docs(local-llm): add multimodal stack, model recommendations, and troubleshooting
- docs/04-multimodal-local-stack.md: vision models (llava, qwen2.5vl, moondream2), audio pipeline architecture, video understanding status, Kimi alternatives, complete local AI stack diagram - docs/07-model-recommendations.md: 6-tier model guide (coding, fast, general, reasoning, vision, embeddings), recommended 10-model stack for M4 Pro 48GB, use-case quick reference, hardware scaling guide - docs/08-troubleshooting.md: corporate Forcepoint proxy workarounds, MLX warning, JSON parse errors, slow inference, whisper-cli vs whisper-cpp naming, audio format conversion, proxy-corrupted downloads detection
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171
__LOCAL_LLMs/docs/04-multimodal-local-stack.md
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__LOCAL_LLMs/docs/04-multimodal-local-stack.md
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# 04 — Multimodal Local Stack
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> Vision models, audio pipeline architecture, and video understanding status.
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---
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## Overview
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A fully local multimodal AI stack requires three pipelines:
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```
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Audio In → whisper.cpp (STT) → text
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Image In → vision LLM (Ollama) → text description / analysis
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Video In → frame extraction → vision LLM per frame → text
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Text → LLM (Ollama) → response
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Text Out → TTS (optional) → audio
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```
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---
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## Vision Models (Image Understanding) ✅ Available
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These run on Ollama and accept image input alongside text prompts.
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### Available on Ollama
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| Model | Size | RAM Needed | Capability |
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| ----------------- | ------ | ----------------------- | ------------------------------- |
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| `qwen2.5vl:72b` | ~45 GB | ~45 GB ⚠️ tight on 48GB | Best vision locally |
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| `llava:34b` | ~22 GB | ~22 GB | Strong image understanding, OCR |
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| `llava:13b` | ~9 GB | ~9 GB | Good balance |
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| `llava-llama3:8b` | ~6 GB | ~6 GB | LLaVA on Llama3 base |
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| `minicpm-v:8b` | ~6 GB | ~6 GB | Strong vision + OCR |
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| `qwen2.5vl:7b` | ~6 GB | ~6 GB | Qwen vision — very capable |
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| `moondream2` | ~2 GB | ~2 GB | Tiny, fast, basic vision |
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### Recommended for M4 Pro 48 GB
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- **Best quality:** `qwen2.5vl:72b` — possible but tight (~45 GB, leaves 3 GB)
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- **Safe choice:** `llava:34b` — 22 GB, leaves 26 GB free for other tools
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- **Fast:** `qwen2.5vl:7b` — 6 GB, very responsive
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### Pull & Use
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```bash
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# Pull a vision model
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ollama pull llava:34b
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# Use with an image
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ollama run llava:34b "Describe this image" --images /path/to/image.png
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```
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### Vision API
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```bash
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curl http://localhost:11434/api/generate -d '{
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"model": "llava:34b",
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"prompt": "What do you see in this image?",
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"images": ["<base64-encoded-image>"]
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}'
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```
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---
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## Audio Pipeline ✅ Available (via Whisper.cpp)
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No Ollama models handle audio input natively. Audio requires a separate pipeline:
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```
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Microphone → whisper.cpp (local STT) → LLM (Ollama) → optional TTS
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```
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### Components
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| Stage | Tool | Status |
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| ------------------ | --------------------------------------------- | ---------------------------- |
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| **Speech-to-Text** | whisper-cpp (`whisper-cli`, `whisper-stream`) | ✅ Installed, awaiting model |
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| **LLM Processing** | Ollama (any model) | ✅ Ready |
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| **Text-to-Speech** | kokoro / piper (local TTS) | Not installed |
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### Real-Time Voice → LLM Pipeline
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```bash
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# whisper-talk-llama does this in one binary!
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whisper-talk-llama \
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--model ~/whisper-models/ggml-large-v3-turbo.bin \
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--llama-model ~/.ollama/models/... \
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--language en
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```
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> Note: `whisper-talk-llama` is installed but requires both Whisper + LLaMA GGUF models configured.
