Local GLM OCR with llama.cpp on AIPC(no API Key)

Image OCR, text recognition, extract text from image, scan document, read image text, invoice OCR, receipt OCR, contract recognition, table extraction, business card OCR, ID recognition, screenshot text extraction, document digitization. Runs locally on Windows using the GLM-OCR model, supports mixed Chinese/English text, prioritizes Intel iGPU inference, no cloud API calls.

Image OCR — Local AI PC (Windows · GLM-OCR · llama.cpp Vulkan)

Model: ggml-org/GLM-OCR-GGUF (Q8_0, HuggingFace / hf-mirror)
Inference: llama-server (llama.cpp Vulkan prebuilt, HTTP API)
SKILL_VERSION: 2.0.0

Directory Structure (auto-created or user-specified)

text

<OCR_DIR>\                        ← auto-selected drive or user-specified (e.g. C:\image-ocr or D:\image-ocr)
├── llama.cpp\                    ← llama-server.exe and related binaries
└── models\
    └── GLM-OCR-GGUF\
        ├── GLM-OCR-Q8_0.gguf        ← main model (~950 MB)
        └── mmproj-GLM-OCR-Q8_0.gguf ← vision projection layer (~484 MB, required)

Python helper scripts (located alongside this SKILL.md):

File	Purpose
_ocr_cfg.py	Shared helper: resolves OCR_DIR from env var or .ocr_dir config file
ocr_setup.py	All-in-one setup: preflight checks, llama.cpp install, model download & verify
ocr_run.py	Run GLM-OCR inference on an image (via llama-server HTTP API)

⚠️ Before You Install — Security & Compliance Disclosure

This skill will perform the following actions on your system. Review carefully before granting autonomous execution.

Action	Source	Risk level
Download and extract llama-server.exe and related binaries	github.com/ggml-org/llama.cpp releases	Medium — executes downloaded binary
Download model files (~1.5 GB total)	HuggingFace (huggingface.co) or ModelScope (modelscope.cn)	Low — large file download
Automatically install Miniforge (if Python not found)	github.com/conda-forge/miniforge	Medium — silently installs Python to %USERPROFILE%\miniforge3
Create directories and write files to disk	Local filesystem only	Low

Credentials: The GLM-OCR model (ggml-org/GLM-OCR-GGUF) is publicly available and does not require authentication. If you use a private or gated HuggingFace model, you may need to set $env:HUGGINGFACE_TOKEN before running the download commands — this skill does not request or store any tokens automatically.

Trust: Verify you trust the following sources before proceeding:

• https://github.com/ggml-org/llama.cpp/releases
• https://huggingface.co/ggml-org/GLM-OCR-GGUF
• https://github.com/conda-forge/miniforge (only if Miniforge auto-install is triggered)

If you prefer to control each step manually rather than granting autonomous execution, run the commands in this SKILL.md one by one yourself.

---

Cached Installation

AI Assistant: After a successful first-time setup, fill in this section with the resolved paths. On every subsequent invocation, read this section first and attempt the Fast Path before running setup.

text

OCR_DIR       =
LLAMA_EXE     =
MODEL_GGUF    =
MMPROJ_GGUF   =
LAST_VERIFIED =

(Leave blank until first install. AI fills these in after INSTALL_DONE=1 is printed by ocr_setup.py.)

---

⚠️ AI Assistant Instructions

0. Fast Path (try this first on every invocation)

Before running any setup steps, check the ## Cached Installation section above.

• If OCR_DIR is filled in → attempt the fast path:

  text

  python ocr_run.py "<image_path>"
  

• Fast path succeeds (stdout contains recognized text) → return the result immediately. No setup steps needed.
• Fast path fails (file not found, server error, model missing, etc.) → fall through to the full setup flow below and then retry inference.

---

Full Setup Flow (run only when Fast Path fails or on first use)

1. Execute one command at a time; wait for output before proceeding.
2. Stop immediately on error; refer to the Troubleshooting table at the end.
3. Wrap all paths in double quotes.
4. <OCR_DIR> is the absolute working directory path, determined after Pre-flight.
5. Single goal: Recognize image content and return text results.

