Image-Text-to-Text
MLX
Safetensors
multilingual
qwen3_5
ocr
document-parsing
markdown
tables
formulas
multimodal
vision-language
qwen3.5
apple-silicon
oq
oqe6
quantized
conversational
6-bit
Instructions to use yugeshkarunamurthy/OvisOCR2-oQ6e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use yugeshkarunamurthy/OvisOCR2-oQ6e with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("yugeshkarunamurthy/OvisOCR2-oQ6e") config = load_config("yugeshkarunamurthy/OvisOCR2-oQ6e") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use yugeshkarunamurthy/OvisOCR2-oQ6e with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "yugeshkarunamurthy/OvisOCR2-oQ6e"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "yugeshkarunamurthy/OvisOCR2-oQ6e" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use yugeshkarunamurthy/OvisOCR2-oQ6e with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "yugeshkarunamurthy/OvisOCR2-oQ6e"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default yugeshkarunamurthy/OvisOCR2-oQ6e
Run Hermes
hermes
- OpenClaw new
How to use yugeshkarunamurthy/OvisOCR2-oQ6e with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "yugeshkarunamurthy/OvisOCR2-oQ6e"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "yugeshkarunamurthy/OvisOCR2-oQ6e" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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| "cache_reused": false, | |
| "entry_count": 186, | |
| "calib_dataset": "oqe_code_multilingual", | |
| "collection": { | |
| "dataset": "oqe_code_multilingual", | |
| "requested_samples": 128, | |
| "seq_length": 512, | |
| "adaptive": true, | |
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| "adaptive_max_samples": 1024, | |
| "available_samples": 1024, | |
| "micro_batch_size": 32, | |
| "micro_batches": 4, | |
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| "live_available_bytes": 8290926592, | |
| "hidden_size": 1024, | |
| "num_experts": 0, | |
| "top_k": 1 | |
| }, | |
| "processed_samples": 128, | |
| "installed_modules": 236, | |
| "capture_module_classes": { | |
| "Linear": 236 | |
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