   └── ...                    # add your own!
├── install.sh                 # Automated installer with path patching
├── requirements.txt           # Python dependencies
├── run_mcp.sh                 # MCP server launcher
├── scripts/sync_app_models.py # Check/fix Open WebUI & Perplexica model settings
├── docs/                      # PDF guide generators
├── backup.sh                  # Backup script
├── telegram_config.example.json
├── LICENSE
└── README.md

FAQ

Contributing

PRs welcome. The codebase is intentionally simple — vanilla JS frontend, single FastAPI backend, no build step.

Good first contributions:

New Telegram personas
New module YAML definitions
UI improvements
Automated Gradio API for Wan2GP / Hunyuan3D
Documentation / translations

License

MIT — do whatever you want with it.

Credits

Project	Used for
Ollama	Local LLM inference (Qwen, Mistral, Gemma, DeepSeek, Nemotron)
FastAPI	Backend API (69 endpoints)
Telethon	Telegram User API
Qdrant	Vector database for RAG
bge-m3	Multilingual embeddings (ONNX GPU-accelerated)
MiniCPM-V	Vision model — Telegram photo analysis
ComfyUI	Image generation (FLUX Klein)
Wan2GP	Video generation (Wan 2.2, LTX-Video)
Hunyuan3D	3D model generation
faster-whisper	Speech recognition (99 languages)
Qwen3-TTS	Text-to-speech & 3-second voice cloning
ACE-Step	Music generation
Unsloth	LoRA fine-tuning (2x faster, 60% less memory)
SearXNG	Privacy-first meta-search for AI agents
Perplexica	AI-powered search engine
Open WebUI	Chat interface for LLM models