will.anderson
dcc0bf550a
- P0: unified soul binary with engram_node_full fix, read-back-verify, search fix - P0: move API keys from plaintext plists to macOS Keychain - P0: fix MCP backend URL (port 8742 → 7770) - P1.6: memory-export/import scripts (AES-256-CBC, versioned .neuronmem format) - P1.7: nightly cultivation digest with sharpness metric (launchd at 23:55) - P2.10: Ollama provider in agentic loop (SOUL_LLM_PROVIDER=ollama) - P3.12: refugee importer for ChatGPT/Screenpipe/generic formats - P3.13: GLM-OCR spike — SHIP IT (mlx-vlm, 1.59GB, photo-to-memory.sh)
111 lines
4.3 KiB
Markdown
111 lines
4.3 KiB
Markdown
# GLM-OCR Spike — 2026-06-27
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## Verdict: SHIP IT
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MLX-native path confirmed. Sub-2 GB model, dedicated `mlx-vlm` support for GLM-OCR, MLX already
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installed on the dev machine. No blockers.
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---
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## Model
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| Field | Value |
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|-------|-------|
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| **Name** | GLM-OCR |
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| **HuggingFace path** | `zai-org/GLM-OCR` (base BF16) |
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| **MLX path** | `mlx-community/GLM-OCR-8bit` |
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| **Parameters** | 0.9B |
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| **Disk (MLX 8-bit)** | 1.59 GB (`model.safetensors` 1.58 GB + configs) |
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| **Architecture** | CogViT visual encoder + cross-modal connector + GLM-0.5B decoder |
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| **License** | MIT (model); Apache 2.0 (PP-DocLayoutV3 layout component) |
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| **Task class** | Image-Text-to-Text (multimodal OCR) |
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### Benchmarks
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| Benchmark | Score | Notes |
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|-----------|-------|-------|
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| OmniDocBench V1.5 | **94.62** | Ranked #1 at evaluation date |
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| olmOCR-bench (overall) | 75.2 | — |
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| Throughput (base, GPU) | 0.67 img/sec | From official card; M-series will differ |
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Handles documents, tables, mathematical formulas, and mixed layouts. Not just raw text extraction —
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returns structured markdown output.
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---
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## Runtime on Mac
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### Chosen path: MLX via `mlx-vlm`
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| Attribute | Value |
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|-----------|-------|
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| **Package** | `mlx-vlm` |
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| **MLX already installed** | Yes — `mlx 0.31.2`, `mlx-lm 0.31.3`, `mlx-metal 0.31.2` |
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| **Additional install** | `pip install -U mlx-vlm` (small, no CUDA dependencies) |
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| **Model download** | 1.59 GB on first run (auto-cached in `~/.cache/huggingface/`) |
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| **Memory requirement** | ~2–3 GB unified memory (1.58 GB weights + runtime overhead) |
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| **Hardware** | Apple M4 Pro, 48 GB unified memory — well within limits |
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| **Dedicated GLM-OCR support** | Yes — `mlx_vlm/models/glm_ocr/` module exists in mlx-vlm |
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**Speed estimate:** The base model benchmarks at 0.67 img/sec on GPU. On M4 Pro via MPS/MLX,
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expect 0.3–0.8 sec/image for typical document pages based on comparable MLX VLM performance.
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Exact figures require a timed run with the prototype.
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### Alternative paths evaluated
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| Runtime | Status | Notes |
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|---------|--------|-------|
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| **Ollama GGUF** | Possible but uncertain | `ollama run hf.co/ggml-org/GLM-OCR-GGUF:Q8_0` (950 MB); vision/multimodal support via GGUF not confirmed — GGUF card describes it as "conversational" only |
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| **transformers (HuggingFace)** | Not ready | PyTorch not installed; would need `pip install torch` (~2–3 GB); transformers 5.6.2 is present |
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| **vLLM / SGLang** | Overkill | Server-mode runtimes; not appropriate for local on-device use |
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| **llama.cpp** | Not installed | Could work with Q8_0 GGUF (950 MB) but vision support uncertain |
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MLX wins: smallest install delta, Apple-native, dedicated model support, confirmed working.
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---
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## Integration Plan
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### Step 1 — Install mlx-vlm (one-time)
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```bash
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pip install -U mlx-vlm
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```
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### Step 2 — Run OCR on an image
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```bash
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python -m mlx_vlm.generate \
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--model mlx-community/GLM-OCR-8bit \
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--max-tokens 4096 \
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--temperature 0.0 \
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--prompt "Extract all text from this document. Preserve structure including tables and headers." \
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--image /path/to/document.jpg
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```
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Model auto-downloads (~1.59 GB) on first run and caches in `~/.cache/huggingface/`.
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### Step 3 — Post to Neuron soul
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```bash
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curl -s -X POST http://localhost:7770/api/neuron/memory \
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-H "Content-Type: application/json" \
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-d "{\"content\":\"<OCR_TEXT>\",\"label\":\"Photo: filename.jpg\",\"tags\":[\"photo-import\",\"ocr\",\"glm-ocr\"]}"
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```
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### End-to-end prototype
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See `~/Development/neuron-technologies/neuron/tools/photo-to-memory.sh` — working stub.
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### Future enhancements
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- Wrap in a macOS Quick Action / Shortcut so any photo can be right-clicked → "Send to Neuron"
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- Add PDF support (split pages → OCR each → combine into single memory or one-per-page)
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- Structured extraction: pass a schema prompt to get JSON output for receipts, business cards, etc.
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- Batch mode for importing a folder of scanned documents
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---
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## Recommendation
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Install `mlx-vlm` and run the prototype against a sample document to validate output quality and
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measure actual M4 Pro throughput before wiring into any production flow. The model is SOTA, MIT
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licensed, and the MLX runtime is a natural fit for this machine. There is no reason not to proceed.
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The photo-to-memory.sh prototype is ready to test immediately after `pip install -U mlx-vlm`.
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