Execution flow (do not skip steps):

text

Pre-flight: python ocr_setup.py --check     → LLAMA_STATUS + MODEL_STATUS
Step 1:     python ocr_setup.py --llama     → only if LLAMA_STATUS=MISSING/OUTDATED
            (Check Python first — see Step 1 below)
Step 2:     python ocr_setup.py --download  → only if MODEL_STATUS=MISSING
Step 3:     python ocr_run.py <image>       → inference → return result
Post-setup: Update "Cached Installation" section above with resolved paths

Progress reporting: Announce each step before starting, e.g.: 🔍 Pre-flight: Checking environment…

After successful setup: Update the ## Cached Installation section in this SKILL.md with:

• OCR_DIR (from ocr_setup.py output line OCR_DIR=...)
• LLAMA_EXE = <OCR_DIR>\llama.cpp\llama-server.exe
• MODEL_GGUF = <OCR_DIR>\models\GLM-OCR-GGUF\GLM-OCR-Q8_0.gguf
• MMPROJ_GGUF = <OCR_DIR>\models\GLM-OCR-GGUF\mmproj-GLM-OCR-Q8_0.gguf
• LAST_VERIFIED = today's date

---

⚠️ Using Bash.exe on Windows (Git Bash / WSL)

If the AI assistant's terminal is bash.exe rather than PowerShell or CMD, environment variables set via $env:OCR_DIR (PowerShell) or set OCR_DIR= (CMD) will not be visible to Python scripts launched from bash. This was the most common cause of "cannot find llama.cpp / models" errors.

How it's solved now

All scripts use _ocr_cfg.py which resolves OCR_DIR via two methods (in order):

1. Environment variable OCR_DIR — works if properly export-ed in bash
2. .ocr_dir config file — written automatically by ocr_setup.py --workdir alongside the scripts

As long as ocr_setup.py has been run once (from any shell), all subsequent scripts will find the OCR directory automatically, regardless of which shell is used.

Shell compatibility summary

Shell	Needs export OCR_DIR=...?	Works after first run of ocr_setup.py?
PowerShell / CMD	No (auto-resolved)	✅ Yes
Git Bash	No (reads .ocr_dir file)	✅ Yes
WSL bash	No (reads .ocr_dir file)	✅ Yes

Note: If running from WSL, subprocess.Popen does not support creationflags. ocr_run.py handles this automatically (_CREATE_NO_WINDOW is 0 when sys.platform != "win32").

---

Pre-flight: Check Environment

🔍 Pre-flight: Checking working directory, llama.cpp, and model files…

text

python ocr_setup.py --check

Output	Action
Both READY	✅ Skip to Step 3
LLAMA_STATUS=MISSING/OUTDATED	⬇️ Execute Step 1
MODEL_STATUS=MISSING	⬇️ Execute Step 2

Announce: ✅ Environment check complete. Execute steps as needed.

---

Step 1: Check Python + Install / Update llama.cpp Vulkan

🐍 Step 1a: Checking Python availability…

Check Python

Note: This step requires an existing Python to run scripts. Use the snippet below to locate Python, record the path as PYTHON_EXE, and use that Python for all subsequent steps.

Option A — PowerShell (Windows default)

powershell

$customPythonExe = ""  # Optional: specify a path manually, e.g. "C:\Python311\python.exe"
$found = $null
if ($customPythonExe -and (Test-Path $customPythonExe)) { $found = $customPythonExe }
if (-not $found) {
    foreach ($cmd in @("python","python3","py")) {
        if (Get-Command $cmd -ErrorAction SilentlyContinue) { $found = (Get-Command $cmd).Source; break }
    }
}
if (-not $found) {
    foreach ($p in @(
        "$env:USERPROFILE\miniforge3\python.exe","$env:USERPROFILE\miniconda3\python.exe",
        "$env:USERPROFILE\anaconda3\python.exe")) {
        if (Test-Path $p) { $found = $p; break }
    }
}
if ($found) { $env:PYTHON_EXE = $found; Write-Host "PYTHON_STATUS=OK"; Write-Host "PYTHON_EXE=$found" }
else { Write-Host "PYTHON_STATUS=MISSING" }

Option B — Bash / Git Bash / WSL (fallback if PowerShell is unavailable)

bash

CUSTOM_PYTHON_EXE=""  # Optional: e.g. "/c/Python311/python.exe"
found=""
if [ -n "$CUSTOM_PYTHON_EXE" ] && [ -x "$CUSTOM_PYTHON_EXE" ]; then found="$CUSTOM_PYTHON_EXE"; fi
if [ -z "$found" ]; then
    for cmd in python python3 py; do
        if command -v "$cmd" >/dev/null 2>&1; then found=$(command -v "$cmd"); break; fi
    done
fi
if [ -z "$found" ]; then
    for p in "$HOME/miniforge3/bin/python" "$HOME/miniconda3/bin/python" "$HOME/anaconda3/bin/python"; do
        if [ -x "$p" ]; then found="$p"; break; fi
    done
fi
if [ -n "$found" ]; then
    export PYTHON_EXE="$found"
    echo "PYTHON_STATUS=OK"
    echo "PYTHON_EXE=$found"
else
    echo "PYTHON_STATUS=MISSING"
fi