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### Local TTS Options (Not Yet Installed)
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| Tool | Quality | Speed | Install |
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| ------------- | --------- | --------- | ------------------------ |
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| **Kokoro** | Excellent | Fast | `pip install kokoro` |
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| **Piper** | Good | Very fast | `brew install piper` |
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| **espeak-ng** | Basic | Instant | `brew install espeak-ng` |
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---
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## Video Understanding ❌ Not Available Locally
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**True video understanding is not yet available on local models.** Current state:
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- No end-to-end video model runs on Ollama
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- Workaround: Extract frames with ffmpeg → process each frame with a vision model
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- Practical for screenshots/thumbnails, not for real video understanding
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### Frame Extraction Workaround
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```bash
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# Extract 1 frame per second from video
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ffmpeg -i video.mp4 -vf "fps=1" frames/frame_%04d.jpg
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# Then process each frame with vision model
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for f in frames/*.jpg; do
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ollama run llava:34b "Describe what's happening" --images "$f"
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done
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```
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### For Real Video Understanding
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Use cloud APIs:
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- **Google Gemini** — native video input support
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- **OpenAI GPT-4o** — accepts video frames
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- **Anthropic Claude** — accepts images (frame extraction needed)
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---
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## Kimi (Moonshot AI) — Cloud Only
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Kimi models are **not available on Ollama** — they're cloud-only via the Moonshot API (`api.moonshot.cn`). No official Ollama-compatible release exists.
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### Closest Local Alternatives
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| Looking For | Local Alternative | Why Similar |
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| ------------------------ | ------------------- | ------------------------------ |
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| Kimi k1.5 (reasoning) | `deepseek-r1:32b` | Strong reasoning, long context |
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| Kimi long context (128k) | `qwen2.5:72b` | Same tier, 128k context |
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| Kimi coding | `qwen2.5-coder:32b` | Best local coding model |
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```bash
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ollama pull deepseek-r1:32b # 20 GB, reasoning-focused
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```
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---
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## Complete Local AI Stack Summary
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```
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┌─────────────────────────────────────────────────────┐
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│ Local AI Stack (M4 Pro 48GB) │
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├─────────────────────────────────────────────────────┤
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│ │
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│ Audio In ──→ whisper-cpp (STT) ──→ Text │
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│ │ │
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│ Image In ──→ llava:34b (Vision) ──→ Text │
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│ │ │
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│ Text ──────→ qwen2.5-coder:32b ──→ Response │
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│ │ │
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│ Response ──→ kokoro/piper (TTS) ──→ Audio Out │
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│ │
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│ Server: Ollama :11434 │
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│ Dashboard: Mission Control :3100 │
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│ Whisper Server: whisper-server :8080 (optional) │
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│ │
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└─────────────────────────────────────────────────────┘
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```
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__LOCAL_LLMs/docs/07-model-recommendations.md
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# 07 — Model Recommendations
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> Tiered model guide by use case, size, and quality for Apple M4 Pro with 48 GB unified memory.
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---
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## Tier 1 — Best Overall Coding Models
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| Model | Size | RAM Used | Pull Command | Notes |
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| ----------------------- | ----- | -------- | ------------------------------- | -------------------------------------------------- |
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| **`qwen2.5-coder:32b`** | 19 GB | ~22 GB | `ollama pull qwen2.5-coder:32b` | **Top pick** — rivals GPT-4o on code, 128k context |
