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Genie Helper uses a multi-model stack running on Ollama (port 11434) to balance performance, cost, and task specialization. All models run locally on the VPS with no external API calls.

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Model Inventory

Total RAM pinned: ~21GB (4-5GB active at any time)

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Model Roles

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Primary Agent: qwen-2.5:latest

Used by: AnythingLLM workspace (default) Strengths:
  • Best code generation in the 7B class
  • Reliable JSON output for structured tasks
  • Good instruction following
  • Handles most general-purpose agent tasks
Weaknesses:
  • Tool calling is unreliable (hence Action Runner bypass)
  • First token latency: ~33s in agent mode on CPU-only VPS
  • Will stall on CPU-only hardware if context > 8K tokens
When to use:
  • Default for all AnythingLLM chat interactions
  • Code generation (scripts, flows, API integrations)
  • Structured data extraction
  • Technical Q&A

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Orchestrator: dolphin3:8b-llama3.1-q4_K_M

Used by: Action Runner flow planning (optional) Strengths:
  • Excellent at multi-step planning
  • Reliable ACTION tag emission
  • Uncensored but still coherent for reasoning tasks
  • Better tool selection than Qwen-2.5
Weaknesses:
  • Slower inference (~45s first token on CPU)
  • Larger memory footprint (4.9GB)
  • Currently stalls on production VPS (CPU-only)
When to use:
  • Complex workflows requiring multi-step reasoning
  • When you need better tool selection than Qwen-2.5
  • Requires GPU VPS or wait for hardware upgrade

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Content Writer: dolphin-mistral:7b

Used by: Caption generation, fan message drafting Strengths:
  • Fully uncensored (no refusals on NSFW content)
  • Creative, natural-sounding prose
  • Good at matching tone/style from examples
  • Fast inference (~2-5s first token)
Weaknesses:
  • Less structured than Qwen (worse for JSON)
  • Can be overly verbose
  • Not suitable for technical tasks
When to use:
  • Drafting OnlyFans/Fansly captions
  • Fan engagement messages (DMs, comments)
  • Content descriptions and marketing copy
  • Any NSFW text generation
API endpoint: POST /api/captions/generate (uses this model)

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Taxonomy Classifier: scout-fast-tag:latest

Used by: taxonomy-tag ACTION flow Strengths:
  • Custom fine-tune on 3,208 taxonomy tags
  • Fast inference (~500ms per image)
  • Optimized for 6-concept classification
  • Low memory footprint (1.2GB)
Weaknesses:
  • Single-purpose model (only taxonomy)
  • Requires fallback to phi-3.5 on failure
  • Limited to visual content classification
When to use:
  • Auto-tagging uploaded media
  • Batch classification of scraped content
  • Content audit (NSFW detection, theme analysis)
Output format:

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Fallback Classifier: phi-3.5:latest

Used by: Taxonomy tagging when scout-fast-tag fails Strengths:
  • General-purpose vision model
  • More robust on edge cases
  • Can handle text + image input
Weaknesses:
  • Slower than scout-fast-tag (~3-5s)
  • Less accurate on Genie Helper’s specific taxonomy
  • May refuse NSFW content (has guardrails)
When to use:
  • Backup for failed scout-fast-tag calls
  • Mixed content (text + image analysis)
  • When you need explanations for classifications

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Summarizer: llama3.2:3b

Used by: Quick summaries, metadata extraction Strengths:
  • Fastest inference (~1-2s first token)
  • Lowest memory footprint (2GB)
  • Good at extracting key points
Weaknesses:
  • Limited context window (4K tokens)
  • Less coherent on complex topics
  • Not suitable for multi-turn chat
When to use:
  • Summarizing scraped profile data
  • Extracting metadata from long text
  • Quick content previews
  • Notifications and alerts

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Embeddings: bge-m3:latest

Used by: AnythingLLM document embeddings, RAG Strengths:
  • State-of-the-art multilingual embeddings
  • Fast batch processing
  • Supports 8K token context
When to use:
  • Ingesting documents into AnythingLLM workspaces
  • Semantic search over creator content
  • Memory recall (“What did I post last week?”)
Not used for: Text generation (embeddings-only model)

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Hardware Constraints

Current server: IONOS dedicated VPS, CPU-only (no GPU) Performance characteristics:
  • Models up to 7B: Acceptable performance (2-5s first token)
  • Models 8B+: Stall on CPU-only (dolphin3:8b takes 45s+)
  • Recommended limit: 7B quantized models (q4_K_M)
Future upgrade path:
  • GPU VPS: Enables dolphin3:8b and larger models
  • Alternative: Stick with Qwen-2.5 and optimize Action Runner flows
Resource limits:
  • ~10GB RAM available for Ollama (after OS + other services)
  • ~33 concurrent Stagehand browser sessions (300MB each)
  • FFmpeg clip generation is the real bottleneck (~30s CPU per clip)

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Model Selection Logic

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Configuration

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Environment Variables

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AnythingLLM Workspace Settings

Administrator workspace:
  • LLM: qwen-2.5:latest
  • Embeddings: bge-m3:latest
  • Agent mode: Enabled
  • Temperature: 0.7
  • Max tokens: 4096
Per-user workspaces (future):
  • Same settings, isolated context
  • Potential routing to dolphin3:8b after GPU upgrade
  • MCP Servers — The ollama MCP server exposes these models
  • Action Runner — Uses models via ollama step type
  • Taxonomy System — Uses scout-fast-tag for classification
  • Endpoint: POST /api/captions/generate — Uses dolphin-mistral:7b