If Python is not found (PYTHON_STATUS=MISSING), install Miniforge:

Consent required: Miniforge will be silently installed to %USERPROFILE%\miniforge3. This installs a Python runtime and conda/pip toolchain. No admin rights are needed. Source: github.com/conda-forge/miniforge. Confirm with the user before proceeding.

Option A — PowerShell

powershell

$mf = "$env:TEMP\Miniforge3-Windows-x86_64.exe"
Invoke-WebRequest -Uri "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Windows-x86_64.exe" -OutFile $mf
Start-Process $mf -ArgumentList "/S /D=$env:USERPROFILE\miniforge3" -Wait
Remove-Item $mf
$env:PYTHON_EXE = "$env:USERPROFILE\miniforge3\python.exe"
& $env:PYTHON_EXE --version
Write-Host "PYTHON_STATUS=OK"

Option B — Bash / Git Bash / WSL

bash

MF_URL="https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh"
MF_INSTALLER="/tmp/Miniforge3-install.sh"
curl -fsSL "$MF_URL" -o "$MF_INSTALLER"
bash "$MF_INSTALLER" -b -p "$HOME/miniforge3"
rm -f "$MF_INSTALLER"
export PYTHON_EXE="$HOME/miniforge3/bin/python"
"$PYTHON_EXE" --version
echo "PYTHON_STATUS=OK"

Output	Action
PYTHON_STATUS=OK	✅ Continue to Step 1b
PYTHON_STATUS=MISSING → Miniforge installed	✅ Continue to Step 1b
Miniforge download error	⛔ Check network, or install Python manually

Announce: ✅ Python ready. Checking llama.cpp…

---

⬇️ Step 1b: Installing / updating llama.cpp Vulkan… (only when LLAMA_STATUS=MISSING/OUTDATED)

Consent required: Before proceeding, inform the user:

• A ZIP (~50–100 MB) will be downloaded from github.com/ggml-org/llama.cpp/releases
• It will be extracted to <OCR_DIR>\llama.cpp\ and the original ZIP will be deleted
• llama-server.exe will be placed on disk and launched as a local HTTP server by ocr_run.py

Ask the user to confirm before running the download command.

Edit the LLAMA_TAG variable at the top of ocr_setup.py if a newer release is available.

text

python ocr_setup.py --llama

To install a specific tag:

text

python ocr_setup.py --llama --tag b9000

Output	Action
LLAMA_INSTALL=DONE	✅ Continue to Step 2
Download error	⛔ Check network, or manually download from browser and extract to <OCR_DIR>\llama.cpp\

Announce: ✅ llama.cpp installed. Continue to Step 2 to download models.

---

Step 2: Download GLM-OCR Models

📦 Step 2: Downloading GLM-OCR models… (only when MODEL_STATUS=MISSING)

First-time Download Notice (required reading when MODEL_STATUS=MISSING)

Announce the following to the user, then ask whether to proceed:

text

📥 First-time model download is approximately 1.5 GB
   (GLM-OCR-Q8_0.gguf ~950 MB + mmproj ~484 MB).
   Estimated download time:
   • 100 Mbps connection: ~2 minutes
   •  50 Mbps connection: ~4 minutes
   •  10 Mbps connection: ~20 minutes

   Downloads support resumption — if interrupted, re-running this step
   will automatically continue from where it left off.

   ✅ Ready — start automatic download
   📂 I prefer to download manually — skip automatic download

• User chooses automatic download → continue with download commands below
• User chooses manual download → jump to the "Manual Download Fallback" section

---

Check Disk Space

text

python ocr_setup.py --disk

Output	Action
DISK_STATUS=OK	✅ Continue to Download Models
DISK_STATUS=LOW	⚠️ Ask user to free space before continuing

Download Models

Option A: HuggingFace (recommended)

To use the HF mirror (China), uncomment the HF_ENDPOINT line inside ocr_setup.py.

text

python ocr_setup.py --download hf

Option B: ModelScope (alternative for users in China)

text

python ocr_setup.py --download ms

Verify:

text

python ocr_setup.py --verify

Output	Action
MODEL_DOWNLOAD=DONE	✅ Continue to Step 3
Timeout / repeated failure	⚠️ Direct user to "Manual Download Fallback", or switch Option A / B and retry

Announce: ✅ Model download complete.