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| `deepseek-coder-v2:16b` | 10 GB | ~12 GB | `ollama pull deepseek-coder-v2` | Best open-source coding model at 16B |
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| `codestral:22b` | 13 GB | ~15 GB | `ollama pull codestral` | Mistral's coding model, very fast completions |
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## Tier 2 — Fast & Capable (Speed/Quality Balance)
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| Model | Size | RAM Used | Pull Command | Notes |
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| ---------------------- | ---- | -------- | --------------------------------- | --------------------------------------------- |
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| **`qwen2.5-coder:7b`** | 5 GB | ~6 GB | `ollama pull qwen2.5-coder:7b` | Fast, surprisingly good for TS/Python/Swift |
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| `deepseek-coder:6.7b` | 4 GB | ~5 GB | `ollama pull deepseek-coder:6.7b` | Lightweight, solid everyday coding |
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| `codegemma:7b` | 5 GB | ~6 GB | `ollama pull codegemma:7b` | Google's model, decent but outclassed by Qwen |
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## Tier 3 — General Purpose (Also Good at Code)
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| Model | Size | RAM Used | Pull Command | Notes |
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| ------------------- | ------ | -------- | -------------------------- | ----------------------------------- |
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| `llama3.1:70b` (Q4) | 40 GB | ~42 GB | `ollama pull llama3.1:70b` | Best general model — tight on 48 GB |
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| `llama3.1:8b` | 4.9 GB | ~6 GB | `ollama pull llama3.1:8b` | Fast, good for evals |
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| `mistral-nemo:12b` | 7 GB | ~9 GB | `ollama pull mistral-nemo` | Fast reasoning |
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| `phi4:14b` | 9 GB | ~11 GB | `ollama pull phi4` | Strong reasoning, fits comfortably |
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## Tier 4 — Reasoning & Deep Thinking
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| Model | Size | RAM Used | Pull Command | Notes |
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| --------------------- | ----- | -------- | ----------------------------- | ------------------------------------------------ |
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| **`deepseek-r1:32b`** | 20 GB | ~22 GB | `ollama pull deepseek-r1:32b` | Chain-of-thought reasoning, closest to Kimi k1.5 |
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| `deepseek-r1:7b` | 5 GB | ~6 GB | `ollama pull deepseek-r1:7b` | Lightweight reasoning |
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## Tier 5 — Vision (Multimodal)
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| Model | Size | RAM Used | Pull Command | Notes |
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| -------------- | ----- | -------- | -------------------------- | ------------------------ |
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| `llava:34b` | 22 GB | ~22 GB | `ollama pull llava:34b` | Image understanding, OCR |
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| `qwen2.5vl:7b` | 6 GB | ~6 GB | `ollama pull qwen2.5vl:7b` | Qwen vision, fast |
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| `minicpm-v:8b` | 6 GB | ~6 GB | `ollama pull minicpm-v:8b` | Strong OCR |
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| `moondream2` | 2 GB | ~2 GB | `ollama pull moondream2` | Tiny, basic vision |
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## Tier 6 — Embeddings
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| Model | Size | RAM Used | Pull Command | Notes |
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| ------------------- | ------ | -------- | ------------------------------- | ------------------------- |
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| `nomic-embed-text` | 0.3 GB | ~0.5 GB | `ollama pull nomic-embed-text` | Good for semantic search |
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| `mxbai-embed-large` | 0.7 GB | ~1 GB | `ollama pull mxbai-embed-large` | Higher quality embeddings |
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---
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## Recommended 10-Model Stack for M4 Pro 48 GB
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For maximum coverage across all use cases:
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| # | Model | Disk | Use Case |
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| --- | ----------------------- | ----------- | ---------------------------------------- |
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| 1 | `qwen2.5-coder:32b` | 19 GB | **Primary** — coding (TS, Python, Swift) |
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| 2 | `qwen2.5-coder:7b` | 5 GB | Fast coding completions |
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| 3 | `deepseek-coder-v2:16b` | 10 GB | Alternative coding model |
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| 4 | `llama3.1:8b` | 4.9 GB | Eval default, general tasks |
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| 5 | `deepseek-r1:32b` | 20 GB | Deep reasoning, complex triage |
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| 6 | `codestral:22b` | 13 GB | Fast code completions (Mistral) |
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| 7 | `phi4:14b` | 9 GB | Reasoning, structured output |
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| 8 | `llava:34b` | 22 GB | Vision / image understanding |
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| 9 | `mistral-nemo:12b` | 7 GB | Fast general purpose |
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| 10 | `nomic-embed-text` | 0.3 GB | Embeddings / semantic search |
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| | **Total** | **~115 GB** | |
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Only one loads into RAM at a time. You can have all 10 on disk simultaneously.
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---
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## By Use Case (Quick Reference)
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| Use Case | Best Model | Fallback |
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| -------------------------- | ------------------- | ----------------------- |
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| **TypeScript/ESM coding** | `qwen2.5-coder:32b` | `qwen2.5-coder:7b` |
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| **Python coding** | `qwen2.5-coder:32b` | `deepseek-coder-v2:16b` |
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| **Swift/iOS coding** | `qwen2.5-coder:32b` | `codestral:22b` |
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| **Extraction evals** | `llama3.1:8b` | `qwen2.5:7b` |
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| **JSON structured output** | `qwen2.5:7b` | `qwen2.5-coder:7b` |
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| **Complex reasoning** | `deepseek-r1:32b` | `phi4:14b` |
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| **Image understanding** | `llava:34b` | `qwen2.5vl:7b` |
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| **Embeddings** | `nomic-embed-text` | `mxbai-embed-large` |
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| **Fast iteration** | `qwen2.5-coder:7b` | `llama3.1:8b` |
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---
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## Hardware Guide (General)
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For reference if running on different hardware:
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| RAM | Max Model Size | Recommendation |
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| ------ | -------------- | ------------------------------------- |
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| 8 GB | 7B | `qwen2.5-coder:7b` |
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| 16 GB | 13-16B | `deepseek-coder-v2:16b` |
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| 24 GB | 32B | `qwen2.5-coder:32b` |
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| 32 GB | 32B + headroom | `qwen2.5-coder:32b` (comfortable) |
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| 48 GB | 70B (Q4) | `llama3.1:70b` or any 32B comfortably |
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| 64 GB+ | 70B (Q8) | Full precision 70B models |
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__LOCAL_LLMs/docs/08-troubleshooting.md
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# 08 — Troubleshooting & Corporate Proxy
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> Common issues, Forcepoint proxy workarounds, MLX warnings, and fixes.
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---
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## Corporate Proxy (Forcepoint CertChecker)
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This machine is behind an AT&T Forcepoint proxy that performs SSL deep packet inspection.
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### Proxy Details
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| Setting | Value |
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| --------- | --------------------------------------------- |
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| Proxy URL | `http://cso.proxy.att.com:8080/` |
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| Agent | Forcepoint CertChecker |
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| Impact | Intercepts HTTPS, replaces TLS certificates |
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| Env vars | `HTTP_PROXY`, `HTTPS_PROXY` set automatically |
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### What Works Through Proxy
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| Tool | Status | Notes |
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| -------------------------- | ---------- | ------------------------------------- |
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| `ollama pull` | ✅ Works | Ollama handles proxy natively |
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| `brew install` | ✅ Works | Homebrew handles proxy |
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| `npm install` | ✅ Works | With `NODE_TLS_REJECT_UNAUTHORIZED=0` |
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| `curl` to Hugging Face | ❌ Blocked | Returns 19 KB HTML redirect page |
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| `curl -k` to Hugging Face | ❌ Blocked | Still intercepted even with `-k` |
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| `python requests` to HF | ❌ Blocked | SSL_CERTIFICATE_VERIFY_FAILED |
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| `huggingface_hub` download | ❌ Blocked | Falls back to cached (broken) files |
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### Workaround: Download Off-Network
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For Hugging Face model downloads (e.g., Whisper GGML files):
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1. **Disconnect** from corporate VPN/Wi-Fi
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2. **Connect** to personal hotspot or home Wi-Fi
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3. Run the download:
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```bash
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curl -L -o ~/whisper-models/ggml-large-v3-turbo.bin \
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https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3-turbo.bin
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```
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4. **Reconnect** to corporate. The model is stored locally forever.
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### Detecting a Proxy-Corrupted Download
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If a download completed but the file is suspiciously small:
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```bash
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# Check file size (should be ~1.6 GB for large-v3-turbo)
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ls -lh ~/whisper-models/ggml-large-v3-turbo.bin
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# Check file type (should NOT be HTML)
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file ~/whisper-models/ggml-large-v3-turbo.bin
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# If it says "HTML document text" — delete and re-download off-network
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rm ~/whisper-models/ggml-large-v3-turbo.bin
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```
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---
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## Ollama Issues
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### `MLX dynamic library not available`
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```
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WARN MLX dynamic library not available error="failed to load MLX dynamic library"
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```
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**Severity:** Harmless
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**Cause:** Ollama searches for Apple MLX framework but it's not installed
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**Impact:** None — falls back to Metal backend which is fully functional on M4 Pro
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**Fix:** None needed. Ignore the warning.