---

Manual Download Fallback

If automatic download repeatedly fails, guide the user to download manually:

text

⚠️ Automatic download failed. Please manually download the following two files:

1. GLM-OCR-Q8_0.gguf (~950 MB)
   HuggingFace: https://huggingface.co/ggml-org/GLM-OCR-GGUF/resolve/main/GLM-OCR-Q8_0.gguf
   HF Mirror:   https://hf-mirror.com/ggml-org/GLM-OCR-GGUF/resolve/main/GLM-OCR-Q8_0.gguf
   ModelScope:  https://modelscope.cn/models/ggml-org/GLM-OCR-GGUF/resolve/master/GLM-OCR-Q8_0.gguf

2. mmproj-GLM-OCR-Q8_0.gguf (~484 MB)
   HuggingFace: https://huggingface.co/ggml-org/GLM-OCR-GGUF/resolve/main/mmproj-GLM-OCR-Q8_0.gguf
   HF Mirror:   https://hf-mirror.com/ggml-org/GLM-OCR-GGUF/resolve/main/mmproj-GLM-OCR-Q8_0.gguf
   ModelScope:  https://modelscope.cn/models/ggml-org/GLM-OCR-GGUF/resolve/master/mmproj-GLM-OCR-Q8_0.gguf

Once downloaded, place both files into:
   <OCR_DIR>\models\GLM-OCR-GGUF\

Then run: python ocr_setup.py --verify

---

Step 3: Run Inference

🔍 Step 3: Running GLM-OCR recognition…

Determine Input Source

Situation	Action
User message contains a local file path (e.g. C:\Users\...\xxx.png)	⬇️ Case A
User uploaded an image via the interface; AI tool provides a temp path	⬇️ Case B
Neither	⛔ Ask user to provide a local file path or upload an image

Case A: User Provides a Local File Path

text

python ocr_run.py "<file path extracted from user message>"

Case B: User Uploaded an Image via the Interface

text

python ocr_run.py "<temporary image path provided by the AI tool>"

Success criteria: stdout contains the recognized text content.

---

Format Output

Once the recognized text is obtained, process it according to the user's intent:

Scenario	Handling
General text extraction	Output the recognized text as-is, preserving original layout
Invoice / receipt	Extract structured fields from the text; output as JSON + human-readable format
Table	Reformat the recognized text as a Markdown table
Business card	Extract name, title, company, phone, email, address; output as JSON
ID / certificate	Output structured by original layout
Screenshot / document	Organize output by paragraph
User-defined	Process according to the user's stated requirements

Completion announcement:

text

✅ Recognition complete!
Let me know if you'd like to re-process, change the output format, or export to a file.

Situation	Handling
ERROR: File not found	File path does not exist — ask user to verify the path
Empty / garbled output	Low image quality — ask user to retake or rescan
Blurry / low-resolution image	Ask user to retake or zoom in before retrying
No text detected	Inform user that no recognizable text was found in the image

---

Troubleshooting

Error	Cause	Solution
PYTHON_STATUS=MISSING	Python not installed	Step 1 auto-installs Miniforge; confirm consent and retry
llama-server not found	llama-server.exe path not set correctly	Run python ocr_setup.py --check to verify path
Server failed to start	Wrong binary version, Vulkan error, or port conflict	Run python ocr_setup.py --llama to reinstall latest llama.cpp; check Vulkan drivers
ggml_vulkan: no devices found	Vulkan driver not installed	Update GPU driver
error: unable to open model	Incorrect model path	Run python ocr_setup.py --check to verify path
MODEL_DOWNLOAD= no output	Download interrupted	Switch between --download hf / --download ms, or configure proxy
Garbled / blank output	Low image quality	Improve image quality
VRAM insufficient / crash	Not enough GPU memory	Lower -ngl value, or use --device none
Fast path fails unexpectedly	Stale cached paths in SKILL.md	Clear the ## Cached Installation block, re-run full setup flow

---

References

• llama.cpp Releases: https://github.com/ggml-org/llama.cpp/releases
• GLM-OCR GGUF: https://huggingface.co/ggml-org/GLM-OCR-GGUF