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### Model Pull Fails (SSL / Proxy)
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```bash
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# Try bypassing proxy for Ollama registry
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NO_PROXY="ollama.com,registry.ollama.ai" ollama pull llama3.1:8b
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```
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### Ollama Not Responding
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||||
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```bash
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# Check if running
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curl http://localhost:11434/api/tags
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# Restart
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brew services restart ollama
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# or
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pkill ollama && ollama serve
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```
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### JSON Parse Errors in Evals
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Model returned markdown-wrapped JSON (` ```json ... ``` `). Fix by adding to your prompt:
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```
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Return ONLY a valid JSON object — no markdown, no backticks, no explanation.
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```
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### Slow Inference
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Check Activity Monitor — Ollama should be using GPU (Metal). If CPU-only:
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```bash
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# Restart with performance flags
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OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama serve
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```
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### Model Won't Unload from RAM
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```bash
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# Force unload via API
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curl http://localhost:11434/api/generate -d '{"model": "MODEL_NAME", "prompt": "", "keep_alive": "0"}'
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# Or restart Ollama entirely
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brew services restart ollama
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```
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||||
### Disk Space Running Low
|
||||
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```bash
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# Check Ollama disk usage
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du -sh ~/.ollama/models/
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# List models with sizes
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ollama list
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# Remove models you don't need
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ollama rm <model-name>
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```
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---
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## Whisper.cpp Issues
|
||||
|
||||
### `command not found: whisper-cpp`
|
||||
|
||||
The binary is named `whisper-cli`, NOT `whisper-cpp`:
|
||||
|
||||
```bash
|
||||
# Wrong
|
||||
whisper-cpp --model ...
|
||||
|
||||
# Correct
|
||||
whisper-cli --model ...
|
||||
```
|
||||
|
||||
Full list of binaries: `ls /opt/homebrew/bin/whisper-*`
|
||||
|
||||
### Audio Format Not Supported
|
||||
|
||||
Whisper.cpp requires WAV format. Convert first:
|
||||
|
||||
```bash
|
||||
# m4a → wav (16kHz mono)
|
||||
ffmpeg -i input.m4a -ar 16000 -ac 1 output.wav
|
||||
|
||||
# mp3 → wav
|
||||
ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav
|
||||
```
|
||||
|
||||
### Model File Is HTML (Proxy-Corrupted)
|
||||
|
||||
```bash
|
||||
file ~/whisper-models/ggml-large-v3-turbo.bin
|
||||
# If output says "HTML document text" — it's corrupted
|
||||
rm ~/whisper-models/ggml-large-v3-turbo.bin
|
||||
# Re-download off corporate network
|
||||
```
|
||||
|
||||
### `ffmpeg: command not found`
|
||||
|
||||
```bash
|
||||
brew install ffmpeg
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Dashboard Issues
|
||||
|
||||
### Port Conflict
|
||||
|
||||
```bash
|
||||
# Default port 3100, change if needed
|
||||
npm run dev -- -p 3101
|
||||
```
|
||||
|
||||
### Lockfile Warning
|
||||
|
||||
```
|
||||
Warning: Next.js inferred your workspace root, but it may not be correct.
|
||||
We detected multiple lockfiles...
|
||||
```
|
||||
|
||||
This is harmless — the dashboard has its own `package-lock.json` inside the pnpm monorepo. Can be silenced by adding `turbopack.root` to `next.config.ts`.
|
||||
|
||||
### API Routes Return Empty Data
|
||||
|
||||
- **Ollama offline:** Start with `ollama serve` or `brew services start ollama`
|
||||
- **Whisper not installed:** Run `brew install whisper-cpp`
|
||||
- **No models:** Check `ollama list` and `ls ~/whisper-models/`
|
||||
|
||||
---
|
||||
|
||||
## General macOS Issues
|
||||
|
||||
### Accessibility / Permissions
|
||||
|
||||
Some tools (e.g., `whisper-stream` for mic access) need explicit macOS permissions:
|
||||
|
||||
System Settings → Privacy & Security → Microphone → enable Terminal / your IDE
|
||||
|
||||
### Node.js TLS Warning
|
||||
|
||||
```
|
||||
Warning: Setting NODE_TLS_REJECT_UNAUTHORIZED to '0' makes TLS connections insecure
|
||||
```
|
||||
|
||||
This is set in the corporate environment to handle Forcepoint proxy. Harmless for local dev.
|
||||
Loading…
Reference in New Issue
Block a user